what is data gathering in qualitative research

8 Essential Qualitative Data Collection Methods

Qualitative data methods allow you to deep dive into the mindset of your audience to discover areas for growth, development, and improvement. 

British mathematician and marketing mastermind Clive Humby once famously stated that “Data is the new oil.”  He has a point. Without data, nonprofit organizations are left second-guessing what their clients and supporters think, how their brand compares to others in the market, whether their messaging is on-point, how their campaigns are performing, where improvements can be made, and how overall results can be optimized. 

There are two primary data collection methodologies: qualitative data collection and quantitative data collection. At UpMetrics, we believe that relying on quantitative, static data is no longer an option to drive effective impact. In the nonprofit sector, where financial gain is not the sole purpose of your organization’s existence. In this guide, we’ll focus on qualitative data collection methods and how they can help you gather, analyze, and collate information that can help drive your organization forward. 

What is Qualitative Data? 

Data collection in qualitative research focuses on gathering contextual information. Unlike quantitative data, which focuses primarily on numbers to establish ‘how many’ or ‘how much,’ qualitative data collection tools allow you to assess the ‘why’s’ and ‘how’s’ behind those statistics. This is vital for nonprofits as it enables organizations to determine:

• Existing knowledge surrounding a particular issue.
• How social norms and cultural practices impact a cause.
• What kind of experiences and interactions people have with your brand.
• Trends in the way people change their opinions.
• Whether meaningful relationships are being established between all parties.

In short, qualitative data collection methods collect perceptual and descriptive information that helps you understand the reasoning and motivation behind particular reactions and behaviors. For that reason, qualitative data methods are usually non-numerical and center around spoken and written words rather than data extrapolated from a spreadsheet or report. 

Qualitative vs. Quantitative Data 

Quantitative and qualitative data represent both sides of the same coin. There will always be some degree of debate over the importance of quantitative vs. qualitative research, data, and collection. However, successful organizations should strive to achieve a balance between the two. 

Organizations can track their performance by collecting quantitative data based on metrics including dollars raised, membership growth, number of people served, overhead costs, etc. This is all essential information to have. However, the data lacks value without the additional details provided by qualitative research because it doesn’t tell you anything about how your target audience thinks, feels, and acts. 

Qualitative data collection is particularly relevant in the nonprofit sector as the relationships people have with the causes they support are fundamentally personal and cannot be expressed numerically. Qualitative data methods allow you to deep dive into the mindset of your audience to discover areas for growth, development, and improvement. 

8 Types of Qualitative Data Collection Methods  

As we have firmly established the need for qualitative data, it’s time to answer the next big question: how to collect qualitative data. 

Here is a list of the most common qualitative data collection methods. You don’t need to use them all in your quest for gathering information. However, a foundational understanding of each will help you refine your research strategy and select the methods that are likely to provide the highest quality business intelligence for your organization. 

1. Interviews

One-on-one interviews are one of the most commonly used data collection methods in qualitative research because they allow you to collect highly personalized information directly from the source. Interviews explore participants' opinions, motivations, beliefs, and experiences and are particularly beneficial in gathering data on sensitive topics because respondents are more likely to open up in a one-on-one setting than in a group environment. 

Interviews can be conducted in person or by online video call. Typically, they are separated into three main categories:

• Structured Interviews - Structured interviews consist of predetermined (and usually closed) questions with little or no variation between interviewees. There is generally no scope for elaboration or follow-up questions, making them better suited to researching specific topics. 
• Unstructured Interviews – Conversely, unstructured interviews have little to no organization or preconceived topics and include predominantly open questions. As a result, the discussion will flow in completely different directions for each participant and can be very time-consuming. For this reason, unstructured interviews are generally only used when little is known about the subject area or when in-depth responses are required on a particular subject.
• Semi-Structured Interviews – A combination of the two interviews mentioned above, semi-structured interviews comprise several scripted questions but allow both interviewers and interviewees the opportunity to diverge and elaborate so more in-depth reasoning can be explored. 

While each approach has its merits, semi-structured interviews are typically favored as a way to uncover detailed information in a timely manner while highlighting areas that may not have been considered relevant in previous research efforts. Whichever type of interview you utilize, participants must be fully briefed on the format, purpose, and what you hope to achieve. With that in mind, here are a few tips to follow: 

• Give them an idea of how long the interview will last
• If you plan to record the conversation, ask permission beforehand
• Provide the opportunity to ask questions before you begin and again at the end. 

2. Focus Groups

Focus groups share much in common with less structured interviews, the key difference being that the goal is to collect data from several participants simultaneously. Focus groups are effective in gathering information based on collective views and are one of the most popular data collection instruments in qualitative research when a series of one-on-one interviews proves too time-consuming or difficult to schedule. 

Focus groups are most helpful in gathering data from a specific group of people, such as donors or clients from a particular demographic. The discussion should be focused on a specific topic and carefully guided and moderated by the researcher to determine participant views and the reasoning behind them. 

Feedback in a group setting often provides richer data than one-on-one interviews, as participants are generally more open to sharing when others are sharing too. Plus, input from one participant may spark insight from another that would not have come to light otherwise. However, here are a couple of potential downsides:

• If participants are uneasy with each other, they may not be at ease openly discussing their feelings or opinions.
• If the topic is not of interest or does not focus on something participants are willing to discuss, data will lack value. 

The size of the group should be carefully considered. Research suggests over-recruiting to avoid risking cancellation, even if that means moderators have to manage more participants than anticipated. The optimum group size is generally between six and eight for all participants to be granted ample opportunity to speak. However, focus groups can still be successful with as few as three or as many as fourteen participants. 

3. Observation

Observation is one of the ultimate data collection tools in qualitative research for gathering information through subjective methods. A technique used frequently by modern-day marketers, qualitative observation is also favored by psychologists, sociologists, behavior specialists, and product developers. 

The primary purpose is to gather information that cannot be measured or easily quantified. It involves virtually no cognitive input from the participants themselves. Researchers simply observe subjects and their reactions during the course of their regular routines and take detailed field notes from which to draw information. 

Observational techniques vary in terms of contact with participants. Some qualitative observations involve the complete immersion of the researcher over a period of time. For example, attending the same church, clinic, society meetings, or volunteer organizations as the participants. Under these circumstances, researchers will likely witness the most natural responses rather than relying on behaviors elicited in a simulated environment. Depending on the study and intended purpose, they may or may not choose to identify themselves as a researcher during the process. 

Regardless of whether you take a covert or overt approach, remember that because each researcher is as unique as every participant, they will have their own inherent biases. Therefore, observational studies are prone to a high degree of subjectivity. For example, one researcher’s notes on the behavior of donors at a society event may vary wildly from the next. So, each qualitative observational study is unique in its own right. 

4. Open-Ended Surveys and Questionnaires

Open-ended surveys and questionnaires allow organizations to collect views and opinions from respondents without meeting in person. They can be sent electronically and are considered one of the most cost-effective qualitative data collection tools. Unlike closed question surveys and questionnaires that limit responses, open-ended questions allow participants to provide lengthy and in-depth answers from which you can extrapolate large amounts of data. 

The findings of open-ended surveys and questionnaires can be challenging to analyze because there are no uniform answers. A popular approach is to record sentiments as positive, negative, and neutral and further dissect the data from there. To gather the best business intelligence, carefully consider the presentation and length of your survey or questionnaire. Here is a list of essential considerations:

• Number of questions : Too many can feel intimidating, and you’ll experience low response rates. Too few can feel like it’s not worth the effort. Plus, the data you collect will have limited actionability. The consensus on how many questions to include varies depending on which sources you consult. However, 5-10 is a good benchmark for shorter surveys that take around 10 minutes and 15-20 for longer surveys that take approximately 20 minutes to complete. 
• Personalization: Your response rate will be higher if you greet patients by name and demonstrate a historical knowledge of their interactions with your brand. 
• Visual elements : Recipients can be easily turned off by poorly designed questionnaires. Besides, it’s a good idea to customize your survey template to include brand assets like colors, logos, and fonts to increase brand loyalty and recognition.
• Reminders : Sending survey reminders is the best way to improve your response rate. You don’t want to hassle respondents too soon, nor do you want to wait too long. Sending a follow-up at around the 3-7 mark is usually the most effective. 
• Building a feedback loop : Adding a tick-box requesting permission for further follow-ups is a proven way to elicit more in-depth feedback. Plus, it gives respondents a voice and makes their opinion feel valued.

5. Case Studies

Case studies are often a preferred method of qualitative research data collection for organizations looking to generate incredibly detailed and in-depth information on a specific topic. Case studies are usually a deep dive into one specific case or a small number of related cases. As a result, they work well for organizations that operate in niche markets.

Case studies typically involve several qualitative data collection methods, including interviews, focus groups, surveys, and observation. The idea is to cast a wide net to obtain a rich picture comprising multiple views and responses. When conducted correctly, case studies can generate vast bodies of data that can be used to improve processes at every client and donor touchpoint. 

The best way to demonstrate the purpose and value of a case study is with an example: A Longitudinal Qualitative Case Study of Change in Nonprofits – Suggesting A New Approach to the Management of Change . 

The researchers established that while change management had already been widely researched in commercial and for-profit settings, little reference had been made to the unique challenges in the nonprofit sector. The case study examined change and change management at a single nonprofit hospital from the viewpoint of all those who witnessed and experienced it. To gain a holistic view of the entire process, research included interviews with employees at every level, from nursing staff to CEOs, to identify the direct and indirect impacts of change. Results were collated based on detailed responses to questions about preparing for change, experiencing change, and reflecting on change.

6. Text Analysis

Text analysis has long been used in political and social science spheres to gain a deeper understanding of behaviors and motivations by gathering insights from human-written texts. By analyzing the flow of text and word choices, relationships between other texts written by the same participant can be identified so that researchers can draw conclusions about the mindset of their target audience. Though technically a qualitative data collection method, the process can involve some quantitative elements, as often, computer systems are used to scan, extract, and categorize information to identify patterns, sentiments, and other actionable information. 

You might be wondering how to collect written information from your research subjects. There are many different options, and approaches can be overt or covert. 

Examples include:

• Investigating how often certain cause-related words and phrases are used in client and donor social media posts.
• Asking participants to keep a journal or diary.
• Analyzing existing interview transcripts and survey responses.

By conducting a detailed analysis, you can connect elements of written text to specific issues, causes, and cultural perspectives, allowing you to draw empirical conclusions about personal views, behaviors, and social relations. With small studies focusing on participants' subjective experience on a specific theme or topic, diaries and journals can be particularly effective in building an understanding of underlying thought processes and beliefs. 

7. Audio and Video Recordings

Similarly to how data is collected from a person’s writing, you can draw valuable conclusions by observing someone’s speech patterns, intonation, and body language when you watch or listen to them interact in a particular environment or within specific surroundings. 

Video and audio recordings are helpful in circumstances where researchers predict better results by having participants be in the moment rather than having them think about what to write down or how to formulate an answer to an email survey. 

You can collect audio and video materials for analysis from multiple sources, including:

• Previously filmed records of events
• Interview recordings
• Video diaries

Utilizing audio and video footage allows researchers to revisit key themes, and it's possible to use the same analytical sources in multiple studies – providing that the scope of the original recording is comprehensive enough to cover the intended theme in adequate depth. 

It can be challenging to present the results of audio and video analysis in a quantifiable form that helps you gauge campaign and market performance. However, results can be used to effectively design concept maps that extrapolate central themes that arise consistently. Concept Mapping offers organizations a visual representation of thought patterns and how ideas link together between different demographics. This data can prove invaluable in identifying areas for improvement and change across entire projects and organizational processes. 

8. Hybrid Methodologies

It is often possible to utilize data collection methods in qualitative research that provide quantitative facts and figures. So if you’re struggling to settle on an approach, a hybrid methodology may be a good starting point. For instance, a survey format that asks closed and open questions can collect and collate quantitative and qualitative data. 

A Net Promoter Score (NPS) survey is a great example. The primary goal of an NPS survey is to collect quantitative ratings of various factors on a score of 1-10. However, they also utilize open-ended follow-up questions to collect qualitative data that helps identify insights into the trends, thought processes, reasoning, and behaviors behind the initial scoring. 

Collect and Collate Actionable Data with UpMetrics

Most nonprofits believe data is strategically important. It has been statistically proven that organizations with advanced data insights achieve their missions more efficiently. Yet, studies show that despite 90% of organizations collecting data, only 5% believe internal decision-making is data-driven. At UpMetrics, we’re here to help you change that. 

UpMetrics specializes in bringing technology and humanity together to serve social good. Our unique  social impact software  combines quantitative and qualitative data collection methods and analysis techniques, enabling social impact organizations to gain insights, drive action, and inspire change. By reporting and analyzing quantitative and qualitative data in one intuitive platform, your impact organization gains the understanding it needs to identify the drivers of positive outcomes, achieve transparency, and increase knowledge sharing across stakeholders.

Contact us today  to learn more about our  nonprofit impact measurement  solutions and discover the power of a partnership with UpMetrics. 

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Qualitative Data Analysis: Step-by-Step Guide (Manual vs. Automatic)

When we conduct qualitative methods of research, need to explain changes in metrics or understand people's opinions, we always turn to qualitative data. Qualitative data is typically generated through:

• Interview transcripts
• Surveys with open-ended questions
• Contact center transcripts
• Texts and documents
• Audio and video recordings
• Observational notes

Compared to quantitative data, which captures structured information, qualitative data is unstructured and has more depth. It can answer our questions, can help formulate hypotheses and build understanding.

It's important to understand the differences between quantitative data & qualitative data . But unfortunately, analyzing qualitative data is difficult. While tools like Excel, Tableau and PowerBI crunch and visualize quantitative data with ease, there are a limited number of mainstream tools for analyzing qualitative data . The majority of qualitative data analysis still happens manually.

That said, there are two new trends that are changing this. First, there are advances in natural language processing (NLP) which is focused on understanding human language. Second, there is an explosion of user-friendly software designed for both researchers and businesses. Both help automate the qualitative data analysis process.

In this post we want to teach you how to conduct a successful qualitative data analysis. There are two primary qualitative data analysis methods; manual & automatic. We will teach you how to conduct the analysis manually, and also, automatically using software solutions powered by NLP. We’ll guide you through the steps to conduct a manual analysis, and look at what is involved and the role technology can play in automating this process.

More businesses are switching to fully-automated analysis of qualitative customer data because it is cheaper, faster, and just as accurate. Primarily, businesses purchase subscriptions to feedback analytics platforms so that they can understand customer pain points and sentiment.

We’ll take you through 5 steps to conduct a successful qualitative data analysis. Within each step we will highlight the key difference between the manual, and automated approach of qualitative researchers. Here's an overview of the steps:

The 5 steps to doing qualitative data analysis

• Gathering and collecting your qualitative data
• Organizing and connecting into your qualitative data
• Coding your qualitative data
• Analyzing the qualitative data for insights
• Reporting on the insights derived from your analysis

What is Qualitative Data Analysis?

Qualitative data analysis is a process of gathering, structuring and interpreting qualitative data to understand what it represents.

Qualitative data is non-numerical and unstructured. Qualitative data generally refers to text, such as open-ended responses to survey questions or user interviews, but also includes audio, photos and video.

Businesses often perform qualitative data analysis on customer feedback. And within this context, qualitative data generally refers to verbatim text data collected from sources such as reviews, complaints, chat messages, support centre interactions, customer interviews, case notes or social media comments.

How is qualitative data analysis different from quantitative data analysis?

Understanding the differences between quantitative & qualitative data is important. When it comes to analyzing data, Qualitative Data Analysis serves a very different role to Quantitative Data Analysis. But what sets them apart?

Qualitative Data Analysis dives into the stories hidden in non-numerical data such as interviews, open-ended survey answers, or notes from observations. It uncovers the ‘whys’ and ‘hows’ giving a deep understanding of people’s experiences and emotions.

Quantitative Data Analysis on the other hand deals with numerical data, using statistics to measure differences, identify preferred options, and pinpoint root causes of issues.  It steps back to address questions like "how many" or "what percentage" to offer broad insights we can apply to larger groups.

In short, Qualitative Data Analysis is like a microscope,  helping us understand specific detail. Quantitative Data Analysis is like the telescope, giving us a broader perspective. Both are important, working together to decode data for different objectives.

Qualitative Data Analysis methods

Once all the data has been captured, there are a variety of analysis techniques available and the choice is determined by your specific research objectives and the kind of data you’ve gathered.  Common qualitative data analysis methods include:

Content Analysis

This is a popular approach to qualitative data analysis. Other qualitative analysis techniques may fit within the broad scope of content analysis. Thematic analysis is a part of the content analysis.  Content analysis is used to identify the patterns that emerge from text, by grouping content into words, concepts, and themes. Content analysis is useful to quantify the relationship between all of the grouped content. The Columbia School of Public Health has a detailed breakdown of content analysis .

Narrative Analysis

Narrative analysis focuses on the stories people tell and the language they use to make sense of them.  It is particularly useful in qualitative research methods where customer stories are used to get a deep understanding of customers’ perspectives on a specific issue. A narrative analysis might enable us to summarize the outcomes of a focused case study.

Discourse Analysis

Discourse analysis is used to get a thorough understanding of the political, cultural and power dynamics that exist in specific situations.  The focus of discourse analysis here is on the way people express themselves in different social contexts. Discourse analysis is commonly used by brand strategists who hope to understand why a group of people feel the way they do about a brand or product.

Thematic Analysis

Thematic analysis is used to deduce the meaning behind the words people use. This is accomplished by discovering repeating themes in text. These meaningful themes reveal key insights into data and can be quantified, particularly when paired with sentiment analysis . Often, the outcome of thematic analysis is a code frame that captures themes in terms of codes, also called categories. So the process of thematic analysis is also referred to as “coding”. A common use-case for thematic analysis in companies is analysis of customer feedback.

Grounded Theory

Grounded theory is a useful approach when little is known about a subject. Grounded theory starts by formulating a theory around a single data case. This means that the theory is “grounded”. Grounded theory analysis is based on actual data, and not entirely speculative. Then additional cases can be examined to see if they are relevant and can add to the original grounded theory.

Challenges of Qualitative Data Analysis

While Qualitative Data Analysis offers rich insights, it comes with its challenges. Each unique QDA method has its unique hurdles. Let’s take a look at the challenges researchers and analysts might face, depending on the chosen method.

• Time and Effort (Narrative Analysis): Narrative analysis, which focuses on personal stories, demands patience. Sifting through lengthy narratives to find meaningful insights can be time-consuming, requires dedicated effort.
• Being Objective (Grounded Theory): Grounded theory, building theories from data, faces the challenges of personal biases. Staying objective while interpreting data is crucial, ensuring conclusions are rooted in the data itself.
• Complexity (Thematic Analysis): Thematic analysis involves identifying themes within data, a process that can be intricate. Categorizing and understanding themes can be complex, especially when each piece of data varies in context and structure. Thematic Analysis software can simplify this process.
• Generalizing Findings (Narrative Analysis): Narrative analysis, dealing with individual stories, makes drawing broad challenging. Extending findings from a single narrative to a broader context requires careful consideration.
• Managing Data (Thematic Analysis): Thematic analysis involves organizing and managing vast amounts of unstructured data, like interview transcripts. Managing this can be a hefty task, requiring effective data management strategies.
• Skill Level (Grounded Theory): Grounded theory demands specific skills to build theories from the ground up. Finding or training analysts with these skills poses a challenge, requiring investment in building expertise.

Benefits of qualitative data analysis

Qualitative Data Analysis (QDA) is like a versatile toolkit, offering a tailored approach to understanding your data. The benefits it offers are as diverse as the methods. Let’s explore why choosing the right method matters.

• Tailored Methods for Specific Needs: QDA isn't one-size-fits-all. Depending on your research objectives and the type of data at hand, different methods offer unique benefits. If you want emotive customer stories, narrative analysis paints a strong picture. When you want to explain a score, thematic analysis reveals insightful patterns
• Flexibility with Thematic Analysis: thematic analysis is like a chameleon in the toolkit of QDA. It adapts well to different types of data and research objectives, making it a top choice for any qualitative analysis.
• Deeper Understanding, Better Products: QDA helps you dive into people's thoughts and feelings. This deep understanding helps you build products and services that truly matches what people want, ensuring satisfied customers
• Finding the Unexpected: Qualitative data often reveals surprises that we miss in quantitative data. QDA offers us new ideas and perspectives, for insights we might otherwise miss.
• Building Effective Strategies: Insights from QDA are like strategic guides. They help businesses in crafting plans that match people’s desires.
• Creating Genuine Connections: Understanding people’s experiences lets businesses connect on a real level. This genuine connection helps build trust and loyalty, priceless for any business.

How to do Qualitative Data Analysis: 5 steps

Now we are going to show how you can do your own qualitative data analysis. We will guide you through this process step by step. As mentioned earlier, you will learn how to do qualitative data analysis manually , and also automatically using modern qualitative data and thematic analysis software.

To get best value from the analysis process and research process, it’s important to be super clear about the nature and scope of the question that’s being researched. This will help you select the research collection channels that are most likely to help you answer your question.

Depending on if you are a business looking to understand customer sentiment, or an academic surveying a school, your approach to qualitative data analysis will be unique.

Once you’re clear, there’s a sequence to follow. And, though there are differences in the manual and automatic approaches, the process steps are mostly the same.

The use case for our step-by-step guide is a company looking to collect data (customer feedback data), and analyze the customer feedback - in order to improve customer experience. By analyzing the customer feedback the company derives insights about their business and their customers. You can follow these same steps regardless of the nature of your research. Let’s get started.

Step 1: Gather your qualitative data and conduct research (Conduct qualitative research)

The first step of qualitative research is to do data collection. Put simply, data collection is gathering all of your data for analysis. A common situation is when qualitative data is spread across various sources.

Classic methods of gathering qualitative data

Most companies use traditional methods for gathering qualitative data: conducting interviews with research participants, running surveys, and running focus groups. This data is typically stored in documents, CRMs, databases and knowledge bases. It’s important to examine which data is available and needs to be included in your research project, based on its scope.

Using your existing qualitative feedback

As it becomes easier for customers to engage across a range of different channels, companies are gathering increasingly large amounts of both solicited and unsolicited qualitative feedback.

Most organizations have now invested in Voice of Customer programs , support ticketing systems, chatbot and support conversations, emails and even customer Slack chats.

These new channels provide companies with new ways of getting feedback, and also allow the collection of unstructured feedback data at scale.

The great thing about this data is that it contains a wealth of valubale insights and that it’s already there! When you have a new question about user behavior or your customers, you don’t need to create a new research study or set up a focus group. You can find most answers in the data you already have.

Typically, this data is stored in third-party solutions or a central database, but there are ways to export it or connect to a feedback analysis solution through integrations or an API.

Utilize untapped qualitative data channels

There are many online qualitative data sources you may not have considered. For example, you can find useful qualitative data in social media channels like Twitter or Facebook. Online forums, review sites, and online communities such as Discourse or Reddit also contain valuable data about your customers, or research questions.

If you are considering performing a qualitative benchmark analysis against competitors - the internet is your best friend. Gathering feedback in competitor reviews on sites like Trustpilot, G2, Capterra, Better Business Bureau or on app stores is a great way to perform a competitor benchmark analysis.

Customer feedback analysis software often has integrations into social media and review sites, or you could use a solution like DataMiner to scrape the reviews.

Step 2: Connect & organize all your qualitative data

Now you all have this qualitative data but there’s a problem, the data is unstructured. Before feedback can be analyzed and assigned any value, it needs to be organized in a single place. Why is this important? Consistency!

If all data is easily accessible in one place and analyzed in a consistent manner, you will have an easier time summarizing and making decisions based on this data.

The manual approach to organizing your data

The classic method of structuring qualitative data is to plot all the raw data you’ve gathered into a spreadsheet.

Typically, research and support teams would share large Excel sheets and different business units would make sense of the qualitative feedback data on their own. Each team collects and organizes the data in a way that best suits them, which means the feedback tends to be kept in separate silos.

An alternative and a more robust solution is to store feedback in a central database, like Snowflake or Amazon Redshift .

Keep in mind that when you organize your data in this way, you are often preparing it to be imported into another software. If you go the route of a database, you would need to use an API to push the feedback into a third-party software.

Computer-assisted qualitative data analysis software (CAQDAS)

Traditionally within the manual analysis approach (but not always), qualitative data is imported into CAQDAS software for coding.

In the early 2000s, CAQDAS software was popularised by developers such as ATLAS.ti, NVivo and MAXQDA and eagerly adopted by researchers to assist with the organizing and coding of data.  

The benefits of using computer-assisted qualitative data analysis software:

• Assists in the organizing of your data
• Opens you up to exploring different interpretations of your data analysis
• Allows you to share your dataset easier and allows group collaboration (allows for secondary analysis)

However you still need to code the data, uncover the themes and do the analysis yourself. Therefore it is still a manual approach.

Organizing your qualitative data in a feedback repository

Another solution to organizing your qualitative data is to upload it into a feedback repository where it can be unified with your other data , and easily searchable and taggable. There are a number of software solutions that act as a central repository for your qualitative research data. Here are a couple solutions that you could investigate:  

• Dovetail: Dovetail is a research repository with a focus on video and audio transcriptions. You can tag your transcriptions within the platform for theme analysis. You can also upload your other qualitative data such as research reports, survey responses, support conversations, and customer interviews. Dovetail acts as a single, searchable repository. And makes it easier to collaborate with other people around your qualitative research.
• EnjoyHQ: EnjoyHQ is another research repository with similar functionality to Dovetail. It boasts a more sophisticated search engine, but it has a higher starting subscription cost.

Organizing your qualitative data in a feedback analytics platform

If you have a lot of qualitative customer or employee feedback, from the likes of customer surveys or employee surveys, you will benefit from a feedback analytics platform. A feedback analytics platform is a software that automates the process of both sentiment analysis and thematic analysis . Companies use the integrations offered by these platforms to directly tap into their qualitative data sources (review sites, social media, survey responses, etc.). The data collected is then organized and analyzed consistently within the platform.

If you have data prepared in a spreadsheet, it can also be imported into feedback analytics platforms.

Once all this rich data has been organized within the feedback analytics platform, it is ready to be coded and themed, within the same platform. Thematic is a feedback analytics platform that offers one of the largest libraries of integrations with qualitative data sources.

Step 3: Coding your qualitative data

Your feedback data is now organized in one place. Either within your spreadsheet, CAQDAS, feedback repository or within your feedback analytics platform. The next step is to code your feedback data so we can extract meaningful insights in the next step.

Coding is the process of labelling and organizing your data in such a way that you can then identify themes in the data, and the relationships between these themes.

To simplify the coding process, you will take small samples of your customer feedback data, come up with a set of codes, or categories capturing themes, and label each piece of feedback, systematically, for patterns and meaning. Then you will take a larger sample of data, revising and refining the codes for greater accuracy and consistency as you go.

If you choose to use a feedback analytics platform, much of this process will be automated and accomplished for you.

The terms to describe different categories of meaning (‘theme’, ‘code’, ‘tag’, ‘category’ etc) can be confusing as they are often used interchangeably.  For clarity, this article will use the term ‘code’.

To code means to identify key words or phrases and assign them to a category of meaning. “I really hate the customer service of this computer software company” would be coded as “poor customer service”.

How to manually code your qualitative data

• Decide whether you will use deductive or inductive coding. Deductive coding is when you create a list of predefined codes, and then assign them to the qualitative data. Inductive coding is the opposite of this, you create codes based on the data itself. Codes arise directly from the data and you label them as you go. You need to weigh up the pros and cons of each coding method and select the most appropriate.
• Read through the feedback data to get a broad sense of what it reveals. Now it’s time to start assigning your first set of codes to statements and sections of text.
• Keep repeating step 2, adding new codes and revising the code description as often as necessary.  Once it has all been coded, go through everything again, to be sure there are no inconsistencies and that nothing has been overlooked.
• Create a code frame to group your codes. The coding frame is the organizational structure of all your codes. And there are two commonly used types of coding frames, flat, or hierarchical. A hierarchical code frame will make it easier for you to derive insights from your analysis.
• Based on the number of times a particular code occurs, you can now see the common themes in your feedback data. This is insightful! If ‘bad customer service’ is a common code, it’s time to take action.

We have a detailed guide dedicated to manually coding your qualitative data .

Using software to speed up manual coding of qualitative data

An Excel spreadsheet is still a popular method for coding. But various software solutions can help speed up this process. Here are some examples.

• CAQDAS / NVivo - CAQDAS software has built-in functionality that allows you to code text within their software. You may find the interface the software offers easier for managing codes than a spreadsheet.
• Dovetail/EnjoyHQ - You can tag transcripts and other textual data within these solutions. As they are also repositories you may find it simpler to keep the coding in one platform.
• IBM SPSS - SPSS is a statistical analysis software that may make coding easier than in a spreadsheet.
• Ascribe - Ascribe’s ‘Coder’ is a coding management system. Its user interface will make it easier for you to manage your codes.

Automating the qualitative coding process using thematic analysis software

In solutions which speed up the manual coding process, you still have to come up with valid codes and often apply codes manually to pieces of feedback. But there are also solutions that automate both the discovery and the application of codes.

Advances in machine learning have now made it possible to read, code and structure qualitative data automatically. This type of automated coding is offered by thematic analysis software .

Automation makes it far simpler and faster to code the feedback and group it into themes. By incorporating natural language processing (NLP) into the software, the AI looks across sentences and phrases to identify common themes meaningful statements. Some automated solutions detect repeating patterns and assign codes to them, others make you train the AI by providing examples. You could say that the AI learns the meaning of the feedback on its own.

Thematic automates the coding of qualitative feedback regardless of source. There’s no need to set up themes or categories in advance. Simply upload your data and wait a few minutes. You can also manually edit the codes to further refine their accuracy.  Experiments conducted indicate that Thematic’s automated coding is just as accurate as manual coding .

Paired with sentiment analysis and advanced text analytics - these automated solutions become powerful for deriving quality business or research insights.

You could also build your own , if you have the resources!

The key benefits of using an automated coding solution

Automated analysis can often be set up fast and there’s the potential to uncover things that would never have been revealed if you had given the software a prescribed list of themes to look for.

Because the model applies a consistent rule to the data, it captures phrases or statements that a human eye might have missed.

Complete and consistent analysis of customer feedback enables more meaningful findings. Leading us into step 4.

Step 4: Analyze your data: Find meaningful insights

Now we are going to analyze our data to find insights. This is where we start to answer our research questions. Keep in mind that step 4 and step 5 (tell the story) have some overlap . This is because creating visualizations is both part of analysis process and reporting.

The task of uncovering insights is to scour through the codes that emerge from the data and draw meaningful correlations from them. It is also about making sure each insight is distinct and has enough data to support it.

Part of the analysis is to establish how much each code relates to different demographics and customer profiles, and identify whether there’s any relationship between these data points.

Manually create sub-codes to improve the quality of insights

If your code frame only has one level, you may find that your codes are too broad to be able to extract meaningful insights. This is where it is valuable to create sub-codes to your primary codes. This process is sometimes referred to as meta coding.

Note: If you take an inductive coding approach, you can create sub-codes as you are reading through your feedback data and coding it.

While time-consuming, this exercise will improve the quality of your analysis. Here is an example of what sub-codes could look like.

You need to carefully read your qualitative data to create quality sub-codes. But as you can see, the depth of analysis is greatly improved. By calculating the frequency of these sub-codes you can get insight into which  customer service problems you can immediately address.

Correlate the frequency of codes to customer segments

Many businesses use customer segmentation . And you may have your own respondent segments that you can apply to your qualitative analysis. Segmentation is the practise of dividing customers or research respondents into subgroups.

Segments can be based on:

• Demographic
• And any other data type that you care to segment by

It is particularly useful to see the occurrence of codes within your segments. If one of your customer segments is considered unimportant to your business, but they are the cause of nearly all customer service complaints, it may be in your best interest to focus attention elsewhere. This is a useful insight!

Manually visualizing coded qualitative data

There are formulas you can use to visualize key insights in your data. The formulas we will suggest are imperative if you are measuring a score alongside your feedback.

If you are collecting a metric alongside your qualitative data this is a key visualization. Impact answers the question: “What’s the impact of a code on my overall score?”. Using Net Promoter Score (NPS) as an example, first you need to:

• Calculate overall NPS
• Calculate NPS in the subset of responses that do not contain that theme
• Subtract B from A

Then you can use this simple formula to calculate code impact on NPS .

You can then visualize this data using a bar chart.

You can download our CX toolkit - it includes a template to recreate this.

Trends over time

This analysis can help you answer questions like: “Which codes are linked to decreases or increases in my score over time?”

We need to compare two sequences of numbers: NPS over time and code frequency over time . Using Excel, calculate the correlation between the two sequences, which can be either positive (the more codes the higher the NPS, see picture below), or negative (the more codes the lower the NPS).

Now you need to plot code frequency against the absolute value of code correlation with NPS. Here is the formula:

The visualization could look like this:

These are two examples, but there are more. For a third manual formula, and to learn why word clouds are not an insightful form of analysis, read our visualizations article .

Using a text analytics solution to automate analysis

Automated text analytics solutions enable codes and sub-codes to be pulled out of the data automatically. This makes it far faster and easier to identify what’s driving negative or positive results. And to pick up emerging trends and find all manner of rich insights in the data.

Another benefit of AI-driven text analytics software is its built-in capability for sentiment analysis, which provides the emotive context behind your feedback and other qualitative textual data therein.

Thematic provides text analytics that goes further by allowing users to apply their expertise on business context to edit or augment the AI-generated outputs.

Since the move away from manual research is generally about reducing the human element, adding human input to the technology might sound counter-intuitive. However, this is mostly to make sure important business nuances in the feedback aren’t missed during coding. The result is a higher accuracy of analysis. This is sometimes referred to as augmented intelligence .

Step 5: Report on your data: Tell the story

The last step of analyzing your qualitative data is to report on it, to tell the story. At this point, the codes are fully developed and the focus is on communicating the narrative to the audience.

A coherent outline of the qualitative research, the findings and the insights is vital for stakeholders to discuss and debate before they can devise a meaningful course of action.

Creating graphs and reporting in Powerpoint

Typically, qualitative researchers take the tried and tested approach of distilling their report into a series of charts, tables and other visuals which are woven into a narrative for presentation in Powerpoint.

Using visualization software for reporting

With data transformation and APIs, the analyzed data can be shared with data visualisation software, such as Power BI or Tableau , Google Studio or Looker. Power BI and Tableau are among the most preferred options.

Visualizing your insights inside a feedback analytics platform

Feedback analytics platforms, like Thematic, incorporate visualisation tools that intuitively turn key data and insights into graphs.  This removes the time consuming work of constructing charts to visually identify patterns and creates more time to focus on building a compelling narrative that highlights the insights, in bite-size chunks, for executive teams to review.

Using a feedback analytics platform with visualization tools means you don’t have to use a separate product for visualizations. You can export graphs into Powerpoints straight from the platforms.

Conclusion - Manual or Automated?

There are those who remain deeply invested in the manual approach - because it’s familiar, because they’re reluctant to spend money and time learning new software, or because they’ve been burned by the overpromises of AI.  

For projects that involve small datasets, manual analysis makes sense. For example, if the objective is simply to quantify a simple question like “Do customers prefer X concepts to Y?”. If the findings are being extracted from a small set of focus groups and interviews, sometimes it’s easier to just read them

However, as new generations come into the workplace, it’s technology-driven solutions that feel more comfortable and practical. And the merits are undeniable.  Especially if the objective is to go deeper and understand the ‘why’ behind customers’ preference for X or Y. And even more especially if time and money are considerations.

The ability to collect a free flow of qualitative feedback data at the same time as the metric means AI can cost-effectively scan, crunch, score and analyze a ton of feedback from one system in one go. And time-intensive processes like focus groups, or coding, that used to take weeks, can now be completed in a matter of hours or days.

But aside from the ever-present business case to speed things up and keep costs down, there are also powerful research imperatives for automated analysis of qualitative data: namely, accuracy and consistency.

Finding insights hidden in feedback requires consistency, especially in coding.  Not to mention catching all the ‘unknown unknowns’ that can skew research findings and steering clear of cognitive bias.

Some say without manual data analysis researchers won’t get an accurate “feel” for the insights. However, the larger data sets are, the harder it is to sort through the feedback and organize feedback that has been pulled from different places.  And, the more difficult it is to stay on course, the greater the risk of drawing incorrect, or incomplete, conclusions grows.

Though the process steps for qualitative data analysis have remained pretty much unchanged since psychologist Paul Felix Lazarsfeld paved the path a hundred years ago, the impact digital technology has had on types of qualitative feedback data and the approach to the analysis are profound.  

If you want to try an automated feedback analysis solution on your own qualitative data, you can get started with Thematic .

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• Knowledge Base
• Methodology
• What Is Qualitative Research? | Methods & Examples

What Is Qualitative Research? | Methods & Examples

Published on 4 April 2022 by Pritha Bhandari . Revised on 30 January 2023.

Qualitative research involves collecting and analysing non-numerical data (e.g., text, video, or audio) to understand concepts, opinions, or experiences. It can be used to gather in-depth insights into a problem or generate new ideas for research.

Qualitative research is the opposite of quantitative research , which involves collecting and analysing numerical data for statistical analysis.

Qualitative research is commonly used in the humanities and social sciences, in subjects such as anthropology, sociology, education, health sciences, and history.

• How does social media shape body image in teenagers?
• How do children and adults interpret healthy eating in the UK?
• What factors influence employee retention in a large organisation?
• How is anxiety experienced around the world?
• How can teachers integrate social issues into science curriculums?

Table of contents

Approaches to qualitative research, qualitative research methods, qualitative data analysis, advantages of qualitative research, disadvantages of qualitative research, frequently asked questions about qualitative research.

Qualitative research is used to understand how people experience the world. While there are many approaches to qualitative research, they tend to be flexible and focus on retaining rich meaning when interpreting data.

Common approaches include grounded theory, ethnography, action research, phenomenological research, and narrative research. They share some similarities, but emphasise different aims and perspectives.

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Each of the research approaches involve using one or more data collection methods . These are some of the most common qualitative methods:

• Observations: recording what you have seen, heard, or encountered in detailed field notes.
• Interviews:  personally asking people questions in one-on-one conversations.
• Focus groups: asking questions and generating discussion among a group of people.
• Surveys : distributing questionnaires with open-ended questions.
• Secondary research: collecting existing data in the form of texts, images, audio or video recordings, etc.
• You take field notes with observations and reflect on your own experiences of the company culture.
• You distribute open-ended surveys to employees across all the company’s offices by email to find out if the culture varies across locations.
• You conduct in-depth interviews with employees in your office to learn about their experiences and perspectives in greater detail.

Qualitative researchers often consider themselves ‘instruments’ in research because all observations, interpretations and analyses are filtered through their own personal lens.

For this reason, when writing up your methodology for qualitative research, it’s important to reflect on your approach and to thoroughly explain the choices you made in collecting and analysing the data.

Qualitative data can take the form of texts, photos, videos and audio. For example, you might be working with interview transcripts, survey responses, fieldnotes, or recordings from natural settings.

Most types of qualitative data analysis share the same five steps:

• Prepare and organise your data. This may mean transcribing interviews or typing up fieldnotes.
• Review and explore your data. Examine the data for patterns or repeated ideas that emerge.
• Develop a data coding system. Based on your initial ideas, establish a set of codes that you can apply to categorise your data.
• Assign codes to the data. For example, in qualitative survey analysis, this may mean going through each participant’s responses and tagging them with codes in a spreadsheet. As you go through your data, you can create new codes to add to your system if necessary.
• Identify recurring themes. Link codes together into cohesive, overarching themes.

There are several specific approaches to analysing qualitative data. Although these methods share similar processes, they emphasise different concepts.

Qualitative research often tries to preserve the voice and perspective of participants and can be adjusted as new research questions arise. Qualitative research is good for:

• Flexibility

The data collection and analysis process can be adapted as new ideas or patterns emerge. They are not rigidly decided beforehand.

• Natural settings

Data collection occurs in real-world contexts or in naturalistic ways.

• Meaningful insights

Detailed descriptions of people’s experiences, feelings and perceptions can be used in designing, testing or improving systems or products.

• Generation of new ideas

Open-ended responses mean that researchers can uncover novel problems or opportunities that they wouldn’t have thought of otherwise.

Researchers must consider practical and theoretical limitations in analysing and interpreting their data. Qualitative research suffers from:

• Unreliability

The real-world setting often makes qualitative research unreliable because of uncontrolled factors that affect the data.

• Subjectivity

Due to the researcher’s primary role in analysing and interpreting data, qualitative research cannot be replicated . The researcher decides what is important and what is irrelevant in data analysis, so interpretations of the same data can vary greatly.

• Limited generalisability

Small samples are often used to gather detailed data about specific contexts. Despite rigorous analysis procedures, it is difficult to draw generalisable conclusions because the data may be biased and unrepresentative of the wider population .

• Labour-intensive

Although software can be used to manage and record large amounts of text, data analysis often has to be checked or performed manually.

Quantitative research deals with numbers and statistics, while qualitative research deals with words and meanings.

Quantitative methods allow you to test a hypothesis by systematically collecting and analysing data, while qualitative methods allow you to explore ideas and experiences in depth.

There are five common approaches to qualitative research :

• Grounded theory involves collecting data in order to develop new theories.
• Ethnography involves immersing yourself in a group or organisation to understand its culture.
• Narrative research involves interpreting stories to understand how people make sense of their experiences and perceptions.
• Phenomenological research involves investigating phenomena through people’s lived experiences.
• Action research links theory and practice in several cycles to drive innovative changes.

Data collection is the systematic process by which observations or measurements are gathered in research. It is used in many different contexts by academics, governments, businesses, and other organisations.

There are various approaches to qualitative data analysis , but they all share five steps in common:

• Prepare and organise your data.
• Review and explore your data.
• Develop a data coding system.
• Assign codes to the data.
• Identify recurring themes.

The specifics of each step depend on the focus of the analysis. Some common approaches include textual analysis , thematic analysis , and discourse analysis .

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Bhandari, P. (2023, January 30). What Is Qualitative Research? | Methods & Examples. Scribbr. Retrieved 22 April 2024, from https://www.scribbr.co.uk/research-methods/introduction-to-qualitative-research/

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26 Gathering data on the Internet: Qualitative approaches and possibilities for mixed methods research

Claire Hewson, Faculty of Social Sciences, The Open University

• Published: 18 September 2012
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This article outlines the range of possibilities for conducting qualitative interview, observation, and document-analysis data-gathering procedures in an Internet-mediated research (IMR) context. It considers the range of approaches, tools, and technologies for supporting such research, as well as the advantages and disadvantages incurred in using the different techniques. The article argues that mixed-methods approaches are valuable, and that these may benefit particularly from being adapted to an IMR environment, since the cost and time savings that IMR affords may be especially useful in supporting research which is typically high in resource demands.

Introduction

This chapter first outlines the scope for implementing qualitative data-gathering techniques on the Internet, considering the tools, technologies and procedures available, their advantages and disadvantages (compared with each other and with traditional approaches) and some ethical issues which emerge. Possibilities for conducting interviews, observation and document analysis on the Internet are considered, these having been identified as the main tools of data collection used by qualitative researchers (Mann and Stewart 2000 ). In the course of the chapter, specific procedures and technologies will be described, with reference to web resources (such as free software, databases, etc.) which may be of use to the qualitative researcher. A precise and robust distinction between qualitative and quantitative research is not easily derived (Mann and Stewart 2000 ), but for current purposes qualitative approaches are seen as characterized by an emphasis on gathering rich, elaborate, meaningful, language- or image-based data amenable to a ‘thick’ interpretive description. Quantitative approaches in contrast are characterized as those which aim to generate numerical data amenable to statistical analysis, with an emphasis on generalizability, validity and reliability. Qualitative researchers are essentially concerned with questions about how people construct meanings, and how these meanings may vary over different historical, cultural and individual contexts. Political, social and action/change-oriented strategies may often be integral to qualitative approaches (e.g. feminist research; action research). Qualitative researchers also tend to recognize and welcome subjectivity as part of the research process, emphasizing the importance of researcher reflexivity. Quantitative researchers, on the other hand, tend to strive for objectivity and aim to derive context-free generalizations which cut across historical and cultural boundaries. While the methods described in the present chapter (interviews, observation, document analysis) may be applied in a more qualitative or quantitative manner (it is probably most useful to view qualitative and quantitative approaches as opposite poles of a continuum), interest here is in a fully qualitative research orientation, as just characterized. After a thorough consideration of the range of qualitative research techniques which the Internet can support, the chapter concludes with some comments on the scope for conducting mixed methods research on the Internet. Mixed methods approaches often combine both qualitative and quantitative techniques, and are becoming increasingly advocated by social science researchers (e.g. Johnson et al . 2004 ). The benefits of adopting a mixed methods approach will be further considered in the final section of this chapter. Quantitative methods (e.g. structured surveys, experiments, structured observation), though integral to mixed methods approaches, are not considered in any depth in the present chapter, since they are well-covered elsewhere in the current volume.

Online interviews

Tools, technologies and procedures, synchronous approaches.

Traditionally, interviews are conducted face-to-face (ftf) or via telephone (Mann and Stewart 2000 ). These approaches are synchronous , in that they take place in real time. Conducting synchronous interviews via the Internet is possible using online ‘chat’ facilities, such as Internet Relay Chat (IRC). This allows users to log on to a server and communicate with others in a text-based conversational environment, typically referred to as a chat room. Individuals communicate by typing in text, which is then displayed in a discussion window, viewable by all participants. On a practical level, both researcher and interviewee must have access to the relevant equipment (a computer and Internet connection), appropriate software program (IRC client, which enables connection to an IRC server), and the necessary experience and skills needed to be able to communicate via this medium. A number of IRC clients are available, either freely or commercially; for example, the popular ‘mIRC’ (available at http://www.mirc.com/ ), though a one-off payment ($20, at the time of writing) is required in order to continue using this software after the 30-day trial period. Other IRC clients, including some ‘freeware’ (meaning no payment is required) packages, are listed and available for download at the very useful ‘Tucows’ website ( http://www.tucows.com ). 1 Online synchronous chat is also supported by several other more general software packages, such as Virtual Learning Environments (VLEs). Both the freely available, open source VLE ‘Moodle’ ( http://www.moodle.org ), and the more expensive, commercial package ‘WebCT’ ( http://www.WebCT.com ) 2 allow users to take part in synchronous online discussion sessions.

Chat rooms tend to involve discussion amongst groups of several participants, though many also provide scope for ‘going private’, thus supporting dyadic interactions. An alternative option for one-to-one (and perhaps multi-user) discussions, however, is offered by Instant Messaging (IM) software. Here, users who are logged on at the same time are able to send messages rapidly back and forth, thus creating what may be considered a close approximation to a real-time conversation. Both conversational partners will need to have appropriate software installed on their computer; free IM packages include Microsoft's MSN Messenger for Windows (available at http://messenger.msn.com/ ), and ICQ (‘I Seek You’, available at http://www.icq.com/ ) which supports both Windows and Macintosh platforms.

The range of technologies described above makes conducting both one-to-one and larger ‘focus group’ style interviews online feasible (focus groups are interviews involving more than one participant, moderated by the researcher). A number of researchers to date have reported making use of these Internet technologies to support interview-based approaches, with differing levels of success. Davis et al . ( 2004 ) report a study looking at gay mens' experiences of seeking sex via the Internet, with particular focus on their awareness of, attitudes towards, and practice of safe and risky sex. These researchers used IRC style software to carry out one-to-one online synchronous interviews in private chat rooms in ‘Gay.com’. Though the procedure was somewhat successful, in that participants were able to log on and engage in discussion with the researcher (probably aided by the fact that they were already experienced Internet users), Davis et al . report finding the data generated by this method impoverished and limited, due to the tendency for superficial, non-elaborate and brief exchanges. Madge and OʼConnor ( 2002 ), on the other hand, report greater success. In contrast to Davis et al .'s approach of using an existing chat room environment, Madge and OʼConnor set up their own synchronous discussion forum, on a web server, specifically designed for the purpose of supporting their ‘Cyberparents’ research project. Participants were recruited from an existing online community of new and soon-to-be parents (at http://www.babyworld.co.uk ), and online focus groups were conducted after volunteers had received and installed the necessary software for accessing the researchers' discussion forum. Madge and OʼConnor report that the online focus groups were successful in generating rich, elaborate data, in which participants were forthcoming in sharing their views, experiences and feelings relating to issues surrounding childbirth and parenting. Possible reasons for the different experiences of these researchers are explored further below.

The possibilities for conducting online synchronous interviews, as described above, does of course impose the constraint that any discussion must proceed in entirely text-based format. Thus the types of non-verbal (e.g. body language) and paralinguistic (e.g. tone of voice) cues which are typically present in ftf contexts are not available in the online interview (the implications of which are discussed later). Technologies do exist, though, for incorporating audio and video capabilities into online real-time chat, thus allowing possibilities for creating what may be the closest approximation to a ftf interview without direct spatial proximity (see for example Apple's iSight at www.apple.com ). However, at present many Internet users are unlikely to have ready access to the equipment necessary for implementing such an approach, i.e. a microphone and speakers (for audio), and a webcam (for video), and this will thus severely restrict the types of samples that can be obtained using this method. Furthermore, the use of audio and video in online communication is at present hampered by a range of technical difficulties, which makes the approach currently unfeasible for conducting online interviews (see also Chapter 29 , this volume). These problems include low reliability and sound/image quality, due to the need to transfer large amounts of data using relatively limited bandwidths (for most users at least), 3 which can lead to ‘packet loss’ (Apostolopoulos 2001 ), and produce jerky video and inaudible sound (Hewson et al . 2003 ). Even assuming the availability of a high-speed Internet connection, data transmission can still be impeded by the unpredictable effects of the volume of network traffic, and the resulting fluctuation in bandwidths that this can cause. The impact of varying data transmission rates in online communications using audio and video has been explored by Yoshini et al . ( 2001 ), in a context which has relevance for online interview approaches. These researchers conducted online clinical assessment interviews, using both low (128 kilobits/s) and high (2 megabits/s) bandwidths, and found that the high bandwidth online-assessment interviews generated greater agreement with ftf interviews than did the low bandwidth assessment interviews. In the low bandwidth condition, raters reported problems with the audio and video quality, and the near impossibility of observing facial expressions (Yoshini et al . 2001 ). While many Internet users in the UK will now likely have Internet connection speeds of greater than 128 kilobytes/s, many will not yet have speeds of 2 megabytes/s. However, increasingly fast Internet connection speeds are clearly becoming more available, and at decreasing cost.

As well as bandwidth issues, other factors may influence the ability to transfer audio and video data reliably via the Internet. Firewalls, for example, can prevent data transmission. Also, the technical specification of a user's local computer can have an impact, with relatively high processing speeds and high-quality hardware (e.g. webcam, monitor), being needed to produce good quality images and sound. The upshot of all these considerations is that, at present, incorporating sound and video into online interviews is not advisable, except possibly in well-controlled environments where it is known that participants have suitable equipment, and where steps are taken to maximize reliability (Hewson et al . 2003 ). Even then, interruptions such as the loss of a connection, or slow data transmission rates, are likely to occur. The extent and impact of such technical difficulties remains to be explored; to date, online interviewers have tended to stick to the more reliable text-based approaches.

Asynchronous approaches

The Internet may also be used to support asynchronous interviews. Indeed, a particularly interesting feature of the Internet is the possibilities it creates for novel forms of asynchronous communication, email probably being the most widely known and used technology. While in some ways analogous to postal mail, emails travel much more swiftly than letters, arriving at the recipient's mail server account only seconds after the sender has pressed the send button. 4 Thus, in theory, a response can be returned within a matter of minutes; in practice, email exchanges are often punctuated with periods of hours, days or even weeks intervening (e.g. Murray and Sixsmith 1998 ). Email thus provides a potentially useful tool for conducting online asynchronous interviews, due to the typically more relaxed timing of email exchanges compared with the tools mentioned above (IM, online chat), whose typically more ‘interactive’ contexts of usage, and the expectation for an instantaneous response, make them more suitable for conducting synchronous interviews. In order to conduct an email interview, both interviewer and interviewee need access to the appropriate technologies: a computer with an email client installed (e.g. Pegasus, Eudora, Outlook), an ‘email account’ on a mail server (provided by an ISP, or Internet Service Provider) and an Internet connection (e.g. via dial-up, ADSL broadband, or cable). Having made initial contact (see below for a discussion of sampling possibilities) and gained consent for participation, the interviewer may initiate the interview by sending questions or topics for discussion in the body of an email message, to which the interviewee can respond in their own time. Follow-up questions and responses can then create an ongoing interview-type dialogue, expanding over a time period negotiated by the participants. Given the asynchronous nature of this approach, it may well be appropriate to extend the interview over a period of several weeks in order to allow sufficient exploration of the topic at hand, depending on the time frequency of exchanges. However, it has been noted that it is the researcher's responsibility to impose a time limit for completion of email interviews, to avoid the process dragging out the duration of a research study (Murray and Sixsmith 1998 ).

Probably one of the earliest attempts to use email to conduct asynchronous interviews has been reported by Murray and Sixsmith ( 1998 ). In a study of people's experiences of prosthesis use, these researchers recruited participants via topic-relevant specialist listserv discussion groups (or mailing lists, discussed further below) and then conducted one-to-one email interviews with individuals who volunteered to take part. From their experiences, they conclude that the email interview presents a viable and valuable approach, alongside traditional ftf interview methods. Bowker and Tuffin ( 2004 ) have more recently adopted a similar approach, recruiting disabled people who were already using the Internet and/or email, and conducting one-to-one email interviews with these participants in order to find out about their perceptions of the Internet as a source of information and support on disability issues. Again, these authors report overall success in using this method, describing the email interview as offering an ‘effective and appropriate approach for accessing discourse about the online experiences of people with disabilities’ (Bowker and Tuffin 2004 : 228).

As well as the possibilities email offers for conducting one-to-one asynchronous interviews, the ease of emailing to several respondents simultaneously also provides scope for conducting asynchronous online focus groups. Most simply, online focus groups may be conducted by instructing all participants to send their email messages to all other group members. If the group moderator (researcher) starts by sending an introductory email to everyone in the group, then it is a simple matter for any individual recipient to hit the ‘reply all’ button in their mail client in order to post a comment to the whole group. However, probably a more reliable and efficient approach (e.g. some participants may forget to mail everyone in the group) would be to make use of specialist software available for managing dissemination of individuals' email postings to all members of a group. Listserv 5 is probably the most well-known software application for managing group mailing lists, which are also referred to as ‘discussion lists’ or ‘discussion groups’ (the latter not to be confused with Usenet discussion groups or ‘newsgroups’ which are described later, but which serve a similar purpose). Thousands of mailing lists exist on the Internet, each one managed by a listserver, covering topics ranging from mountain biking to quantum theories of consciousness. Some of these lists will have a moderator who monitors the messages before they are posted to list members and can thus intercept messages which are deemed off-topic or inappropriate in any other way; unmoderated lists do not have a moderator and messages are automatically relayed to all subscribers. If a list is ‘open’ (which many are) all a user needs to do to join is send a subscribe email to the relevant listserver address, and the Listserv software will add them automatically. For further details on how to locate and subscribe to lists on particular topics see the listserv primer in Appendix A. Catalist ( http://www.lsoft.com/catalist.html ) is a convenient web-based resource which provides access to a large searchable database of mailing lists, some of which allow subscription via a web browser.

Mailing lists thus provide a potential resource for implementing online asynchronous focus groups, though in many cases it will not be viable for a researcher to simply jump into an existing list and ask to conduct a focus group interview. Aside from the impracticalities concerning distinguishing the research focus group's discussion (involving those who have agreed to participate) from the larger group's discussion, it is generally considered impolite to join a group and post off-topic requests or comments. 6 Of course, in much qualitative research the aim is to target special interest groups and elicit their views on a shared topic of interest, such as in the work of Murray and Sixsmith ( 1998 ) and Bowker and Tuffin ( 2004 ) mentioned above, who did recruit people for one-to-one email interviews via specialist mailing list discussion groups. Such on-topic requests, projected via a mailing list moderator, should not be problematic, and are unlikely to cause offence. Several researchers have successfully adopted this approach to recruit participants for online asynchronous focus group interviews. Gaiser ( 1997 ), for example, reports posting requests (via moderators) to existing listserv discussion groups, and then making use of a private distribution list set up for research purposes in which to conduct focus group discussions with volunteers. This approach requires access to Listserver software, and the required level of technical expertise (or assistance) to set up a dedicated mailing list. For more information and guidance on how to do this see the Introductory List Owners Guide and other related documents available at http://www.lsoft.com/resources/manuals.asp (accessed 15 March 2005).

Other software applications which serve a very similar purpose to mailing lists may also prove useful for conducting asynchronous online focus groups. These include Bulletin Board System (BBS) software, which as the name suggests aims to provide a virtual bulletin board on which users can post messages which are then readable by anyone who has access to the board (and this can be restricted, e.g. via a user-name/password). Yahoo! provides a list of BBSs at: http://uk.dir.yahoo.com/Computers_and_Internet/Internet/Chats_and_Forums/Bulletin_Board_System_BBS_/ . 7 Usenet has been referred to as a ‘giant distributed bulletin board system’; it uses the Internet to provide access to thousands of newsgroups, which are essentially like massive bulletin boards which may have hundreds of readers. 8 Anyone can subscribe to and post messages to these discussion boards if they have a computer with the appropriate software for accessing a news server (to which they must be subscribed 9 ) on which these newsgroups reside. While some groups will be set up to restrict access, most are open. Many web browsers are able to act as newsreaders (e.g. versions of Firefox, Explorer, and Safari). Also, Usenet groups can be accessed and managed at the web-based interface: http://groups.google.com/ , using a web browser without the need for any additional newsreader software, or access to a news server. This web-based interface allows users to create new groups, selecting from several different access options (e.g. public, invitation only), very easily in a matter of minutes, with minimal levels of user expertise. Given their massive distributed nature, and the particular customs, rules, goals and agendas of the online communities they support, many existing Usenet newsgroups are unlikely to prove useful for conducting online focus group interviews (for the same types of reasons as outlined above in relation to mailing list discussion groups). However, as just noted new specialist and restricted access groups can easily be created. The larger, existing groups may provide a useful source of access to participants, as well as a potential resource for online observational research (as discussed below).

Other software packages, such as the VLEs mentioned earlier, will also often incorporate asynchronous (as well as synchronous) discussion tools which function very much like the online discussion forums just mentioned. These may thus provide another alternative tool for researchers wishing to conduct online interviews, who have access to such packages. Kenny ( 2005 ) has reported successfully using the VLE WebCT, and its integrated multithread discussion board, to conduct an online asynchronous focus group interview with 38 Australian nurses.

Finally, a more recent development serving essentially the same function as Usenet groups is the World Wide Web (WWW) forum. WWW forums are discussion forums hosted on a web server, and having the advantage that they do not typically require any specialist software (e.g. a newsreader) for access and participation other than a basic web browser and Internet connection. A searchable database of WWW forums is available at: http://www.ezboard.com . Information on how to set up a WWW forum is also available at the ezboard website; registering is free and setting up and maintaining a board, or ‘online community’, costs around $4 a month (after a 30-day free trial period). Setting up a WWW forum may thus be the easiest way for a researcher to conduct an online focus group, in terms of the levels of equipment, software, expertise and experience required.

Advantages of online interviews

A number of general advantages of Internet-mediated research (IMR) have by now become well recognized, and many of these apply to the qualitative data-gathering techniques (interviews, observation and document analysis) considered in the present chapter. These advantages include increased cost and time efficiency (due to automation of procedural aspects and data input, and cost-effective recruitment procedures), broad geographical reach and access to specialist groups, potential for enhanced candidness in online communications, and reduced biases resulting from perceptions of biosocial attributes (see, for example, the summaries provided in Hewson [ 2003 ] and Reips [ 2000 ]). Several potential advantages are especially salient in relation to qualitative online interviewing approaches, including increased candidness and a greater willingness to discuss sensitive topics, increased depth and reflexivity, more balanced power relationships and facilitation of participation for otherwise difficult-to-access groups.

Enhanced candour and self-disclosure

The increased levels of anonymity typical in an online interview setting, compared with ftf contexts, may result in participants being more open and willing to disclose personal and sensitive information. Heightened anonymity emerges due to the lack of readily available information about biosocial attributes (e.g. age, sex, social class, and ethnic origin); though some such information can often be inferred from, for example, user-names, this information can be inaccurate or deliberately misleading and is typically unverifiable. Thus online interviewees may experience enhanced perceptions of privacy, since they are able to choose to conceal or disclose as much information about themselves as they wish. This may plausibly lead to a greater willingness to disclose information that may otherwise be withheld in a ftf setting. Also, the lack of social context cues available in the online interview, due to both greater levels of anonymity and a lack of paralinguistic information (body language, tone of voice, facial expression, etc.), may contribute to enhanced levels of self-disclosure, since participants may have less of a sense of being observed, and judged. In computer-mediated communication (CMC) in general (of which online communication is a specific instance), evidence has been presented for both higher levels of self-disclosure (e.g. Bargh et al . 2002 ; Joinson 2001 ) and reduced levels of socially desirable responding (e.g. Joinson 1999 ), compared with ftf communication contexts. Though there is evidence that these effects may be constrained to visually anonymous (i.e. excluding approaches making use of a video link, which were discussed above) CMC (Joinson 2001 ). Overall, there seems to be compelling evidence that CMC in general, and online communication in particular, does lead to enhanced levels of disclosure of sensitive and personal information (see Joinson and Paine, Chapter 16 this volume).

With regards to online interviews (compared with ftf approaches) several authors have confirmed these effects. Thus Murray and Sixsmith ( 1998 ) note that their online asynchronous interviews with prosthesis users were more effective in eliciting information on personal and intimate topics, such as respondents' sex lives, than were their ftf interviews. These authors suggest that in the latter context the direct physical proximity of the researcher is more likely to cause embarrassment to participants when personal topics are raised. Madge and OʼConnor ( 2002 ) also report what they perceived as the disinhibiting effects of the online environment during their online synchronous focus group interviews with new parents, noting that on some occasions participants were observed to engage in flirtatious behaviours, with each other and the researchers, which it was felt would likely not have occurred in a ftf setting. For a more comprehensive discussion of issues relating to levels of disclosure, privacy, anonymity and social regulation in CMC, across a broader range of contexts, the interested reader is referred to Joinson ( 2003 ), Spears et al . ( 2002 ), and Chapter 16 this volume.

Depth and reflexivity

A further possible advantage of online interviews, particularly in asynchronous contexts, is the scope for elicitating deeper, more reflective, detailed and accurate responses. The timescale of an asynchronous email interview can allow participants scope for reflection, in a way not achievable in synchronous (online or offline) approaches. Further, since a ready log of the conversation is automatically archived during an email exchange, participants (and researchers) can reflect back over the points and comments that have already been made at their leisure, which could serve to enhance levels of accuracy and reflection. Participants engaged in email or other asynchronous online interviews also have greater scope for checking the accuracy of information provided, e.g. by reference to documents, diaries, etc. Synchronous online interview approaches seem to offer less scope for allowing depthy, reflective responses, due to their less relaxed timescale, although they may share the feature of providing a readily available log of previous conversational elements, as the comments typed by each participant will often remain visible as they scroll up the chat window. This adds a further dimension to online synchronous interviews, compared with offline synchronous approaches, the implications of which requires further exploration. Overall, it is likely that asynchronous online interviews will be better able to generate the kind of deep, detailed, rich and reflective data typically desired by qualitative researchers, than online, and perhaps also offline, synchronous approaches.

Researchers using asynchronous online interviews have typically reported success in generating rich and reflective data (e.g. Murray and Sixsmith 1998 ; Bowker and Tuffin 2004 ; Kenny 2005 ). Bowker and Tuffin ( 2004 ) used both asynchronous and synchronous approaches within the same study, and report that the former were more successful in generating rich, detailed data; these authors suggest that the extended timescale of an email interview may account for this effect by inducing a ‘reduced sense of urgency’, thus allowing for more considered and lengthy responses. Online interview approaches may also lead to greater levels of researcher reflexivity , since the anonymous nature of the interaction can encourage participants to direct probing questions at the researcher (Ward 1999 ).

Balanced power relationships

There is some evidence that online, compared with ftf, interviews may lead to more balanced power relationships between researcher and participant, as well as between research participants themselves (e.g. in online focus groups). Factors which have been identified to account for this include the typically heightened levels of anonymity in online communication, as noted above, and the greater levels of participant control over where and when to participate (Madge and OʼConnor 2002 ). Evidence for such effects has been reported by Murray and Sixsmith ( 1998 ), who note that their online interviews proceeded in a manner whereby researcher and participant acted largely as co-researchers, an outcome often desired in qualitative research. Thus these researchers found that their online interviewees asserted themselves, and took on an active role in directing the flow 10 and direction of the interview, in a way which did not occur with their ftf interviewees. A third factor which has been highlighted as contributing to the enhanced empowerment of online interviewees relates to the increased scope for participants to become involved in the interpretative process, an ideal often advocated in qualitative research but rarely achieved. Murray and Sixsmith ( 1998 ) explain how the extended timescale of their asynchronous email interviews allowed them to maintain several ongoing interviews with different participants simultaneously, which in turn created possibilities for what they refer to as a ‘cross-fertilisation of ideas’; that is, the sharing (via the researcher) of comments between participants. The extended timescale involved also allowed the researchers to reflect upon and present their own interpretations of the interview data to participants, giving participants the opportunity to feed back their own reflections on these interpretations, thus leading to their greater involvement in the research process. Also relevant to the issue of more balanced power relationships in online communication, due to enhanced levels of anonymity, is Hewson et al ( 1996 ) suggestion that the lack of ready access to biosocial information in online interactions may help to reduce interpersonal perceptual biases, such as those due to the application of stereotypical assumptions relating to gender, social class or ethnicity (Hewson et al . 1996 ). The potential for such biases in traditional ftf interviews has long been recognized (Murray and Sixsmith 1998 ). The possible elimination, or reduction, of such biases in online interviews may contribute towards creating more balanced power relationships than may be possible in many offline ftf settings (though see the further discussion on this topic below for an alternative perspective).

Facilitating participation

The ability to respond remotely, as opposed to having to travel to an interview site, and in one's own timeframe, may have particular relevance in terms of facilitating participation for certain groups, such as disabled people (Bowker and Tuffin 2004 ), or new mothers (Madge and OʼConnor 2002 ). This may help give a voice to, and thereby empower, groups who may otherwise be under-represented due to participation barriers. Also, the possibilities for the ‘cross-fertilisation of ideas’ in email interviews (Murray and Sixsmith 1998 ), as mentioned above, may allow these to function somewhat like a focus group, yet without requiring the presence of all group members simultaneously. This could confer benefits in facilitating participation from, and interaction amongst, respondents across different geographical regions and time zones in a way not practicable in ftf contexts. These outcomes are less likely to be applicable to synchronous approaches, however, since these demand participation at a set time, and certain skills and abilities, such as typing dexterity/speed, visual acuity, etc. These constraints in synchronous interviews may thus create participation barriers for certain groups, such as those with a physical disability which makes them less able to type speedy responses.

Overall, there is evidence that online interviews can offer the advantages of encouraging more candid responses from participants, and a willingness to discuss personal and sensitive topics, compared with ftf approaches, due to the effects of anonymity and a lack of paralinguistic social cues. Also, they may encourage more balanced power relations, due to less information being available about personal characteristics (sex, class, race, etc.), and interviewees' greater control over the participation context. Asynchronous approaches may have an advantage over synchronous approaches, both online and offline, in facilitating the generation of richer data, due to a more relaxed timescale. Asynchronous approaches may also help facilitate participation from a broader range of participants.

Disadvantages of online interviews

Online interviews are also susceptible to the general disadvantages which have been identified with IMR (as noted above, see Hewson [ 2003 ], and Reips [ 2000 ] for summaries). The main issues concern restrictions on who can participate, due to the need for technological equipment and levels of expertise (of both researchers and participants) and the possibly biased nature of the Internet User Population (IUP) and subpopulations; threats to reliability and validity due to a lack of researcher control over and knowledge of procedural details; and difficulty in implementing appropriate ethical procedures in an online context. All these factors may affect online interviews, though reliability and validity issues are generally of more relevance in quantitative than qualitative research. Nevertheless, greater difficulty in verifying who has responded, and whether responses are genuine and honest, may constitute a disadvantage of online interview approaches, compared with ftf alternatives. In relation to participation restrictions, given the more widespread use of asynchronous communication tools, such as email, mailing lists, and WWW forums, than synchronous communication facilities, it would seem that synchronous online interview approaches must inevitably impose greater constraints on who can participate. While techniques are available for arriving at reasonable estimates of the IUP, i.e. by counting the number of accessible Internet hosts and IP addresses (Hewson et al . 2003 ), estimating the relative size and diversity of synchronous and asynchronous user populations is more difficult, and any such estimates will likely be inaccurate. Recent figures estimate the number of Internet-accessible hosts to be 353 million in July 2005, up from 147 million in January 2002 (Internet software consortium, http://www.isc.org/ ). Estimates of the number of Internet users will be much higher than the number of hosts; recent figures suggest around 1 billion users (e.g. Computer Industry Almanac Inc., http://www.c-i-a.com/pr0106.htm , accessed February 2006). What is clear from examination of the resources and tools outlined above is that both synchronous and asynchronous Internet communication technologies support vast communities of users, whatever actual proportion of the currently estimated number of Internet users each represents. The synchronous user population will no doubt be smaller, since whereas most synchronous users will also use asynchronous communication tools, the reverse is not true.

Several disadvantages of IMR seem to be of particular relevance for online interview approaches, including barriers in establishing rapport; reduced depth and greater levels of ambiguity due to the text-only nature of the communication medium; and difficulty in implementing ethical procedures.

Establishing rapport

Enhanced levels of anonymity in online communication, and a lack of paralinguistic cues, were mentioned earlier in terms of the potential benefits for reducing bias, balancing power relations, and enhancing levels of candidness and self-disclosure. However, these features may also embody the disadvantage of making it more difficult to establish rapport with participants in an online interview, due to the less ‘personal’ tone of the interaction. Researchers conducting online interviews have recognized and addressed this issue in a number of ways. One approach has been to adopt a strategy of (researcher) self-disclosure in order to encourage levels of trust from participants, and thus help establish levels of rapport. Madge and OʼConnor ( 2002 ) adopted this approach, posting pictures and biographies of themselves on their research project website, and engaging in preliminary one-to-one email interactions with focus group volunteers prior to the actual synchronous focus group sessions. The subsequent reported success in generating good rapport with participants, who were recruited via the website www.babyworld.com , may also have been aided by fact that the researchers were themselves new mothers, and thus shared a common identity with the participants. Bowker and Tuffin ( 2004 ) also used self-disclosure to try and encourage rapport in their asynchronous email interviews, but comment that establishing relational development did take longer than in ftf approaches. Though being able to meet participants ftf in advance, or being already casually acquainted, did help facilitate relational development, and the depth and ‘flow’ (here most appropriately referring to ‘fluency’ and ‘continuity’) of the conversation. Gaiser ( 1997 ) has reported successfully making use of ‘introductory exercises’ as a precursor to an asynchronous focus group session, in order to encourage group cohesion, bonding and participant ease. These examples all suggest that levels of rapport comparable to those which can be achieved in offline contexts are possible in online interviews. However, some researchers have reported less positive experiences. Strickland et al . ( 2003 ) report conducting online asynchronous focus groups with perimenopausal women, using an online discussion board set up for research purposes. Several groups were conducted, each consisting of between four and eight participants. However, these researchers found that participation was slow, and group members failed to spontaneously post to the discussion board, requiring reminders and prompts from the researcher. Also, some participants tended to get locked into one-to-one discussions with each other, while ignoring the researcher's prompts and questions. Perhaps of significance, compared with the above more successful reports, is that Strickland et al . do not report using any special strategies for building rapport, such as self-disclosure, or the use of personally engaging exercises; rather the researcher/moderator posted an initial welcome to the group along with a set of questions.

Further research is clearly needed to determine which factors may enhance or inhibit levels of rapport in online interviews. The above illustrations highlight some possible contributing factors, including the nature of the sample (in the above accounts, the studies where rapport was more readily developed utilized recruitment methods more likely to obtain participants who had greater familiarity with the online communication tools utilized in the study), and the use of strategies for building rapport and levels of relational development. Also, making some kind of preliminary contact, e.g. ftf or by email, or being acquainted, may help. Individual communication style may also play a role in the way an online interview progresses (Bowker and Tuffin 2004 ). Whether synchronous or asynchronous approaches have different implications in terms of establishing rapport is to date unclear, and as the examples noted above indicate, success has been achieved in both contexts.

Impoverished and ambiguous data

One implication of failing to establish good levels of rapport with online interview participants is a possible reduction in the frequency, depth and reflectivity of the responses obtained, due to a lack of engagement in the interview process (as was experienced by Strickland et al . 2003 ). As noted earlier, there is evidence that asynchronous approaches, at least, have the potential to encourage more reflective and detailed responses from interviewees. However, failure to establish good levels of rapport in online interviews could inhibit this effect. Another factor which may act as a barrier to obtaining rich and meaningful data in online interviews is the lack of paralinguistic information available, noted above as potentially beneficial in relation to the positive consequences of enhanced levels of anonymity. However, a potential disadvantage of this lack of paralinguistic information is reduced depth, and greater ambiguity, due to the inability to utilize cues such as facial expressions, body language, tone of voice and so on. Given that synchronous online interviews do not benefit from a relaxed timescale, and, enhanced participant control which may play a role in encouraging depth and reflectivity, the limitations due to a lack of paralinguistic information may be particularly problematic for these approaches. There is evidence that this is the case. Researchers have reported that synchronous online interviews generate less text than ftf approaches, even though they take longer (Schneider et al . 2002 ; Davis et al . 2004 ), and interviewees’ responses tend to convey ‘information’, but lack depth and meaning (Davis et al . 2004 ). Ambiguities have also been found to emerge in synchronous (e.g. Davis et al . 2004 ), and asynchronous (e.g. Bowker and Tuffin 2004 ) online interviews. Gaiser ( 1997 ) has suggested that because online chat is typically expected to be playful and superficial, it may be of limited value as an interview medium where detailed, sincere, thoughtful responses are desired. However, as noted above, Madge and OʼConnor ( 2002 ) have been able to generate rich, detailed, reflective data using online synchronous focus groups. Aside from the rapport-building techniques these researchers used, other factors may have contributed to their greater success in generating rich, elaborative data.

For one thing, the focus groups consisted entirely of women, and women have been shown to typically engage in more personal, cooperative and supportive styles of communication than men (e.g. see the chapters in Coates 1998 ). Secondly, a relatively novel approach was adopted in which one researcher took on the role of typist, using pre-prepared chunks of text to cut and paste into the discussion window, while the other acted as discussion group moderator. In any case, this example illustrates that rich, meaningful data can be achieved in synchronous contexts. Researchers should be especially aware of using techniques which may encourage this process.

While it has been taken for granted in the above discussion that online communication precludes possibilities for the use of paralinguistic information, ‘emoticons’ (e.g. smile::-), wink and smile: ;-), kiss: :- * ) and acronyms (e.g. LOL: ‘laughing out loud’, ROTFL: ‘rolling on the floor laughing’) can provide textual/graphical representations of facial expressions and/or body language. Using capital letters is taken to indicate shouting. Although perhaps a rather crude substitute for actual paralinguistic cues in ftf interactions, such textual/graphical linguistic devices can actually make online communication far richer than those who are unfamiliar with such devices might imagine. For experienced and fluent users these devices can both speed up and enrich the online communication experience. They may also serve the function of reducing ambiguities, which may otherwise arise in interpreting purely textual expressions; for example a smile may clarify whether an utterance is intended as hostile or friendly. Of course, naive users who are unfamiliar with the use of such online paralinguistic devices will find them awkward to use and interpret, at first. While commonly used in online synchronous chatroom discussions, such cues are probably used less liberally in asynchronous settings, such as email, where they are likely less necessary since the extended timescale allows for the use of richer and more descriptive language. Thus, while a lack of paralinguistic information in the online interview has been seen as particularly problematic for synchronous interviews, the use of emoticons and acronyms by experienced users may serve to create richer and less ambiguous communication. A further possible consequence of the lack of paralinguistic cues in both synchronous and asynchronous online interviews, which cannot be resolved by the use of online paralinguistic substitutes, is the greater ease afforded to interviewees to deceive the researcher. For example, online interviewees may easily lie about their age, gender or emotional state (Murray and Sixsmith 1998 ), or whether there is a third party present guiding their responses (Bowker and Tuffin 2004 ).

One possible disadvantage of asynchronous over synchronous approaches in online interviewing is the decreased sense of continuity and ‘flow’ which can characterize the discussion, as a result of the extended timescale. This may potentially lead to reduced clarity, and instances of ambiguity. ‘Flow’ in this sense refers roughly to the perceived links between conversational elements, such as questions from the interviewer. For example, if the interviewer has just asked ‘would you consider yourself racist?’, and then asks ‘what ethnic backgrounds do your friends have?’, these questions will likely be seen as linked in a synchronous exchange, but the connections may be less likely to be made in an asynchronous exchange. Murray and Sixsmith ( 1998 ) have indicated experiencing a lack of such continuity and flow in their asynchronous online interviews, referring to the ‘slow and interrupted flow of information’. Bowker and Tuffin ( 2004 ) have similarly commented on the interruptions which can occur in asynchronous communication settings, noting the frustration that would sometimes emerge from not knowing when their email interviewees might respond.

Ethical issues

Several novel ethical issues emerge in online interviewing, which also occur as more general concerns in Internet-mediated research. These include how to obtain informed consent (e.g. Kraut et al . 2004 ) and how to maintain data security and confidentiality (e.g. Gaiser 1997 ; Hessler et al . 2003 ). The latter issue is especially problematic in email-based approaches, where data travels across a number of servers (Internet-connected computers) and thus has the potential to be intercepted and viewed by a third party at a number of stages. Some issues will demand technological solutions, such as how to store participants' responses securely, so that they are safe from hackers, for example. This particular issue will, of course, be more pertinent to qualitative research eliciting data on sensitive and personal topics. Other issues emerge in the form of new questions and dilemmas for which there are no existing guidelines. In relation to interview-based approaches, these include questions about what is appropriate and acceptable behaviour online, e.g. is it okay to post unsolicited participation requests to selected newsgroups? To date, a comprehensive set of established guidelines on ethics in IMR has not been developed. However, there are a number of useful and informative publications on the topic (e.g. Kraut et al . 2004 ; Peden and Flashinski 2004 ; Birnbaum and Reips 2005 ). For a detailed discussion of ethical issues in online research see Ess (Chapter 31 this volume).

Sampling approaches for online interviews

A number of approaches are possible in recruiting online interview participants. As noted above, researchers have placed advertisements on existing websites (e.g. Madge and OʼConnor 2002 ), and posted invitations to newsgroups or mailing lists (e.g. Gaiser 1997 ; Murray and Sixsmith 1997).

Gaiser ( 1997 ) has suggested that recruiting all members of a focus group from the same list may not be optimal, due to the likelihood of thus obtaining a homogenous group of participants who are well-acquainted with each other (though in some instances this may be what is wanted). Other researchers have used email (e.g. Strickland et al . 2003 ), or made use of offline recruitment methods (e.g. Kenny 2005 ) or both offline and online methods (e.g. Strickland et al . 2003 ).

There has been some general concern about levels of bias inherent in the Internet user population (e.g. Bordia 1996 ; Schmidt 1997 ), though more recently it is becoming recognized that with the rapid growth and changing composition of the IUP, ever more diverse samples are achievable (Hewson 2003 ). The problems in obtaining probability samples via the Internet, arising due to difficulties in specifying a sampling frame, and implementing effective random selection procedures, has also received discussion (e.g. Dillman and Bowker 2001 ). It is especially difficult to verify that an individual has received a participation request in Internet-based methods, given the fluctuating nature of the IUP, changing and dormant email addresses, and so on. However, this issue is less of a problem in qualitative research where obtaining probability samples which can lead to broadly generalizable, representative data is typically less important than generating rich, meaningful responses which give insight into participants' own understandings (Hewson et al . 2003 ). The ready access to small, specialist, and difficult-to-access populations, compared with accessibility to these via traditional offline sampling methods, is a particular strength of Internet-mediated sampling approaches, relevant to the goals of much qualitative research. These populations can be accessed via specialist discussion groups, mailing lists, and topic-specific websites. Indeed, some researchers have found the Internet useful as a source of access to participants who are then asked to take part in a study offline; Parsons et al . ( 2004 ), for example, recruited a specialist sample of male escorts who used the Internet as a point of access to clients, since this population was of key interest to their research goals (comparing the experiences of these men with those of previously studied male escorts who traded in offline contexts). These men were contacted by obtaining their email addresses, through America Online user profiles, and male escort websites. Clearly the sampling approach adopted will influence the type of sample obtained, and in qualitative research the Internet can be especially useful in this respect. Several points are important when recruiting participants by any of the methods suggested above, including directing any requests to discussion groups via moderators where possible (Murray and Sixsmith 1998 ) and, in general, respecting principles of netiquette and codes of professional conduct (e.g. those set out by the British Psychological Society). A more detailed discussion of the sampling procedures available to online interviewers can be found in Mann and Stewart ( 2000 ).

Obviously samples recruited online will be restricted in certain ways. They will probably be particularly interested in the research study topic (though this may often be desirable). They will be already fairly well-acquainted with online technologies, and possess a level of computer literacy (as well as access to the relevant equipment and technologies) not representative of the population at large. Though recruiting participants already well-versed in Internet communication technologies may have the advantage of facilitating the interview process, as noted above. For any study, levels of participant selectivity will depend on both the sampling procedures employed and the procedural and technological demands imposed. Email interviews are likely to enable the widest level of participation, whereas synchronous real-time chat approaches will impose greater restrictions. Nevertheless, the broad geographical reach afforded in IMR has been noted as advantageous by a number of online interviewers (e.g. Murray and Sixsmith 1998 ; Gaiser 1997 ; Strickland et al . 2003 ). In many cases online approaches may be able to facilitate research which would otherwise be difficult or impossible, such as running focus group sessions with people from diverse locations around the world. 11

Summary of online interview approaches

In summary it would appear that both asynchronous and synchronous approaches in online interviewing may have their own benefits and drawbacks, and often trade-offs will be apparent, e.g. features leading to higher levels of anonymity in online interviews may encourage more candid, open responses, but also impede relational development, rapport-building, and richness of data. Similarly, the extended timescale of asynchronous interviews may serve both to allow for deeper, considered responses, and hinder conversational flow. Thus the researcher must make appropriate choices depending on the research context and goals. In general, asynchronous approaches seem to offer the advantages of being able to cut across geographical time zones, being easier for participants who are less fluent typists, and providing greater scope for detailed and reflective responses. Synchronous approaches may allow for a more flowing dialogue, and perhaps make establishing rapport more rapid (Bowker and Tuffin 2004 ). Synchronous approaches may also offer greater scope for making use of paralinguistic information. It is worth noting, however, that the distinction between synchronous and asynchronous approaches in online communication may not always be clear. Email, for example, is generally considered an asynchronous mode of communication, but it is certainly possible for email exchanges to be very rapid. Likewise online chat technologies can be used more or less synchronously (e.g. a participant may be distracted or multitasking). Perhaps the most useful approach may be to view Internet-based forms of communication as lying somewhere on a continuum between synchronous and asynchronous.

Online observation and document analysis

Observational approaches and the analysis of documents are closely related in an Internet-mediated research context, since both may involve the analysis of stored records available online. These will be logs of text-based verbal exchanges between people in the case of observational research, e.g. from archives of discussion forums, mailing lists, and so on (e.g. Bordia 1996 ; Herring et al . 1998 ) and ‘documents’ created for the purpose of publishing on the WWW, in the case of document analysis (e.g. Schütz and Machilek 2003 ). Thus the distinction between observational research and document analysis can become a little blurred in an IMR context, compared with traditional offline approaches where the observational researcher typically observes behaviour in real-time (often creating a log of this behaviour in the form of a video or audio recording for subsequent playback and analysis) while the document analyst makes use of existing text- or image-based records. The nature of the Internet, and the types of communication systems it supports, is such that many online interactions will be automatically logged, thus eliminating the need for the observational researcher to actively engage in this part of the data-gathering process, having only to locate and access the already available archives of online interactions. For the purposes of the current discussion, a working definition will be adopted of online observational research as that which uses logs of interactions (typically verbal exchanges) between participants, as opposed to document analysis which makes use of static records constructed specifically for the purpose of dissemination via the Internet, and whose primary purpose is not to facilitate an ongoing dialogue-type communication between individuals. Of course, observation of online interactions in real-time is also a possibility, and clearly a case of observational research.

Tools, technologies and procedures for online observation and document analysis

Observation.

Qualitative researchers have been inclined to use ‘naturalistic’ observation, that is, observation of behaviour in a typical everyday setting familiar to participants, as opposed to the more controlled, artificial, experimental environments commonly used by quantitative researchers, since this is more likely to produce rich and informative data in which participants' own understandings, meanings and perspectives can emerge within a real-world context. The scope for conducting such observational research online clearly has some limitations, being primarily restricted to observation of ‘linguistic behaviour’. Observation of paralinguistic features, body language and spatial behaviours (e.g. interpersonal proximity) is not readily available online, though as already mentioned emoticons and other paralinguistic substitutes may be informative, as may conversational turn-taking and timings and pauses, in a real-time synchronous conversational context. Further, interaction and navigation within an online ‘virtual environment’ may open up possibilities for observing spatial and other non-linguistic behaviours (e.g. see Givaty et al . 1998 ), though the extent to which this may be considered ‘naturalistic’ observation is debatable. This does not make the approach uninteresting, however, and the implications of the detached embodiment in such settings is a topic worthy of study in itself. Qualitative researchers, however, are coming to view language as of increasing importance in supporting approaches such as ethnomethodological research—the study of shared social meanings—and have developed various techniques (e.g. conversational and discourse analysis) which focus essentially upon the way people use language. The Internet with its wealth of such data thus provides a valuable resource for these forms of qualitative research in which language plays a primary role. As already noted in relation to online interviewing techniques, the Internet creates possibilities for both synchronous and asynchronous forms of verbal interaction, each of which can be useful in online observational research.

Observation of asynchronous online communication is possible using the archives of discussion groups, such as Usenet newsgroups, and listserv mailing lists, as described earlier in relation to online interview approaches. In observational studies very similar techniques can be used, but instead of using these tools to interact with participants and generate data, the researcher may use them to gain access to archived logs of discussions that have taken place. Discussion group archives can be accessed using the technologies and websites already mentioned; for example, listserv archives can be searched at the Catalist website. Bordia ( 1996 ) has made use of discussion archives (on Usenet, Internet and Bitnet) to search for and unobtrusively observe naturalistic rumour transmission, in a way previously not possible using offline approaches (Bordia 1996 ). Susan Herring and colleagues (e.g. Herring et al . 1998 ) have adopted a similar approach, using mailing list discussions to study gender issues in online verbal interactions, though in contrast to Bordia ( 1996 ) these authors appear to have followed and logged discussions as they occurred, rather than searching archives for a specific topic after the discussion had taken place. Sotillo and Wang-Gempp ( 2004 ) report a study in which they downloaded archives of political discussions on a public bulletin board in order to produce a corpus of texts which they then subjected to both quantitative and qualitative analyses. Stegbauer and Rausch ( 2002 ) have observed the behaviour of lurkers in mailing lists.

Observation of online synchronous interaction is possible using online chat, or instant messenger, programs (such as IRC, ICQ, etc.), which can be observed in real time, or by accessing archives of synchronous discussions. In the former approach the researcher will need to create a log of the interaction for subsequent analysis, and this can be done by cutting and pasting the discussion into a text document for storage (Davis et al . 2004 used this approach in their online synchronous interviews), or by making use of recording software which automatically logs and stores the conversation in a data file. Some software programs which support online synchronous chat will incorporate a recording facility (e.g. WebCT). However, currently most do not allow playback in real time, so timing information will be lost. Some researchers have reported successfully using online streamed chat (e.g. IRC) to study online conversations (e.g. Rodino 1997 ). Many IRC programs allow for time-stamping of contributions to the chats.

In both synchronous and asynchronous online observation contexts, the researcher has the option of adopting a participant or non-participant approach. Asynchronous approaches which make use of discussion archives, such as that used by Bordia ( 1996 ), are useful for non-participant observation where the researcher does not wish to be intrusive. Alternatively, a researcher may subscribe to a discussion forum or mailing list, either disclosing their presence or not, and contributing or not to the discussion as they wish. It is possible to ‘lurk’ (observe silently, without revealing one's presence) in asynchronous discussions, but this is not easy in synchronous settings since the presence of all chat forum members is readily available, as their user-name appears in the chat window when they join the discussion. However, the availability of logs of synchronous online chat allows the possibility of a non-participant undisclosed approach in this context, also, though unlike asynchronous conversations many synchronous discussions will not be automatically recorded and archived (a WWW search should locate some that have). For research on lurkers in mailing lists see Stegbauer and Rausch ( 2002 ); for a framework of these and other ‘non-responders’ see Bosnjak ( 2001 ).

A further option for online observational research, as noted above, moves beyond purely text-based approaches: this is made possible by the existence of online virtual environments, known generically as MU environments. These include MUDs (multi-user dungeons, since they started off as places where users played the role-playing game Dungeons and Dragons), MUSEs ‘(multi-user simulated environments)’, MOOs ‘(MUD-object-oriented)’, etc. These online environments support virtual communities who meet up and interact in a virtual world, often based on a fantasy role-playing game of one sort or another. They may often incorporate graphics as well as text, allowing users to wander around in rooms, and interact with objects as well as each other. Observation of behaviour in these environments could give a fascinating insight into role-playing in environments that couldn't possibly be created offline, as well as insight into the way people can entertain multiple alternative identities, for example. The researcher may make use of the existing virtual reality environments available, as a disclosed or undisclosed participant observer (e.g. see the list at: http://uk.dir.yahoo.com/Recreation/Games/Video_Games/Internet_Games/MUDs_MUSHes_MOOs_etc_/ ), or set up their own environment specifically to suit their own research context (which would require some greater level of expertise). The former approach may be considered more naturalistic, though the extent to which behaviour in these types of virtual environments relates to offline behaviour is a research area in its own right.

Document analysis

Document analysis in IMR is similar to some forms of observation, which make use of online archives, but here the records accessed are not logs of interactions but documents which have been purposely placed on the WWW. The Internet provides a massive resource of all sorts of online documents. These include personal home pages with informational or artistic content, theoretical and scientific articles, news articles, stories (fiction or non-fiction), poetry and bibliographies. A fairly recent phenomenon is the emergence of ‘blogs’ (‘weblogs’), which started out as online personal diaries which (strangely enough) individuals post in the public domain, via a website, with the intention that other web users may access and read them. Since blogs are intended to function as regularly updated personal diaries, they are not simply static documents, so the extent to which they constitute data for observational research or document analysis is unclear. Again the continuum between asynchronous and synchronous interactions online raises some new and interesting issues. Based on the earlier definition provided, online personal diaries would probably be classified as documents, since they involve monologues rather than records of interactions between individuals. Having said this, as they have emerged and evolved, blogs are now also becoming used like open bulletin boards. See the Yahoo! list of blogs for examples: http://buzz.yahoo.com/buzz_log/entry/2005/03/08/1300/ . In which case, they may be considered appropriate for linguistic observation research.

Schütz and Machilek ( 2003 ) have carried out research on personal homepages, and provide a useful discussion of the various sampling approaches which may be used to locate these pages. While their focus is on obtaining a representative sample, which may be less relevant to the goals of many qualitative research studies, a number of useful techniques are described, including using directories with which home pages have been registered (e.g. the Yahoo! directory of English language personal home pages: http://dir.yahoo.com/Society_and_Culture/People/ ; the directory of Women Internet Researchers, maintained by Nicola Doring: http://www.nicola-doering.de/women.htm ), sampling via lists of home pages provided by ISPs, and using search engines (such as http://www.google.co.uk ). Qualitative researchers may find the latter approach particularly useful, if for example pages on a specific topic are desired. Whether or not home pages can provide rich and elaborate data of the type typically required by qualitative researchers remains to be further explored; to date, analysis of personal home pages seems to have been carried out largely within a quantitative methodological framework (e.g. Schütz and Machilek 2003 ), though some researchers have also adopted a qualitative approach (e.g. Karlsson 1998 ). Heinz et al . ( 2002 ) report a qualitative study in which they carried out a rhetorical-critical examination of texts and images on a selection of gay, lesbian, bisexual and transgender websites. Sites were chosen based on their popularity and relevance to the research question: global and local constructions of gay identities on WWW sites.

As well as making use of those resources which are already available online, document analysts may choose to solicit documents, such as personal diaries. Hessler et al . ( 2003 ) have taken this approach in a qualitative study on adolescent risk behaviour. These authors report successfully eliciting daily diary entries from participants by email, and thereby generating ‘rich and extensive narratives of everyday life as seen through adolescent eyes’ (Hessler et al . 2003 : 111).

Advantages of online observation and document analysis

There is considerable overlap in the issues affecting online observation and document analysis. Both inherit the general advantages of IMR which have been outlined earlier (cost/time savings, broad geographical reach, ready access to potentially very large pools of data, enhanced disclosure and candidness). Hessler et al . ( 2003 ), for example, note both the cost-effectiveness of their elicitation of online diaries from adolescents, as well as the success in generating more candid responses and establishing better rapport, than has previously been possible in ftf settings, likely due to the nameless, faceless nature of the interaction. The potential for more equitable power relations in the online environment, including facilitation of participation for some marginalized groups, and enhanced possibilities for unobtrusive observation are also particularly relevant.

Ready access to large volumes of data

A key advantage for both observation and document analysis using the Internet is the ready availability of online resources, their ease of accessibility, and the ability to quickly search for and locate topic-relevant sources. In relation to observation studies, this applies to the availability of data trails of interactive communication on the Internet; for document analysis it applies to the enormous range of static documents available (web pages etc., as discussed above). While researchers may need to be carefully selective in determining which of these sources may provide data suitable for a research study, several authors have successfully located and utilized relevant sources using an IMR approach (refer to the examples above). As Bordia ( 1996 ) has noted, the sheer volume of archives of online discussions available creates the potential for gaining easy access to information which may in offline contexts be difficult and time-consuming to obtain. Bordia's success in obtaining transcripts of rumour transmission, in a way that would not have been possible with such ease and efficiency using traditional methods (where the researcher may have to observe numerous conversations before coming across a topic-relevant discussion) illustrates this point well. Ward ( 1999 ) has noted the advantage of the online environment for observational research in allowing extended periods for observation, compared with offline approaches.

Unobtrusiveness

A further advantage of online observation methods in particular, also highlighted by Bordia ( 1996 ), is the enhanced possibilities for unobtrusive observation. Unlike in offline contexts, a researcher can easily lurk (i.e. observe without taking part) in asynchronous communication contexts, including discussion groups and mailing lists, without their presence being known. In synchronous contexts such lurking is still possible, though an individual's presence can be observed (when they log on and enter a chat forum); in large conversational groups this presence may go unnoticed or ignored but in smaller groups it may not and lurking will often attract direct comments requesting at least an introduction. Some researchers report simply not reacting to any such comments, and seem to have successfully gathered records of synchronous text-based interactions by this method (e.g. Al-SaʼDi and Hamdan 2005 ), though this approach could also be expected to provoke a hostile response from chat room members, depending on the setting. Hudson and Bruckman ( 2004 ) provide evidence on this; they found that upon entering IRC chatrooms and posting a message either stating that they were recording participants, or asking people to opt in, or to opt out of being recorded, they were kicked out of the chat room in 63.3 per cent of cases. However, if they simply lurked in the chat room without posting any message (but still recorded participants) they were kicked out on only 29 per cent of occasions. These authors also note that only a tiny proportion of chat room participants consented to being recorded when asked to opt in, and thus conclude that obtaining consent in this type of real time online observational research is impracticable. However, whether lurking and recording participants without obtaining consent meets appropriate ethical standards is open to debate. Nevertheless, the contexts which the Internet offers do potentially expand the possibilities for carrying out undisclosed observational research.

Power relationships

The aforementioned features of anonymity and enhanced participant control in online communication, which may serve to provide more balanced power relations between interviewer and interviewee (as well as amongst interviewees themselves), may also extend to power relations between participants in non-specifically research-oriented online contexts, such as those drawn upon in observation research, as described above (i.e. chat forums, etc.). Indeed, the effect of the online communication medium on power relationships is an interesting area for study. While some authors have claimed a ‘democratizing’ effect in Internet interaction, due to the lack of readily available cues about gender, race, socio-economic status, and so on, various strands of evidence have challenged this view (for a thorough and informed discussion of this issue see Mann and Stewart [ 2000 ], Chapter 7 ). For one thing, online researchers have found that people will direct probing questions where such biosocial and demographic information is not provided (e.g. Ward 1999 ), as well as make assumptions about the personal characteristics of those they meet online (Kendall 1999 ). The language a person uses may give clues about themselves; there is evidence that characteristic ‘gendered’ communication styles are transported to online settings (e.g. Herring 1993 ; Postmes and Spears 2002 ). Further, discriminatory and oppressive behaviours can occur online as well as offline, and perhaps more so, given the possible disinhibiting effects of the online medium, mentioned earlier. For example, women have reported experiencing sexual harassment by men in online interactions where their gender was disclosed (Mann and Stewart 2000 ). Thus Internet-based communication may be far from achieving the democratizing effects which some have claimed. However, perhaps online contexts can offer greater scope for reducing some of the biases often found in ftf interactions.

Disadvantages of online observation and document analysis

The general disadvantages of IMR outlined earlier also apply to observation and document analysis (participation restrictions due to the nature of the IUP and technological requirements; lack of researcher control; difficulty in implementing ethical procedures). Those issues highlighted as especially pertinent to online interactive communication contexts also affect observational approaches (potentially greater ambiguity, poorer relational development and more impoverished data due to a lack of paralinguistic cues). In considering the relative merits of observing interactions in asynchronous or synchronous online contexts, many of the same points discussed above in relation to online interviews apply. However, some of the ethical considerations relevant to observation and document analysis in IMR differ slightly from those which have already been highlighted in relation to online interviewing, as now discussed.

Ethical issues relevant to online observation and document analysis are well-covered in Ess (Chapter 31 this volume). The key issues include ambiguity over the public/private domain distinction in online contexts; anonymity, protecting participant identity and the use of online pseudonyms; and copyright and the appropriate attribution of authorship for online sources. The present discussion is brief, and the reader is referred to the aforementioned well-informed account, as well as other available more detailed discussions of these issues (e.g. Kraut et al . 2004 , and others mentioned earlier). In relation to the distinction between the public and private domain, there has been some disagreement concerning the extent to which researchers should feel free to utilize online discussion archives, or log synchronous chat room conversations. Earlier, the possibilities for undisclosed observation via lurking were noted, but it has been unclear in which contexts this does not constitute an invasion of privacy. ‘Private’ online chat discussions are quite clearly out of bounds for the observational researcher (who would have difficulty accessing these anyway) without first gaining informed consent. This is certainly possible; Al-SaʼDi and Hamdan ( 2005 ) have reported using online synchronous chat logs provided by consenting participants who had already recorded their own private conversations. In other settings, however, appropriate standards are less clear. Several authors have considered materials, including logs of online discussions, made available online as in the public domain (e.g. Bordia 1996 ). A stricter view would advocate that informed consent is required for use of any such archives. However, gaining such consent raises practical problems since tracing participants who have taken part in archived online discussions can be difficult: users may leave a discussion group, email addresses may change, be left unchecked, and so on. Of course, asking for participation consent prior to observing an online real-time conversation may have obvious implications for the naturalness of the subsequent discussion. In the absence of a consistent and officially recognized set of guidelines, currently, researchers will need to make their own judgements on these issues.

While protecting participants' identities by the use of pseudonyms may be considered a sufficient procedure in addressing concerns about whether to, for example, publish individuals' comments in research reports, the issue emerges of the extent to which online identities can be considered anonymous. Some authors have argued that individuals often invest a lot into their online personas, and that these can reveal personal information about their offline identities; thus these online identities should be protected in the same way as offline identities (Frankel and Siang 1999 ). Copyright issues also emerge (Bowker and Tuffin 2004 ). Researchers may be obliged to cite the author of a text, yet this may compromise anonymity (and potentially cause harm, even if the author of the text agreed to or requested the citation). Confidentiality may be jeopardized in IMR, since it may be relatively easy to locate the author of an anonymous quote using Internet search facilities, compared with offline settings (Bowker and Tuffin 2004 ). Because of these issues surrounding anonymity, confidentiality and privacy in online observation research, some researchers have chosen to use online interviews, in which informed consent can more easily be obtained (e.g. Bowker and Tuffin 2004 ). In cases where documents are solicited, gaining informed consent resolves the public/private domain issue. As noted, Hessler et al . ( 2003 ) used solicited documents in a study in which adolescents were asked to send daily diary entries via email. In this study informed consent was clearly required, and careful measures were taken to protect identity and data security as far as was practically possible.

Mixed methods possibilities in IMR

In the social sciences generally researchers are becoming increasingly aware of the possibilities and benefits of a mixed methods research strategy; that is, one which uses both quantitative and qualitative methodologies within the same study in order to address a single research question (Hewson 2006 ). This approach is facilitated by the recognition that the long-standing perceived division between qualitative and quantitative research approaches, and the belief that they are incompatible, is not useful (e.g. Johnson and Onwuegbuzie 2004 ; Eid and Diener 2006). The mixed method strategy is underpinned by a pragmatist philosophical position, which denies the incompatibility of qualitative and quantitative approaches, and urges recognition that both approaches have their own strengths and weaknesses and may be usefully combined in informing a single thread of enquiry. An important concept associated with mixed methods approaches is ‘triangulation’, which refers to the strategy of exploring an issue from several angles in order to cross-validate and corroborate different strands of evidence. In this way a more comprehensive and complete account may be possible. The possibilities, merits and drawbacks of mixed methods approaches are now becoming widely discussed (e.g. Johnson and Onwuegbuzie 2004 ), though some authors remain opposed to this orientation on philosophical grounds (see Smith [ 1983 ] for further discussion of the compatibility or otherwise of quantitative and qualitative approaches). Nevertheless, there is no doubt that mixed methods approaches are becoming more and more widely recognized and used by social science researchers, as is apparent from both the increasing number of published research reports adopting this methodology, and the appearance of chapters and books devoted to the approach in key research methodology texts (e.g. Creswell 2003 ; Eid and Diener 2006). For a detailed and well-informed account of mixed methodology approaches see Tashakkori and Teddlie ( 1998 ). Tashakkori and Teddlie have noted the distinction between mixed methods approaches, which mix qualitative and quantitative approaches in the methodology of a study, and mixed model approaches, which combine both approaches across all stages of the research process. The present focus is on mixed methods research in IMR, since it is the use of the Internet as a data-gathering tool that is under consideration. Mixed mode approaches, in which data is gathered via two or more modes (e.g. via both a computer terminal, and pen and paper questionnaire), will also be briefly considered since these approaches have been useful in IMR.

Internet-mediated mixed methods studies

Mann and Stewart ( 2000 ) provide some discussion of the potential for implementing mixed methods approaches in IMR, referring to examples of research studies which have adopted the approach in an IMR medium. Mixed methods research takes place in one of two major modes: sequential or concurrent (Creswell 2003 12 ). An example of a sequential approach would be administration of (qualitative) semi-structured interviews, the results of which provide a basis for constructing a follow-up (quantitative) structured questionnaire. Concurrent approaches may involve, for example, collecting both behavioural and self-report data simultaneously, such as during a problem-solving task. Within these designs, the respective (quantitative and qualitative) methods can be given greater or lesser importance, depending on the researcher's orientation and goals.

Several authors have implemented sequential mixed methods approaches in IMR. Madge and OʼConnor ( 2002 ) used a combination of online methods, including a web-based survey 13 and synchronous online interviews, in their study looking at new parents’ experiences and reasons for using the website babyworld.co.uk. The survey was posted on the website and follow-up interviews were conducted with respondents who volunteered to take part, after receiving an email request sent to them upon completion of the survey. Fifteen out of 155 survey respondents volunteered to take part in group interviews. Herring's research (e.g. Herring 1993 ; Herring et al . 1998 ) on online gendered communication has also made use of mixed methods approaches. For example, several months after carrying out observation of postings to a mailing list discussion thread, an open-ended survey was posted to the list in order to compare participants perceptions' of what had occurred during the discussion thread with what had been observed by the researchers (Herring et al . 1998 ). Interestingly, the two accounts were quite discrepant, 14 illustrating the value of this type of mixed methods approach. Kendall ( 1999 ) has also pointed out the advantage of being able to readily combine observations of actual behaviour with self-report data in an IMR context, having made use of this approach using ftf interviews, group participation sessions and information gathered via newsgroups and mailing lists. Simultaneous online mixed methods research has also been carried out. Ward's ( 1999 ) ‘cyber-ethnographic’ study of online communities made use of both observation and semi-structured interview approaches, carried out simultaneously over an extended period, using BBS and email. Ward reports the approach as successful in being able to generate a rich understanding of these online communities' perceptions and experiences.

A number of mixed mode studies have been conducted in IMR, largely with the purpose of validating Internet-mediated research procedures, though they may also be seen as serving to validate offline approaches, e.g. by testing the robustness of psychological test instruments with different samples, or in different participation contexts (Hewson and Charlton 2005 ). Buchanan (Chapter 28 this volume) discusses such approaches; see also the collections in Reips and Bosjnak ( 2001 ) and Birnbaum ( 2000 ). Such mixed mode approaches typically compare Internet versus laboratory or pen and paper implementations of the same survey, questionnaire or experimental procedure. Other mixed mode studies, which have not explicitly focused on the goal of validiating online and offline methods, can also be useful. For example, Workman ( 1992 ) reports how he used electronic media to support participant observation fieldwork; while the electronic media did not replace traditional methods, it was useful for scheduling meetings, and gaining additional information from e.g. BBSs and mailing lists. Davis et al . ( 2004 ), though recognizing the limitations of their data generated via online synchronous chat interviews (due mainly to problems with ambiguity, and a lack of deep, detailed descriptions), suggested that such approaches could be useful when combined with other offline methods (e.g. ftf interviews, which were also used in the same study). Mixed mode studies may also be useful due to the diverse and readily available pool of potential participants accessible via online methods, e.g. psychological research has traditionally relied on access to psychology undergraduates as a participant pool, who tend to consist largely of females. The often-cited bias towards males in online contexts 15 may thus be useful in serving to redress this gender imbalance, by using a mixed mode approach which samples by both traditional and online methods (Hewson 2003 ).

Advantages of using the internet in mixed methods research

A number of advantages of using online methods to facilitate mixed methods research seem apparent, including the general advantages of IMR approaches already noted above. Of particular relevance to mixed methods approaches, which tend to involve lengthy and costly data collection procedures, is the digitizing of data which occurs in IMR, thus rendering it in a format which can be readily transported between different data management software packages, including both those designed for qualitative and quantitative forms of analysis (Mann and Stewart 2000 ). As well as the cost- and time-saving benefits to the researcher, online mixed methods approaches can reduce demands on participants, due to the greater control offered over when and where to respond. Of course, mixed methods approaches will inherit all the advantages and disadvantages of the respective data-gathering methods they employ, as discussed throughout this chapter. Another key advantage of mixed methods approaches in IMR is that, given the strengths and weaknesses of different online approaches, e.g. synchronous and asynchronous interviews, a strategy in which several approaches are utilized together in a single study may provide an overall richer exploration of a research topic, by combining the strengths of the respective approaches. Thus, in these early days of IMR, where the relative advantages and disadvantages of the various procedures available are still being uncovered (e.g. Reips 2002 ), and where trade-offs are clearly apparent in making choices about which approach to use, it would seem sensible to suggest making use of more than one approach. Especially given the particularly salient feature of Internet-mediated data gathering approaches that they typically lead to substantial time and cost savings.

In summary, Internet-mediated research is still young, and mixed methods approaches just coming to fruition and widespread recognition; as more researchers join those pioneers of IMR mixed methods research mentioned here, the benefits and drawbacks of marrying these two approaches will become more apparent.

Conclusions

This chapter has outlined the range of possibilities for conducting qualitative interview, observation and document analysis data-gathering procedures in an Internet-mediated research context. The range of approaches, tools and technologies for supporting such research has been considered, as well as the advantages and disadvantages incurred in using the different techniques. Finally, it has been argued that mixed methods approaches are valuable, and that these may benefit particularly from being adapted to an IMR environment, since the cost and time savings that IMR affords may be especially useful in supporting research which is typically high in resource demands. Further exploration of mixed methods IMR is now an important research avenue.

Acknowledgements

The author would like to thank Immo Fritsche and Zak Birchmeier for providing very helpful, thorough and detailed comments in reviewing an earlier draft ofthis chapter. Also Ulf-Dietrich Reips whose guidance and feedback on earlier versions has been extremely valuable.

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Appendix A: Listserv Primer

You will need: an Internet connection, and an email client or web browser. The email method is described in Hewson et al . ( 2003 ); send an email to [email protected], leaving the subject line blank and writing ‘list global’ in the body. You will get back an email stating that there are around 48,000 (at the time of writing) listserv lists available, and that you should narrow your search (using e.g. ‘list global punk’). Alternatively, using a web browser access the Catalist interface available at: http://www.lsoft.com/catalist.html . Using the latter method, a search for ‘punk’ in the list title will return around three hits. Email searches will return a similar number of hits, but the two methods will not always return identical results. Subscription information is provided either in the returned email, or by following the list link on the Catalist webpage. Many lists have a web archive interface which allows a direct search of messages in that list. Let's suppose we wanted to find out more about ‘panpsychism’ and thought that a consciousness search result: [email protected] might contain postings on this topic. Via the web interface, searching all postings from February 1993 to November 2004, eight matches are returned (search conducted 23 February 2005).

Tucows provides information about a large range of software resources: searching the website for ‘IRC clients’ should bring up a list of those currently available.

At the time of writing, WebCT is in the process of merging with what has to date been the other main commercially available VLE—Blackboard—with plans to continue trading under the Blackboard brand name (see: http://www.blackboard.com/webct/merger/index , retrieved 6 February 2006).

‘Bandwidth’ refers to the amount of data that can be transferred in a fixed time, usually measured as bytes (or kilobytes, or megabytes) per second. Broadband is becoming increasingly widespread, compared to the previously common dial-up connection, which uses a standard phone line and has comparatively limited bandwidth. Broadband companies are now offering as standard bandwidths up to 2 megabytes/s, though the typical UK broadband user is likely to have a bandwidth nearer 512 kilobytes/s (costing from around £15 per month). Cable Internet access can offer even higher bandwidths, up to as much as 24 megabytes/s.

Though it should be noted that several factors may delay or prevent entirely emails from reaching the intended recipient, including spam filters and DNS resolve delay.

‘Listserv’ was originally developed in the mid 1980s for the Bitnet computer network, but the term has now come to be used to refer to any software application which manages mailing lists ( http://www.answers.com ).

Researchers should always be mindful of ‘Netiquette’: the rules and conventions which govern what is considered acceptable behaviour online, when undertaking any IMR study. Respecting these principles is essential, not only in protecting the reputation of an individual researcher or research group, but the wider academic research community in general. As well, of course, as protecting potential participants. For a summary of the key principles of Netiquette, see http://www.albion.com/netiquette/ .

BBSs have been around since the 1970s, and started off as a single computer connected to a phone line via a modem, running appropriate software which allowed other computers to dial-up and connect via their modem/phone line ( http://www.bbsdocumentary.com/ ). Setting up a dedicated BBS does require some level of expertise, and with the advent of the Internet the alternative methods now available for creating online discussion forums will probably prove more efficient and accessible to the majority of users.

A key difference between mailing lists and BBSs and newsgroups, despite their similar functions as discussion forums, is that mailing lists deliver messages to individual subscribers’ mailboxes, whereas messages posted to BBSs and newsgroups are stored on a news server (or computer) where they can then be accessed by subscribers as and when they chose. Thus BBSs/newsgroups may be considered less imposing than mailing lists, which could be seen to have implications (though not necessarily) for different levels of commitment, interest and engagement in the discussion topic by subscribers of these two related technologies.

Most Internet Service Providers (ISPs) will provide access to a news server as part of their package.

The meaning of conversational ‘flow’ can vary depending upon context and focus. Here the term probably most aptly refers to the topics covered, links drawn between sub-themes, and the timescale of the interview dialogue. Other relevant usages of the term ‘flow’ will be described at relevant points in the chapter.

Though this would not be practicable using synchronous approaches, due to the issue of different time zones.

Creswell also refers to ‘transformational’ mixed methods designs.

Which can be viewed at http://www.geog.le.ac.uk/baby/babyworldform.asp (accessed 19 February 2005).

Although contrary to what had actually occurred, Herring found men perceived women as having dominated the discussion; in actual fact men had contributed more than women both in terms of length and number of postings (Herring et al . 1998 ).

It is likely that this gender bias may be decreasing, in relation to the demographics of the entire IUP at least, though of course different sampling approaches may still be more or less likely to recruit either males or females.

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a method of research that produces descriptive (non-numerical) data, such as observations of behavior or personal accounts of experiences. The goal of gathering this qualitative data is to examine how individuals can perceive the world from different vantage points. A variety of techniques are subsumed under qualitative research, including content analyses of narratives, in-depth interviews, focus groups, participant observation, and case studies, often conducted in naturalistic settings.

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What is Qualitative in Qualitative Research

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• Volume 42 , pages 139–160, ( 2019 )

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What is qualitative research? If we look for a precise definition of qualitative research, and specifically for one that addresses its distinctive feature of being “qualitative,” the literature is meager. In this article we systematically search, identify and analyze a sample of 89 sources using or attempting to define the term “qualitative.” Then, drawing on ideas we find scattered across existing work, and based on Becker’s classic study of marijuana consumption, we formulate and illustrate a definition that tries to capture its core elements. We define qualitative research as an iterative process in which improved understanding to the scientific community is achieved by making new significant distinctions resulting from getting closer to the phenomenon studied. This formulation is developed as a tool to help improve research designs while stressing that a qualitative dimension is present in quantitative work as well. Additionally, it can facilitate teaching, communication between researchers, diminish the gap between qualitative and quantitative researchers, help to address critiques of qualitative methods, and be used as a standard of evaluation of qualitative research.

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If we assume that there is something called qualitative research, what exactly is this qualitative feature? And how could we evaluate qualitative research as good or not? Is it fundamentally different from quantitative research? In practice, most active qualitative researchers working with empirical material intuitively know what is involved in doing qualitative research, yet perhaps surprisingly, a clear definition addressing its key feature is still missing.

To address the question of what is qualitative we turn to the accounts of “qualitative research” in textbooks and also in empirical work. In his classic, explorative, interview study of deviance Howard Becker ( 1963 ) asks ‘How does one become a marijuana user?’ In contrast to pre-dispositional and psychological-individualistic theories of deviant behavior, Becker’s inherently social explanation contends that becoming a user of this substance is the result of a three-phase sequential learning process. First, potential users need to learn how to smoke it properly to produce the “correct” effects. If not, they are likely to stop experimenting with it. Second, they need to discover the effects associated with it; in other words, to get “high,” individuals not only have to experience what the drug does, but also to become aware that those sensations are related to using it. Third, they require learning to savor the feelings related to its consumption – to develop an acquired taste. Becker, who played music himself, gets close to the phenomenon by observing, taking part, and by talking to people consuming the drug: “half of the fifty interviews were conducted with musicians, the other half covered a wide range of people, including laborers, machinists, and people in the professions” (Becker 1963 :56).

Another central aspect derived through the common-to-all-research interplay between induction and deduction (Becker 2017 ), is that during the course of his research Becker adds scientifically meaningful new distinctions in the form of three phases—distinctions, or findings if you will, that strongly affect the course of his research: its focus, the material that he collects, and which eventually impact his findings. Each phase typically unfolds through social interaction, and often with input from experienced users in “a sequence of social experiences during which the person acquires a conception of the meaning of the behavior, and perceptions and judgments of objects and situations, all of which make the activity possible and desirable” (Becker 1963 :235). In this study the increased understanding of smoking dope is a result of a combination of the meaning of the actors, and the conceptual distinctions that Becker introduces based on the views expressed by his respondents. Understanding is the result of research and is due to an iterative process in which data, concepts and evidence are connected with one another (Becker 2017 ).

Indeed, there are many definitions of qualitative research, but if we look for a definition that addresses its distinctive feature of being “qualitative,” the literature across the broad field of social science is meager. The main reason behind this article lies in the paradox, which, to put it bluntly, is that researchers act as if they know what it is, but they cannot formulate a coherent definition. Sociologists and others will of course continue to conduct good studies that show the relevance and value of qualitative research addressing scientific and practical problems in society. However, our paper is grounded in the idea that providing a clear definition will help us improve the work that we do. Among researchers who practice qualitative research there is clearly much knowledge. We suggest that a definition makes this knowledge more explicit. If the first rationale for writing this paper refers to the “internal” aim of improving qualitative research, the second refers to the increased “external” pressure that especially many qualitative researchers feel; pressure that comes both from society as well as from other scientific approaches. There is a strong core in qualitative research, and leading researchers tend to agree on what it is and how it is done. Our critique is not directed at the practice of qualitative research, but we do claim that the type of systematic work we do has not yet been done, and that it is useful to improve the field and its status in relation to quantitative research.

The literature on the “internal” aim of improving, or at least clarifying qualitative research is large, and we do not claim to be the first to notice the vagueness of the term “qualitative” (Strauss and Corbin 1998 ). Also, others have noted that there is no single definition of it (Long and Godfrey 2004 :182), that there are many different views on qualitative research (Denzin and Lincoln 2003 :11; Jovanović 2011 :3), and that more generally, we need to define its meaning (Best 2004 :54). Strauss and Corbin ( 1998 ), for example, as well as Nelson et al. (1992:2 cited in Denzin and Lincoln 2003 :11), and Flick ( 2007 :ix–x), have recognized that the term is problematic: “Actually, the term ‘qualitative research’ is confusing because it can mean different things to different people” (Strauss and Corbin 1998 :10–11). Hammersley has discussed the possibility of addressing the problem, but states that “the task of providing an account of the distinctive features of qualitative research is far from straightforward” ( 2013 :2). This confusion, as he has recently further argued (Hammersley 2018 ), is also salient in relation to ethnography where different philosophical and methodological approaches lead to a lack of agreement about what it means.

Others (e.g. Hammersley 2018 ; Fine and Hancock 2017 ) have also identified the treat to qualitative research that comes from external forces, seen from the point of view of “qualitative research.” This threat can be further divided into that which comes from inside academia, such as the critique voiced by “quantitative research” and outside of academia, including, for example, New Public Management. Hammersley ( 2018 ), zooming in on one type of qualitative research, ethnography, has argued that it is under treat. Similarly to Fine ( 2003 ), and before him Gans ( 1999 ), he writes that ethnography’ has acquired a range of meanings, and comes in many different versions, these often reflecting sharply divergent epistemological orientations. And already more than twenty years ago while reviewing Denzin and Lincoln’ s Handbook of Qualitative Methods Fine argued:

While this increasing centrality [of qualitative research] might lead one to believe that consensual standards have developed, this belief would be misleading. As the methodology becomes more widely accepted, querulous challengers have raised fundamental questions that collectively have undercut the traditional models of how qualitative research is to be fashioned and presented (1995:417).

According to Hammersley, there are today “serious treats to the practice of ethnographic work, on almost any definition” ( 2018 :1). He lists five external treats: (1) that social research must be accountable and able to show its impact on society; (2) the current emphasis on “big data” and the emphasis on quantitative data and evidence; (3) the labor market pressure in academia that leaves less time for fieldwork (see also Fine and Hancock 2017 ); (4) problems of access to fields; and (5) the increased ethical scrutiny of projects, to which ethnography is particularly exposed. Hammersley discusses some more or less insufficient existing definitions of ethnography.

The current situation, as Hammersley and others note—and in relation not only to ethnography but also qualitative research in general, and as our empirical study shows—is not just unsatisfactory, it may even be harmful for the entire field of qualitative research, and does not help social science at large. We suggest that the lack of clarity of qualitative research is a real problem that must be addressed.

Towards a Definition of Qualitative Research

Seen in an historical light, what is today called qualitative, or sometimes ethnographic, interpretative research – or a number of other terms – has more or less always existed. At the time the founders of sociology – Simmel, Weber, Durkheim and, before them, Marx – were writing, and during the era of the Methodenstreit (“dispute about methods”) in which the German historical school emphasized scientific methods (cf. Swedberg 1990 ), we can at least speak of qualitative forerunners.

Perhaps the most extended discussion of what later became known as qualitative methods in a classic work is Bronisław Malinowski’s ( 1922 ) Argonauts in the Western Pacific , although even this study does not explicitly address the meaning of “qualitative.” In Weber’s ([1921–-22] 1978) work we find a tension between scientific explanations that are based on observation and quantification and interpretative research (see also Lazarsfeld and Barton 1982 ).

If we look through major sociology journals like the American Sociological Review , American Journal of Sociology , or Social Forces we will not find the term qualitative sociology before the 1970s. And certainly before then much of what we consider qualitative classics in sociology, like Becker’ study ( 1963 ), had already been produced. Indeed, the Chicago School often combined qualitative and quantitative data within the same study (Fine 1995 ). Our point being that before a disciplinary self-awareness the term quantitative preceded qualitative, and the articulation of the former was a political move to claim scientific status (Denzin and Lincoln 2005 ). In the US the World War II seem to have sparked a critique of sociological work, including “qualitative work,” that did not follow the scientific canon (Rawls 2018 ), which was underpinned by a scientifically oriented and value free philosophy of science. As a result the attempts and practice of integrating qualitative and quantitative sociology at Chicago lost ground to sociology that was more oriented to surveys and quantitative work at Columbia under Merton-Lazarsfeld. The quantitative tradition was also able to present textbooks (Lundberg 1951 ) that facilitated the use this approach and its “methods.” The practices of the qualitative tradition, by and large, remained tacit or was part of the mentoring transferred from the renowned masters to their students.

This glimpse into history leads us back to the lack of a coherent account condensed in a definition of qualitative research. Many of the attempts to define the term do not meet the requirements of a proper definition: A definition should be clear, avoid tautology, demarcate its domain in relation to the environment, and ideally only use words in its definiens that themselves are not in need of definition (Hempel 1966 ). A definition can enhance precision and thus clarity by identifying the core of the phenomenon. Preferably, a definition should be short. The typical definition we have found, however, is an ostensive definition, which indicates what qualitative research is about without informing us about what it actually is :

Qualitative research is multimethod in focus, involving an interpretative, naturalistic approach to its subject matter. This means that qualitative researchers study things in their natural settings, attempting to make sense of, or interpret, phenomena in terms of the meanings people bring to them. Qualitative research involves the studied use and collection of a variety of empirical materials – case study, personal experience, introspective, life story, interview, observational, historical, interactional, and visual texts – that describe routine and problematic moments and meanings in individuals’ lives. (Denzin and Lincoln 2005 :2)

Flick claims that the label “qualitative research” is indeed used as an umbrella for a number of approaches ( 2007 :2–4; 2002 :6), and it is not difficult to identify research fitting this designation. Moreover, whatever it is, it has grown dramatically over the past five decades. In addition, courses have been developed, methods have flourished, arguments about its future have been advanced (for example, Denzin and Lincoln 1994) and criticized (for example, Snow and Morrill 1995 ), and dedicated journals and books have mushroomed. Most social scientists have a clear idea of research and how it differs from journalism, politics and other activities. But the question of what is qualitative in qualitative research is either eluded or eschewed.

We maintain that this lacuna hinders systematic knowledge production based on qualitative research. Paul Lazarsfeld noted the lack of “codification” as early as 1955 when he reviewed 100 qualitative studies in order to offer a codification of the practices (Lazarsfeld and Barton 1982 :239). Since then many texts on “qualitative research” and its methods have been published, including recent attempts (Goertz and Mahoney 2012 ) similar to Lazarsfeld’s. These studies have tried to extract what is qualitative by looking at the large number of empirical “qualitative” studies. Our novel strategy complements these endeavors by taking another approach and looking at the attempts to codify these practices in the form of a definition, as well as to a minor extent take Becker’s study as an exemplar of what qualitative researchers actually do, and what the characteristic of being ‘qualitative’ denotes and implies. We claim that qualitative researchers, if there is such a thing as “qualitative research,” should be able to codify their practices in a condensed, yet general way expressed in language.

Lingering problems of “generalizability” and “how many cases do I need” (Small 2009 ) are blocking advancement – in this line of work qualitative approaches are said to differ considerably from quantitative ones, while some of the former unsuccessfully mimic principles related to the latter (Small 2009 ). Additionally, quantitative researchers sometimes unfairly criticize the first based on their own quality criteria. Scholars like Goertz and Mahoney ( 2012 ) have successfully focused on the different norms and practices beyond what they argue are essentially two different cultures: those working with either qualitative or quantitative methods. Instead, similarly to Becker ( 2017 ) who has recently questioned the usefulness of the distinction between qualitative and quantitative research, we focus on similarities.

The current situation also impedes both students and researchers in focusing their studies and understanding each other’s work (Lazarsfeld and Barton 1982 :239). A third consequence is providing an opening for critiques by scholars operating within different traditions (Valsiner 2000 :101). A fourth issue is that the “implicit use of methods in qualitative research makes the field far less standardized than the quantitative paradigm” (Goertz and Mahoney 2012 :9). Relatedly, the National Science Foundation in the US organized two workshops in 2004 and 2005 to address the scientific foundations of qualitative research involving strategies to improve it and to develop standards of evaluation in qualitative research. However, a specific focus on its distinguishing feature of being “qualitative” while being implicitly acknowledged, was discussed only briefly (for example, Best 2004 ).

In 2014 a theme issue was published in this journal on “Methods, Materials, and Meanings: Designing Cultural Analysis,” discussing central issues in (cultural) qualitative research (Berezin 2014 ; Biernacki 2014 ; Glaeser 2014 ; Lamont and Swidler 2014 ; Spillman 2014). We agree with many of the arguments put forward, such as the risk of methodological tribalism, and that we should not waste energy on debating methods separated from research questions. Nonetheless, a clarification of the relation to what is called “quantitative research” is of outmost importance to avoid misunderstandings and misguided debates between “qualitative” and “quantitative” researchers. Our strategy means that researchers, “qualitative” or “quantitative” they may be, in their actual practice may combine qualitative work and quantitative work.

In this article we accomplish three tasks. First, we systematically survey the literature for meanings of qualitative research by looking at how researchers have defined it. Drawing upon existing knowledge we find that the different meanings and ideas of qualitative research are not yet coherently integrated into one satisfactory definition. Next, we advance our contribution by offering a definition of qualitative research and illustrate its meaning and use partially by expanding on the brief example introduced earlier related to Becker’s work ( 1963 ). We offer a systematic analysis of central themes of what researchers consider to be the core of “qualitative,” regardless of style of work. These themes – which we summarize in terms of four keywords: distinction, process, closeness, improved understanding – constitute part of our literature review, in which each one appears, sometimes with others, but never all in the same definition. They serve as the foundation of our contribution. Our categories are overlapping. Their use is primarily to organize the large amount of definitions we have identified and analyzed, and not necessarily to draw a clear distinction between them. Finally, we continue the elaboration discussed above on the advantages of a clear definition of qualitative research.

In a hermeneutic fashion we propose that there is something meaningful that deserves to be labelled “qualitative research” (Gadamer 1990 ). To approach the question “What is qualitative in qualitative research?” we have surveyed the literature. In conducting our survey we first traced the word’s etymology in dictionaries, encyclopedias, handbooks of the social sciences and of methods and textbooks, mainly in English, which is common to methodology courses. It should be noted that we have zoomed in on sociology and its literature. This discipline has been the site of the largest debate and development of methods that can be called “qualitative,” which suggests that this field should be examined in great detail.

In an ideal situation we should expect that one good definition, or at least some common ideas, would have emerged over the years. This common core of qualitative research should be so accepted that it would appear in at least some textbooks. Since this is not what we found, we decided to pursue an inductive approach to capture maximal variation in the field of qualitative research; we searched in a selection of handbooks, textbooks, book chapters, and books, to which we added the analysis of journal articles. Our sample comprises a total of 89 references.

In practice we focused on the discipline that has had a clear discussion of methods, namely sociology. We also conducted a broad search in the JSTOR database to identify scholarly sociology articles published between 1998 and 2017 in English with a focus on defining or explaining qualitative research. We specifically zoom in on this time frame because we would have expect that this more mature period would have produced clear discussions on the meaning of qualitative research. To find these articles we combined a number of keywords to search the content and/or the title: qualitative (which was always included), definition, empirical, research, methodology, studies, fieldwork, interview and observation .

As a second phase of our research we searched within nine major sociological journals ( American Journal of Sociology , Sociological Theory , American Sociological Review , Contemporary Sociology , Sociological Forum , Sociological Theory , Qualitative Research , Qualitative Sociology and Qualitative Sociology Review ) for articles also published during the past 19 years (1998–2017) that had the term “qualitative” in the title and attempted to define qualitative research.

Lastly we picked two additional journals, Qualitative Research and Qualitative Sociology , in which we could expect to find texts addressing the notion of “qualitative.” From Qualitative Research we chose Volume 14, Issue 6, December 2014, and from Qualitative Sociology we chose Volume 36, Issue 2, June 2017. Within each of these we selected the first article; then we picked the second article of three prior issues. Again we went back another three issues and investigated article number three. Finally we went back another three issues and perused article number four. This selection criteria was used to get a manageable sample for the analysis.

The coding process of the 89 references we gathered in our selected review began soon after the first round of material was gathered, and we reduced the complexity created by our maximum variation sampling (Snow and Anderson 1993 :22) to four different categories within which questions on the nature and properties of qualitative research were discussed. We call them: Qualitative and Quantitative Research, Qualitative Research, Fieldwork, and Grounded Theory. This – which may appear as an illogical grouping – merely reflects the “context” in which the matter of “qualitative” is discussed. If the selection process of the material – books and articles – was informed by pre-knowledge, we used an inductive strategy to code the material. When studying our material, we identified four central notions related to “qualitative” that appear in various combinations in the literature which indicate what is the core of qualitative research. We have labeled them: “distinctions”, “process,” “closeness,” and “improved understanding.” During the research process the categories and notions were improved, refined, changed, and reordered. The coding ended when a sense of saturation in the material arose. In the presentation below all quotations and references come from our empirical material of texts on qualitative research.

Analysis – What is Qualitative Research?

In this section we describe the four categories we identified in the coding, how they differently discuss qualitative research, as well as their overall content. Some salient quotations are selected to represent the type of text sorted under each of the four categories. What we present are examples from the literature.

Qualitative and Quantitative

This analytic category comprises quotations comparing qualitative and quantitative research, a distinction that is frequently used (Brown 2010 :231); in effect this is a conceptual pair that structures the discussion and that may be associated with opposing interests. While the general goal of quantitative and qualitative research is the same – to understand the world better – their methodologies and focus in certain respects differ substantially (Becker 1966 :55). Quantity refers to that property of something that can be determined by measurement. In a dictionary of Statistics and Methodology we find that “(a) When referring to *variables, ‘qualitative’ is another term for *categorical or *nominal. (b) When speaking of kinds of research, ‘qualitative’ refers to studies of subjects that are hard to quantify, such as art history. Qualitative research tends to be a residual category for almost any kind of non-quantitative research” (Stiles 1998:183). But it should be obvious that one could employ a quantitative approach when studying, for example, art history.

The same dictionary states that quantitative is “said of variables or research that can be handled numerically, usually (too sharply) contrasted with *qualitative variables and research” (Stiles 1998:184). From a qualitative perspective “quantitative research” is about numbers and counting, and from a quantitative perspective qualitative research is everything that is not about numbers. But this does not say much about what is “qualitative.” If we turn to encyclopedias we find that in the 1932 edition of the Encyclopedia of the Social Sciences there is no mention of “qualitative.” In the Encyclopedia from 1968 we can read:

Qualitative Analysis. For methods of obtaining, analyzing, and describing data, see [the various entries:] CONTENT ANALYSIS; COUNTED DATA; EVALUATION RESEARCH, FIELD WORK; GRAPHIC PRESENTATION; HISTORIOGRAPHY, especially the article on THE RHETORIC OF HISTORY; INTERVIEWING; OBSERVATION; PERSONALITY MEASUREMENT; PROJECTIVE METHODS; PSYCHOANALYSIS, article on EXPERIMENTAL METHODS; SURVEY ANALYSIS, TABULAR PRESENTATION; TYPOLOGIES. (Vol. 13:225)

Some, like Alford, divide researchers into methodologists or, in his words, “quantitative and qualitative specialists” (Alford 1998 :12). Qualitative research uses a variety of methods, such as intensive interviews or in-depth analysis of historical materials, and it is concerned with a comprehensive account of some event or unit (King et al. 1994 :4). Like quantitative research it can be utilized to study a variety of issues, but it tends to focus on meanings and motivations that underlie cultural symbols, personal experiences, phenomena and detailed understanding of processes in the social world. In short, qualitative research centers on understanding processes, experiences, and the meanings people assign to things (Kalof et al. 2008 :79).

Others simply say that qualitative methods are inherently unscientific (Jovanović 2011 :19). Hood, for instance, argues that words are intrinsically less precise than numbers, and that they are therefore more prone to subjective analysis, leading to biased results (Hood 2006 :219). Qualitative methodologies have raised concerns over the limitations of quantitative templates (Brady et al. 2004 :4). Scholars such as King et al. ( 1994 ), for instance, argue that non-statistical research can produce more reliable results if researchers pay attention to the rules of scientific inference commonly stated in quantitative research. Also, researchers such as Becker ( 1966 :59; 1970 :42–43) have asserted that, if conducted properly, qualitative research and in particular ethnographic field methods, can lead to more accurate results than quantitative studies, in particular, survey research and laboratory experiments.

Some researchers, such as Kalof, Dan, and Dietz ( 2008 :79) claim that the boundaries between the two approaches are becoming blurred, and Small ( 2009 ) argues that currently much qualitative research (especially in North America) tries unsuccessfully and unnecessarily to emulate quantitative standards. For others, qualitative research tends to be more humanistic and discursive (King et al. 1994 :4). Ragin ( 1994 ), and similarly also Becker, ( 1996 :53), Marchel and Owens ( 2007 :303) think that the main distinction between the two styles is overstated and does not rest on the simple dichotomy of “numbers versus words” (Ragin 1994 :xii). Some claim that quantitative data can be utilized to discover associations, but in order to unveil cause and effect a complex research design involving the use of qualitative approaches needs to be devised (Gilbert 2009 :35). Consequently, qualitative data are useful for understanding the nuances lying beyond those processes as they unfold (Gilbert 2009 :35). Others contend that qualitative research is particularly well suited both to identify causality and to uncover fine descriptive distinctions (Fine and Hallett 2014 ; Lichterman and Isaac Reed 2014 ; Katz 2015 ).

There are other ways to separate these two traditions, including normative statements about what qualitative research should be (that is, better or worse than quantitative approaches, concerned with scientific approaches to societal change or vice versa; Snow and Morrill 1995 ; Denzin and Lincoln 2005 ), or whether it should develop falsifiable statements; Best 2004 ).

We propose that quantitative research is largely concerned with pre-determined variables (Small 2008 ); the analysis concerns the relations between variables. These categories are primarily not questioned in the study, only their frequency or degree, or the correlations between them (cf. Franzosi 2016 ). If a researcher studies wage differences between women and men, he or she works with given categories: x number of men are compared with y number of women, with a certain wage attributed to each person. The idea is not to move beyond the given categories of wage, men and women; they are the starting point as well as the end point, and undergo no “qualitative change.” Qualitative research, in contrast, investigates relations between categories that are themselves subject to change in the research process. Returning to Becker’s study ( 1963 ), we see that he questioned pre-dispositional theories of deviant behavior working with pre-determined variables such as an individual’s combination of personal qualities or emotional problems. His take, in contrast, was to understand marijuana consumption by developing “variables” as part of the investigation. Thereby he presented new variables, or as we would say today, theoretical concepts, but which are grounded in the empirical material.

Qualitative Research

This category contains quotations that refer to descriptions of qualitative research without making comparisons with quantitative research. Researchers such as Denzin and Lincoln, who have written a series of influential handbooks on qualitative methods (1994; Denzin and Lincoln 2003 ; 2005 ), citing Nelson et al. (1992:4), argue that because qualitative research is “interdisciplinary, transdisciplinary, and sometimes counterdisciplinary” it is difficult to derive one single definition of it (Jovanović 2011 :3). According to them, in fact, “the field” is “many things at the same time,” involving contradictions, tensions over its focus, methods, and how to derive interpretations and findings ( 2003 : 11). Similarly, others, such as Flick ( 2007 :ix–x) contend that agreeing on an accepted definition has increasingly become problematic, and that qualitative research has possibly matured different identities. However, Best holds that “the proliferation of many sorts of activities under the label of qualitative sociology threatens to confuse our discussions” ( 2004 :54). Atkinson’s position is more definite: “the current state of qualitative research and research methods is confused” ( 2005 :3–4).

Qualitative research is about interpretation (Blumer 1969 ; Strauss and Corbin 1998 ; Denzin and Lincoln 2003 ), or Verstehen [understanding] (Frankfort-Nachmias and Nachmias 1996 ). It is “multi-method,” involving the collection and use of a variety of empirical materials (Denzin and Lincoln 1998; Silverman 2013 ) and approaches (Silverman 2005 ; Flick 2007 ). It focuses not only on the objective nature of behavior but also on its subjective meanings: individuals’ own accounts of their attitudes, motivations, behavior (McIntyre 2005 :127; Creswell 2009 ), events and situations (Bryman 1989) – what people say and do in specific places and institutions (Goodwin and Horowitz 2002 :35–36) in social and temporal contexts (Morrill and Fine 1997). For this reason, following Weber ([1921-22] 1978), it can be described as an interpretative science (McIntyre 2005 :127). But could quantitative research also be concerned with these questions? Also, as pointed out below, does all qualitative research focus on subjective meaning, as some scholars suggest?

Others also distinguish qualitative research by claiming that it collects data using a naturalistic approach (Denzin and Lincoln 2005 :2; Creswell 2009 ), focusing on the meaning actors ascribe to their actions. But again, does all qualitative research need to be collected in situ? And does qualitative research have to be inherently concerned with meaning? Flick ( 2007 ), referring to Denzin and Lincoln ( 2005 ), mentions conversation analysis as an example of qualitative research that is not concerned with the meanings people bring to a situation, but rather with the formal organization of talk. Still others, such as Ragin ( 1994 :85), note that qualitative research is often (especially early on in the project, we would add) less structured than other kinds of social research – a characteristic connected to its flexibility and that can lead both to potentially better, but also worse results. But is this not a feature of this type of research, rather than a defining description of its essence? Wouldn’t this comment also apply, albeit to varying degrees, to quantitative research?

In addition, Strauss ( 2003 ), along with others, such as Alvesson and Kärreman ( 2011 :10–76), argue that qualitative researchers struggle to capture and represent complex phenomena partially because they tend to collect a large amount of data. While his analysis is correct at some points – “It is necessary to do detailed, intensive, microscopic examination of the data in order to bring out the amazing complexity of what lies in, behind, and beyond those data” (Strauss 2003 :10) – much of his analysis concerns the supposed focus of qualitative research and its challenges, rather than exactly what it is about. But even in this instance we would make a weak case arguing that these are strictly the defining features of qualitative research. Some researchers seem to focus on the approach or the methods used, or even on the way material is analyzed. Several researchers stress the naturalistic assumption of investigating the world, suggesting that meaning and interpretation appear to be a core matter of qualitative research.

We can also see that in this category there is no consensus about specific qualitative methods nor about qualitative data. Many emphasize interpretation, but quantitative research, too, involves interpretation; the results of a regression analysis, for example, certainly have to be interpreted, and the form of meta-analysis that factor analysis provides indeed requires interpretation However, there is no interpretation of quantitative raw data, i.e., numbers in tables. One common thread is that qualitative researchers have to get to grips with their data in order to understand what is being studied in great detail, irrespective of the type of empirical material that is being analyzed. This observation is connected to the fact that qualitative researchers routinely make several adjustments of focus and research design as their studies progress, in many cases until the very end of the project (Kalof et al. 2008 ). If you, like Becker, do not start out with a detailed theory, adjustments such as the emergence and refinement of research questions will occur during the research process. We have thus found a number of useful reflections about qualitative research scattered across different sources, but none of them effectively describe the defining characteristics of this approach.

Although qualitative research does not appear to be defined in terms of a specific method, it is certainly common that fieldwork, i.e., research that entails that the researcher spends considerable time in the field that is studied and use the knowledge gained as data, is seen as emblematic of or even identical to qualitative research. But because we understand that fieldwork tends to focus primarily on the collection and analysis of qualitative data, we expected to find within it discussions on the meaning of “qualitative.” But, again, this was not the case.

Instead, we found material on the history of this approach (for example, Frankfort-Nachmias and Nachmias 1996 ; Atkinson et al. 2001), including how it has changed; for example, by adopting a more self-reflexive practice (Heyl 2001), as well as the different nomenclature that has been adopted, such as fieldwork, ethnography, qualitative research, naturalistic research, participant observation and so on (for example, Lofland et al. 2006 ; Gans 1999 ).

We retrieved definitions of ethnography, such as “the study of people acting in the natural courses of their daily lives,” involving a “resocialization of the researcher” (Emerson 1988 :1) through intense immersion in others’ social worlds (see also examples in Hammersley 2018 ). This may be accomplished by direct observation and also participation (Neuman 2007 :276), although others, such as Denzin ( 1970 :185), have long recognized other types of observation, including non-participant (“fly on the wall”). In this category we have also isolated claims and opposing views, arguing that this type of research is distinguished primarily by where it is conducted (natural settings) (Hughes 1971:496), and how it is carried out (a variety of methods are applied) or, for some most importantly, by involving an active, empathetic immersion in those being studied (Emerson 1988 :2). We also retrieved descriptions of the goals it attends in relation to how it is taught (understanding subjective meanings of the people studied, primarily develop theory, or contribute to social change) (see for example, Corte and Irwin 2017 ; Frankfort-Nachmias and Nachmias 1996 :281; Trier-Bieniek 2012 :639) by collecting the richest possible data (Lofland et al. 2006 ) to derive “thick descriptions” (Geertz 1973 ), and/or to aim at theoretical statements of general scope and applicability (for example, Emerson 1988 ; Fine 2003 ). We have identified guidelines on how to evaluate it (for example Becker 1996 ; Lamont 2004 ) and have retrieved instructions on how it should be conducted (for example, Lofland et al. 2006 ). For instance, analysis should take place while the data gathering unfolds (Emerson 1988 ; Hammersley and Atkinson 2007 ; Lofland et al. 2006 ), observations should be of long duration (Becker 1970 :54; Goffman 1989 ), and data should be of high quantity (Becker 1970 :52–53), as well as other questionable distinctions between fieldwork and other methods:

Field studies differ from other methods of research in that the researcher performs the task of selecting topics, decides what questions to ask, and forges interest in the course of the research itself . This is in sharp contrast to many ‘theory-driven’ and ‘hypothesis-testing’ methods. (Lofland and Lofland 1995 :5)

But could not, for example, a strictly interview-based study be carried out with the same amount of flexibility, such as sequential interviewing (for example, Small 2009 )? Once again, are quantitative approaches really as inflexible as some qualitative researchers think? Moreover, this category stresses the role of the actors’ meaning, which requires knowledge and close interaction with people, their practices and their lifeworld.

It is clear that field studies – which are seen by some as the “gold standard” of qualitative research – are nonetheless only one way of doing qualitative research. There are other methods, but it is not clear why some are more qualitative than others, or why they are better or worse. Fieldwork is characterized by interaction with the field (the material) and understanding of the phenomenon that is being studied. In Becker’s case, he had general experience from fields in which marihuana was used, based on which he did interviews with actual users in several fields.

Grounded Theory

Another major category we identified in our sample is Grounded Theory. We found descriptions of it most clearly in Glaser and Strauss’ ([1967] 2010 ) original articulation, Strauss and Corbin ( 1998 ) and Charmaz ( 2006 ), as well as many other accounts of what it is for: generating and testing theory (Strauss 2003 :xi). We identified explanations of how this task can be accomplished – such as through two main procedures: constant comparison and theoretical sampling (Emerson 1998:96), and how using it has helped researchers to “think differently” (for example, Strauss and Corbin 1998 :1). We also read descriptions of its main traits, what it entails and fosters – for instance, an exceptional flexibility, an inductive approach (Strauss and Corbin 1998 :31–33; 1990; Esterberg 2002 :7), an ability to step back and critically analyze situations, recognize tendencies towards bias, think abstractly and be open to criticism, enhance sensitivity towards the words and actions of respondents, and develop a sense of absorption and devotion to the research process (Strauss and Corbin 1998 :5–6). Accordingly, we identified discussions of the value of triangulating different methods (both using and not using grounded theory), including quantitative ones, and theories to achieve theoretical development (most comprehensively in Denzin 1970 ; Strauss and Corbin 1998 ; Timmermans and Tavory 2012 ). We have also located arguments about how its practice helps to systematize data collection, analysis and presentation of results (Glaser and Strauss [1967] 2010 :16).

Grounded theory offers a systematic approach which requires researchers to get close to the field; closeness is a requirement of identifying questions and developing new concepts or making further distinctions with regard to old concepts. In contrast to other qualitative approaches, grounded theory emphasizes the detailed coding process, and the numerous fine-tuned distinctions that the researcher makes during the process. Within this category, too, we could not find a satisfying discussion of the meaning of qualitative research.

Defining Qualitative Research

In sum, our analysis shows that some notions reappear in the discussion of qualitative research, such as understanding, interpretation, “getting close” and making distinctions. These notions capture aspects of what we think is “qualitative.” However, a comprehensive definition that is useful and that can further develop the field is lacking, and not even a clear picture of its essential elements appears. In other words no definition emerges from our data, and in our research process we have moved back and forth between our empirical data and the attempt to present a definition. Our concrete strategy, as stated above, is to relate qualitative and quantitative research, or more specifically, qualitative and quantitative work. We use an ideal-typical notion of quantitative research which relies on taken for granted and numbered variables. This means that the data consists of variables on different scales, such as ordinal, but frequently ratio and absolute scales, and the representation of the numbers to the variables, i.e. the justification of the assignment of numbers to object or phenomenon, are not questioned, though the validity may be questioned. In this section we return to the notion of quality and try to clarify it while presenting our contribution.

Broadly, research refers to the activity performed by people trained to obtain knowledge through systematic procedures. Notions such as “objectivity” and “reflexivity,” “systematic,” “theory,” “evidence” and “openness” are here taken for granted in any type of research. Next, building on our empirical analysis we explain the four notions that we have identified as central to qualitative work: distinctions, process, closeness, and improved understanding. In discussing them, ultimately in relation to one another, we make their meaning even more precise. Our idea, in short, is that only when these ideas that we present separately for analytic purposes are brought together can we speak of qualitative research.

Distinctions

We believe that the possibility of making new distinctions is one the defining characteristics of qualitative research. It clearly sets it apart from quantitative analysis which works with taken-for-granted variables, albeit as mentioned, meta-analyses, for example, factor analysis may result in new variables. “Quality” refers essentially to distinctions, as already pointed out by Aristotle. He discusses the term “qualitative” commenting: “By a quality I mean that in virtue of which things are said to be qualified somehow” (Aristotle 1984:14). Quality is about what something is or has, which means that the distinction from its environment is crucial. We see qualitative research as a process in which significant new distinctions are made to the scholarly community; to make distinctions is a key aspect of obtaining new knowledge; a point, as we will see, that also has implications for “quantitative research.” The notion of being “significant” is paramount. New distinctions by themselves are not enough; just adding concepts only increases complexity without furthering our knowledge. The significance of new distinctions is judged against the communal knowledge of the research community. To enable this discussion and judgements central elements of rational discussion are required (cf. Habermas [1981] 1987 ; Davidsson [ 1988 ] 2001) to identify what is new and relevant scientific knowledge. Relatedly, Ragin alludes to the idea of new and useful knowledge at a more concrete level: “Qualitative methods are appropriate for in-depth examination of cases because they aid the identification of key features of cases. Most qualitative methods enhance data” (1994:79). When Becker ( 1963 ) studied deviant behavior and investigated how people became marihuana smokers, he made distinctions between the ways in which people learned how to smoke. This is a classic example of how the strategy of “getting close” to the material, for example the text, people or pictures that are subject to analysis, may enable researchers to obtain deeper insight and new knowledge by making distinctions – in this instance on the initial notion of learning how to smoke. Others have stressed the making of distinctions in relation to coding or theorizing. Emerson et al. ( 1995 ), for example, hold that “qualitative coding is a way of opening up avenues of inquiry,” meaning that the researcher identifies and develops concepts and analytic insights through close examination of and reflection on data (Emerson et al. 1995 :151). Goodwin and Horowitz highlight making distinctions in relation to theory-building writing: “Close engagement with their cases typically requires qualitative researchers to adapt existing theories or to make new conceptual distinctions or theoretical arguments to accommodate new data” ( 2002 : 37). In the ideal-typical quantitative research only existing and so to speak, given, variables would be used. If this is the case no new distinction are made. But, would not also many “quantitative” researchers make new distinctions?

Process does not merely suggest that research takes time. It mainly implies that qualitative new knowledge results from a process that involves several phases, and above all iteration. Qualitative research is about oscillation between theory and evidence, analysis and generating material, between first- and second -order constructs (Schütz 1962 :59), between getting in contact with something, finding sources, becoming deeply familiar with a topic, and then distilling and communicating some of its essential features. The main point is that the categories that the researcher uses, and perhaps takes for granted at the beginning of the research process, usually undergo qualitative changes resulting from what is found. Becker describes how he tested hypotheses and let the jargon of the users develop into theoretical concepts. This happens over time while the study is being conducted, exemplifying what we mean by process.

In the research process, a pilot-study may be used to get a first glance of, for example, the field, how to approach it, and what methods can be used, after which the method and theory are chosen or refined before the main study begins. Thus, the empirical material is often central from the start of the project and frequently leads to adjustments by the researcher. Likewise, during the main study categories are not fixed; the empirical material is seen in light of the theory used, but it is also given the opportunity to kick back, thereby resisting attempts to apply theoretical straightjackets (Becker 1970 :43). In this process, coding and analysis are interwoven, and thus are often important steps for getting closer to the phenomenon and deciding what to focus on next. Becker began his research by interviewing musicians close to him, then asking them to refer him to other musicians, and later on doubling his original sample of about 25 to include individuals in other professions (Becker 1973:46). Additionally, he made use of some participant observation, documents, and interviews with opiate users made available to him by colleagues. As his inductive theory of deviance evolved, Becker expanded his sample in order to fine tune it, and test the accuracy and generality of his hypotheses. In addition, he introduced a negative case and discussed the null hypothesis ( 1963 :44). His phasic career model is thus based on a research design that embraces processual work. Typically, process means to move between “theory” and “material” but also to deal with negative cases, and Becker ( 1998 ) describes how discovering these negative cases impacted his research design and ultimately its findings.

Obviously, all research is process-oriented to some degree. The point is that the ideal-typical quantitative process does not imply change of the data, and iteration between data, evidence, hypotheses, empirical work, and theory. The data, quantified variables, are, in most cases fixed. Merging of data, which of course can be done in a quantitative research process, does not mean new data. New hypotheses are frequently tested, but the “raw data is often the “the same.” Obviously, over time new datasets are made available and put into use.

Another characteristic that is emphasized in our sample is that qualitative researchers – and in particular ethnographers – can, or as Goffman put it, ought to ( 1989 ), get closer to the phenomenon being studied and their data than quantitative researchers (for example, Silverman 2009 :85). Put differently, essentially because of their methods qualitative researchers get into direct close contact with those being investigated and/or the material, such as texts, being analyzed. Becker started out his interview study, as we noted, by talking to those he knew in the field of music to get closer to the phenomenon he was studying. By conducting interviews he got even closer. Had he done more observations, he would undoubtedly have got even closer to the field.

Additionally, ethnographers’ design enables researchers to follow the field over time, and the research they do is almost by definition longitudinal, though the time in the field is studied obviously differs between studies. The general characteristic of closeness over time maximizes the chances of unexpected events, new data (related, for example, to archival research as additional sources, and for ethnography for situations not necessarily previously thought of as instrumental – what Mannay and Morgan ( 2015 ) term the “waiting field”), serendipity (Merton and Barber 2004 ; Åkerström 2013 ), and possibly reactivity, as well as the opportunity to observe disrupted patterns that translate into exemplars of negative cases. Two classic examples of this are Becker’s finding of what medical students call “crocks” (Becker et al. 1961 :317), and Geertz’s ( 1973 ) study of “deep play” in Balinese society.

By getting and staying so close to their data – be it pictures, text or humans interacting (Becker was himself a musician) – for a long time, as the research progressively focuses, qualitative researchers are prompted to continually test their hunches, presuppositions and hypotheses. They test them against a reality that often (but certainly not always), and practically, as well as metaphorically, talks back, whether by validating them, or disqualifying their premises – correctly, as well as incorrectly (Fine 2003 ; Becker 1970 ). This testing nonetheless often leads to new directions for the research. Becker, for example, says that he was initially reading psychological theories, but when facing the data he develops a theory that looks at, you may say, everything but psychological dispositions to explain the use of marihuana. Especially researchers involved with ethnographic methods have a fairly unique opportunity to dig up and then test (in a circular, continuous and temporal way) new research questions and findings as the research progresses, and thereby to derive previously unimagined and uncharted distinctions by getting closer to the phenomenon under study.

Let us stress that getting close is by no means restricted to ethnography. The notion of hermeneutic circle and hermeneutics as a general way of understanding implies that we must get close to the details in order to get the big picture. This also means that qualitative researchers can literally also make use of details of pictures as evidence (cf. Harper 2002). Thus, researchers may get closer both when generating the material or when analyzing it.

Quantitative research, we maintain, in the ideal-typical representation cannot get closer to the data. The data is essentially numbers in tables making up the variables (Franzosi 2016 :138). The data may originally have been “qualitative,” but once reduced to numbers there can only be a type of “hermeneutics” about what the number may stand for. The numbers themselves, however, are non-ambiguous. Thus, in quantitative research, interpretation, if done, is not about the data itself—the numbers—but what the numbers stand for. It follows that the interpretation is essentially done in a more “speculative” mode without direct empirical evidence (cf. Becker 2017 ).

Improved Understanding

While distinction, process and getting closer refer to the qualitative work of the researcher, improved understanding refers to its conditions and outcome of this work. Understanding cuts deeper than explanation, which to some may mean a causally verified correlation between variables. The notion of explanation presupposes the notion of understanding since explanation does not include an idea of how knowledge is gained (Manicas 2006 : 15). Understanding, we argue, is the core concept of what we call the outcome of the process when research has made use of all the other elements that were integrated in the research. Understanding, then, has a special status in qualitative research since it refers both to the conditions of knowledge and the outcome of the process. Understanding can to some extent be seen as the condition of explanation and occurs in a process of interpretation, which naturally refers to meaning (Gadamer 1990 ). It is fundamentally connected to knowing, and to the knowing of how to do things (Heidegger [1927] 2001 ). Conceptually the term hermeneutics is used to account for this process. Heidegger ties hermeneutics to human being and not possible to separate from the understanding of being ( 1988 ). Here we use it in a broader sense, and more connected to method in general (cf. Seiffert 1992 ). The abovementioned aspects – for example, “objectivity” and “reflexivity” – of the approach are conditions of scientific understanding. Understanding is the result of a circular process and means that the parts are understood in light of the whole, and vice versa. Understanding presupposes pre-understanding, or in other words, some knowledge of the phenomenon studied. The pre-understanding, even in the form of prejudices, are in qualitative research process, which we see as iterative, questioned, which gradually or suddenly change due to the iteration of data, evidence and concepts. However, qualitative research generates understanding in the iterative process when the researcher gets closer to the data, e.g., by going back and forth between field and analysis in a process that generates new data that changes the evidence, and, ultimately, the findings. Questioning, to ask questions, and put what one assumes—prejudices and presumption—in question, is central to understand something (Heidegger [1927] 2001 ; Gadamer 1990 :368–384). We propose that this iterative process in which the process of understanding occurs is characteristic of qualitative research.

Improved understanding means that we obtain scientific knowledge of something that we as a scholarly community did not know before, or that we get to know something better. It means that we understand more about how parts are related to one another, and to other things we already understand (see also Fine and Hallett 2014 ). Understanding is an important condition for qualitative research. It is not enough to identify correlations, make distinctions, and work in a process in which one gets close to the field or phenomena. Understanding is accomplished when the elements are integrated in an iterative process.

It is, moreover, possible to understand many things, and researchers, just like children, may come to understand new things every day as they engage with the world. This subjective condition of understanding – namely, that a person gains a better understanding of something –is easily met. To be qualified as “scientific,” the understanding must be general and useful to many; it must be public. But even this generally accessible understanding is not enough in order to speak of “scientific understanding.” Though we as a collective can increase understanding of everything in virtually all potential directions as a result also of qualitative work, we refrain from this “objective” way of understanding, which has no means of discriminating between what we gain in understanding. Scientific understanding means that it is deemed relevant from the scientific horizon (compare Schütz 1962 : 35–38, 46, 63), and that it rests on the pre-understanding that the scientists have and must have in order to understand. In other words, the understanding gained must be deemed useful by other researchers, so that they can build on it. We thus see understanding from a pragmatic, rather than a subjective or objective perspective. Improved understanding is related to the question(s) at hand. Understanding, in order to represent an improvement, must be an improvement in relation to the existing body of knowledge of the scientific community (James [ 1907 ] 1955). Scientific understanding is, by definition, collective, as expressed in Weber’s famous note on objectivity, namely that scientific work aims at truths “which … can claim, even for a Chinese, the validity appropriate to an empirical analysis” ([1904] 1949 :59). By qualifying “improved understanding” we argue that it is a general defining characteristic of qualitative research. Becker‘s ( 1966 ) study and other research of deviant behavior increased our understanding of the social learning processes of how individuals start a behavior. And it also added new knowledge about the labeling of deviant behavior as a social process. Few studies, of course, make the same large contribution as Becker’s, but are nonetheless qualitative research.

Understanding in the phenomenological sense, which is a hallmark of qualitative research, we argue, requires meaning and this meaning is derived from the context, and above all the data being analyzed. The ideal-typical quantitative research operates with given variables with different numbers. This type of material is not enough to establish meaning at the level that truly justifies understanding. In other words, many social science explanations offer ideas about correlations or even causal relations, but this does not mean that the meaning at the level of the data analyzed, is understood. This leads us to say that there are indeed many explanations that meet the criteria of understanding, for example the explanation of how one becomes a marihuana smoker presented by Becker. However, we may also understand a phenomenon without explaining it, and we may have potential explanations, or better correlations, that are not really understood.

We may speak more generally of quantitative research and its data to clarify what we see as an important distinction. The “raw data” that quantitative research—as an idealtypical activity, refers to is not available for further analysis; the numbers, once created, are not to be questioned (Franzosi 2016 : 138). If the researcher is to do “more” or “change” something, this will be done by conjectures based on theoretical knowledge or based on the researcher’s lifeworld. Both qualitative and quantitative research is based on the lifeworld, and all researchers use prejudices and pre-understanding in the research process. This idea is present in the works of Heidegger ( 2001 ) and Heisenberg (cited in Franzosi 2010 :619). Qualitative research, as we argued, involves the interaction and questioning of concepts (theory), data, and evidence.

Ragin ( 2004 :22) points out that “a good definition of qualitative research should be inclusive and should emphasize its key strengths and features, not what it lacks (for example, the use of sophisticated quantitative techniques).” We define qualitative research as an iterative process in which improved understanding to the scientific community is achieved by making new significant distinctions resulting from getting closer to the phenomenon studied. Qualitative research, as defined here, is consequently a combination of two criteria: (i) how to do things –namely, generating and analyzing empirical material, in an iterative process in which one gets closer by making distinctions, and (ii) the outcome –improved understanding novel to the scholarly community. Is our definition applicable to our own study? In this study we have closely read the empirical material that we generated, and the novel distinction of the notion “qualitative research” is the outcome of an iterative process in which both deduction and induction were involved, in which we identified the categories that we analyzed. We thus claim to meet the first criteria, “how to do things.” The second criteria cannot be judged but in a partial way by us, namely that the “outcome” —in concrete form the definition-improves our understanding to others in the scientific community.

We have defined qualitative research, or qualitative scientific work, in relation to quantitative scientific work. Given this definition, qualitative research is about questioning the pre-given (taken for granted) variables, but it is thus also about making new distinctions of any type of phenomenon, for example, by coining new concepts, including the identification of new variables. This process, as we have discussed, is carried out in relation to empirical material, previous research, and thus in relation to theory. Theory and previous research cannot be escaped or bracketed. According to hermeneutic principles all scientific work is grounded in the lifeworld, and as social scientists we can thus never fully bracket our pre-understanding.

We have proposed that quantitative research, as an idealtype, is concerned with pre-determined variables (Small 2008 ). Variables are epistemically fixed, but can vary in terms of dimensions, such as frequency or number. Age is an example; as a variable it can take on different numbers. In relation to quantitative research, qualitative research does not reduce its material to number and variables. If this is done the process of comes to a halt, the researcher gets more distanced from her data, and it makes it no longer possible to make new distinctions that increase our understanding. We have above discussed the components of our definition in relation to quantitative research. Our conclusion is that in the research that is called quantitative there are frequent and necessary qualitative elements.

Further, comparative empirical research on researchers primarily working with ”quantitative” approaches and those working with ”qualitative” approaches, we propose, would perhaps show that there are many similarities in practices of these two approaches. This is not to deny dissimilarities, or the different epistemic and ontic presuppositions that may be more or less strongly associated with the two different strands (see Goertz and Mahoney 2012 ). Our point is nonetheless that prejudices and preconceptions about researchers are unproductive, and that as other researchers have argued, differences may be exaggerated (e.g., Becker 1996 : 53, 2017 ; Marchel and Owens 2007 :303; Ragin 1994 ), and that a qualitative dimension is present in both kinds of work.

Several things follow from our findings. The most important result is the relation to quantitative research. In our analysis we have separated qualitative research from quantitative research. The point is not to label individual researchers, methods, projects, or works as either “quantitative” or “qualitative.” By analyzing, i.e., taking apart, the notions of quantitative and qualitative, we hope to have shown the elements of qualitative research. Our definition captures the elements, and how they, when combined in practice, generate understanding. As many of the quotations we have used suggest, one conclusion of our study holds that qualitative approaches are not inherently connected with a specific method. Put differently, none of the methods that are frequently labelled “qualitative,” such as interviews or participant observation, are inherently “qualitative.” What matters, given our definition, is whether one works qualitatively or quantitatively in the research process, until the results are produced. Consequently, our analysis also suggests that those researchers working with what in the literature and in jargon is often called “quantitative research” are almost bound to make use of what we have identified as qualitative elements in any research project. Our findings also suggest that many” quantitative” researchers, at least to some extent, are engaged with qualitative work, such as when research questions are developed, variables are constructed and combined, and hypotheses are formulated. Furthermore, a research project may hover between “qualitative” and “quantitative” or start out as “qualitative” and later move into a “quantitative” (a distinct strategy that is not similar to “mixed methods” or just simply combining induction and deduction). More generally speaking, the categories of “qualitative” and “quantitative,” unfortunately, often cover up practices, and it may lead to “camps” of researchers opposing one another. For example, regardless of the researcher is primarily oriented to “quantitative” or “qualitative” research, the role of theory is neglected (cf. Swedberg 2017 ). Our results open up for an interaction not characterized by differences, but by different emphasis, and similarities.

Let us take two examples to briefly indicate how qualitative elements can fruitfully be combined with quantitative. Franzosi ( 2010 ) has discussed the relations between quantitative and qualitative approaches, and more specifically the relation between words and numbers. He analyzes texts and argues that scientific meaning cannot be reduced to numbers. Put differently, the meaning of the numbers is to be understood by what is taken for granted, and what is part of the lifeworld (Schütz 1962 ). Franzosi shows how one can go about using qualitative and quantitative methods and data to address scientific questions analyzing violence in Italy at the time when fascism was rising (1919–1922). Aspers ( 2006 ) studied the meaning of fashion photographers. He uses an empirical phenomenological approach, and establishes meaning at the level of actors. In a second step this meaning, and the different ideal-typical photographers constructed as a result of participant observation and interviews, are tested using quantitative data from a database; in the first phase to verify the different ideal-types, in the second phase to use these types to establish new knowledge about the types. In both of these cases—and more examples can be found—authors move from qualitative data and try to keep the meaning established when using the quantitative data.

A second main result of our study is that a definition, and we provided one, offers a way for research to clarify, and even evaluate, what is done. Hence, our definition can guide researchers and students, informing them on how to think about concrete research problems they face, and to show what it means to get closer in a process in which new distinctions are made. The definition can also be used to evaluate the results, given that it is a standard of evaluation (cf. Hammersley 2007 ), to see whether new distinctions are made and whether this improves our understanding of what is researched, in addition to the evaluation of how the research was conducted. By making what is qualitative research explicit it becomes easier to communicate findings, and it is thereby much harder to fly under the radar with substandard research since there are standards of evaluation which make it easier to separate “good” from “not so good” qualitative research.

To conclude, our analysis, which ends with a definition of qualitative research can thus both address the “internal” issues of what is qualitative research, and the “external” critiques that make it harder to do qualitative research, to which both pressure from quantitative methods and general changes in society contribute.

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Acknowledgements

Financial Support for this research is given by the European Research Council, CEV (263699). The authors are grateful to Susann Krieglsteiner for assistance in collecting the data. The paper has benefitted from the many useful comments by the three reviewers and the editor, comments by members of the Uppsala Laboratory of Economic Sociology, as well as Jukka Gronow, Sebastian Kohl, Marcin Serafin, Richard Swedberg, Anders Vassenden and Turid Rødne.

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The Ultimate Guide to Qualitative Research - Part 1: The Basics

• Introduction and overview

Basics of qualitative research

Types, aspects, examples, benefits and challenges, how qualitative research complements quantitative research, how is qualitative research reported.

• What is qualitative data?
• Examples of qualitative data
• Qualitative vs. quantitative research
• Mixed methods
• Qualitative research preparation
• Theoretical perspective
• Theoretical framework
• Literature reviews
• Research question
• Conceptual framework
• Conceptual vs. theoretical framework
• Data collection
• Qualitative research methods
• Focus groups
• Observational research
• Case studies
• Ethnographical research

Ethical considerations

• Confidentiality and privacy
• Power dynamics
• Reflexivity

What is qualitative research?

Qualitative research is an essential approach in various academic disciplines and professional fields, as it seeks to understand and interpret the meanings, experiences, and social realities of people in their natural settings. This type of research employs an array of qualitative methods to gather and analyze non-numerical data, such as words, images, and behaviors, and aims to generate in-depth and contextualized insights into the phenomena under study.

Qualitative research is designed to address research questions that focus on understanding the "why" and "how" of human behavior, experiences, and interactions, rather than just the "what" or "how many" that quantitative methods typically seek to answer. The main purpose of qualitative research is to gain a rich and nuanced understanding of people's perspectives, emotions, beliefs, and motivations in relation to specific issues, situations, or phenomena.

Characteristics of qualitative research

Several key characteristics distinguish qualitative research from other types of research, such as quantitative research:

Naturalistic settings : Qualitative researchers collect data in the real-world settings where the phenomena of interest occur, rather than in controlled laboratory environments. This allows researchers to observe and understand the participants' behavior, experiences, and social interactions in their natural context.

Inductive approach : Unlike quantitative research, which often follows a deductive approach , qualitative research begins with the collection of data and then seeks to develop theories, concepts, or themes that emerge from the data. This inductive approach enables researchers to stay open to new insights and unexpected findings.

Holistic perspective : Qualitative research aims to provide a comprehensive understanding of the phenomena under study by considering multiple dimensions, such as the social, cultural, historical, and psychological aspects that shape people's experiences and behavior.

Subjectivity and interpretation : Epistemology plays a crucial role in qualitative research. Researchers are encouraged to reflect on their biases, assumptions, and values , and to consider how these may influence their data collection, analysis, and interpretation.

Flexibility : Qualitative research methods are often flexible and adaptable, allowing researchers to refine their research questions , sampling strategies, or data collection techniques as new insights and perspectives emerge during the research process.

Key principles of qualitative research

Qualitative research is guided by several fundamental principles that shape its approach, methods, and analysis:

Empathy and reflexivity : Qualitative researchers strive to empathize with the participants and to understand their perspectives, experiences, and emotions from their viewpoint. This requires researchers to be attentive, open-minded, and sensitive to the participants' verbal and non-verbal cues. At the same, qualitative researchers critically reflect on their participants’ perspectives, experiences, and emotions to develop their findings and conclusions, instead of taking these at face value. In addition, it is important for the researcher to reflect on how their own role and viewpoint may be shaping the research.

Trustworthiness : Establishing trustworthiness in qualitative research involves demonstrating credibility, transferability, dependability, and confirmability. Researchers can enhance trustworthiness by using various strategies, such as triangulation, member checking , peer debriefing , and reflexivity .

Iterative analysis : Qualitative data analysis is an ongoing and iterative process, in which researchers continually review, compare, and revise their interpretations as they collect and analyze more data. This iterative process allows researchers to refine their understanding of the phenomena and to develop more robust and nuanced theories, concepts, or themes.

Rich description : Providing detailed, vivid, and context-sensitive descriptions of the data is essential in qualitative research. Rich descriptions help convey the complexity and nuances of the phenomena under study, and enable readers to assess the relevance and transferability of the findings to other settings or populations.

What are the common types of qualitative research?

Qualitative research is an umbrella term for various methodologies that focus on understanding and interpreting human experiences, behaviors, and social phenomena within their context. These approaches seek to gather in-depth, rich data through the analysis of language, actions, and expressions. Five common types of qualitative research are narrative research , phenomenology , grounded theory , ethnography , and case study .

Narrative research : This approach focuses on the stories and experiences of individuals, aiming to understand their lives and personal perspectives. Researchers can collect data through interviews, letters, diaries, or autobiographies, and analyze these narratives to identify recurring themes, patterns, and meanings. Narrative research can be valuable for exploring individual identities, cultural beliefs, and historical events.

Phenomenology : Phenomenology seeks to understand the essence of a particular phenomenon by analyzing the experiences and perceptions of individuals who have gone through that phenomenon . Researchers can explore participants' thoughts, feelings, and experiences through in-depth interviews, observations, or written materials. The goal is to describe the commonalities and variations in these experiences, ultimately revealing the underlying structures and meaning of the phenomenon under study.

Grounded theory : This inductive research method aims to generate new theories by systematically collecting and analyzing data. Researchers begin with an open-ended research question and gather data through observations, interviews, and document analysis . They then use a process of coding and constant comparison to identify patterns, categories, and relationships in the data. This iterative process continues until a comprehensive, grounded theory emerges that is based in the recollected data and explains the topic of interest.

Ethnography : Ethnographic research involves the in-depth study of a specific cultural or social group, focusing on understanding its members' behaviors, beliefs, and interactions. Researchers immerse themselves in the group's environment, often for extended periods, to observe and participate in daily activities. They can collect data through field notes, interviews, and document analysis, aiming to provide a holistic and nuanced understanding of the group's cultural practices and social dynamics.

Case study : A case study is an in-depth examination of a specific instance, event, organization, or individual within its real-life context. Researchers use multiple sources of data, such as interviews, observations, documents, and artifacts to build a rich, detailed understanding of the case. Case study research can be used to explore complex phenomena, generate new hypotheses , or evaluate the effectiveness of interventions or policies.

What are the purposes of qualitative research?

Qualitative research presents outcomes that emerge from the process of collecting and analyzing qualitative data. These outcomes often involve generating new theories, developing or challenging existing theories, and proposing practical implications based on actionable insights. The products of qualitative research contribute to a deeper understanding of human experiences, social phenomena, and cultural contexts. Qualitative research can also be a powerful complement to quantitative research.

Generating new theory : One of the primary goals of qualitative research is to develop new theories or conceptual frameworks that help explain previously unexplored or poorly understood phenomena. By conducting in-depth investigations and analyzing rich data, researchers can identify patterns, relationships, and underlying structures that form the basis of novel theoretical insights.

Developing or challenging existing theory : Qualitative research can also contribute to the refinement or expansion of existing theories by providing new perspectives, revealing previously unnoticed complexities, or highlighting areas where current theories may be insufficient or inaccurate. By examining the nuances and context-specific details of a phenomenon, researchers can generate evidence that supports, contradicts, or modifies existing theoretical frameworks .

Proposing practical implications : Qualitative research often yields actionable insights that can inform policy, practice, and intervention strategies. By delving into the lived experiences of individuals and communities, researchers can identify factors that contribute to or hinder the effectiveness of certain approaches, uncovering opportunities for improvement or innovation. The insights gained from qualitative research can be used to design targeted interventions, develop context-sensitive policies, or inform the professional practices of practitioners in various fields.

Enhancing understanding and empathy : Qualitative research promotes a deeper understanding of human experiences, emotions, and perspectives, fostering empathy and cultural sensitivity. By engaging with diverse voices and experiences, researchers can develop a more nuanced appreciation of the complexities of human behavior and social dynamics, ultimately contributing to more compassionate and inclusive societies.

Informing mixed-methods research : The products of qualitative research can also be used in conjunction with quantitative research, as part of a mixed-methods approach . Qualitative findings can help generate hypotheses for further testing, inform the development of survey instruments , or provide context and explanation for quantitative results. Combining the strengths of both approaches can lead to more robust and comprehensive understanding of complex research questions .

What are some examples of qualitative research?

Qualitative research can be conducted across various scientific fields, exploring diverse topics and phenomena. Here are six brief descriptions of qualitative studies that can provide researchers with ideas for their own projects:

Exploring the lived experiences of refugees : A phenomenological study could be conducted to investigate the lived experiences and coping strategies of refugees in a specific host country. By conducting in-depth interviews with refugees and analyzing their narratives , researchers can gain insights into the challenges they face, their resilience, and the factors that contribute to successful integration into their new communities.

Understanding the dynamics of online communities : An ethnographic study could be designed to explore the culture and social dynamics of a particular online community or social media platform. By immersing themselves in the virtual environment, researchers can observe patterns of interaction, communication styles, and shared values among community members, providing a nuanced understanding of the factors that influence online behavior and group dynamics.

Examining the impact of gentrification on local communities : A case study could be conducted to explore the impact of gentrification on a specific neighborhood or community. Researchers can collect data through interviews with residents, local business owners, and policymakers, as well as analyzing relevant documents and media coverage. The study can shed light on the effects of gentrification on housing affordability, social cohesion, and cultural identity, informing policy and urban planning decisions.

Studying the career trajectories of women in STEM fields : A narrative research project can be designed to investigate the career experiences and pathways of women in science, technology, engineering, and mathematics (STEM) fields. By collecting and analyzing the stories of women at various career stages, researchers can identify factors that contribute to their success, as well as barriers and challenges they face in male-dominated fields.

Evaluating the effectiveness of a mental health intervention : A qualitative study can be conducted to evaluate the effectiveness of a specific mental health intervention, such as a mindfulness-based program for reducing stress and anxiety. Researchers can gather data through interviews and focus groups with program participants, exploring their experiences, perceived benefits, and suggestions for improvement. The findings can provide valuable insights for refining the intervention and informing future mental health initiatives.

Investigating the role of social media in political activism : A qualitative study using document analysis and visual methods could explore the role of social media in shaping political activism and public opinion during a specific social movement or election campaign. By analyzing user-generated content, such as tweets, posts, images, and videos, researchers can examine patterns of communication, mobilization, and discourse, shedding light on the ways in which social media influences political engagement and democratic processes.

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What are common qualitative research methods?

Qualitative research methods are techniques used to collect, analyze, and interpret data in qualitative studies. These methods prioritize the exploration of meaning, context, and individual experiences. Common qualitative research methods include interviews, focus groups, observations, document analysis, and visual methods.

Interviews : Interviews involve one-on-one conversations between the researcher and the participant. They can be structured, semi-structured, or unstructured, depending on the level of guidance provided by the researcher. Interviews allow for in-depth exploration of participants' experiences, thoughts, and feelings, providing rich and detailed data for analysis.

Focus groups : Focus groups are group discussions facilitated by a researcher, usually consisting of 6-12 participants. They enable researchers to explore participants' collective perspectives, opinions, and experiences in a social setting. Focus groups can generate insights into group dynamics, cultural norms, and shared understandings, as participants interact and respond to each other's viewpoints.

Observations : Observational research involves the systematic collection of data through watching and recording people, events, or behaviors in their natural settings. Researchers can take on different roles, such as participant-observer or non-participant observer, depending on their level of involvement. Observations provide valuable information about context, social interactions, and non-verbal communication, which can help researchers understand the nuances of a particular phenomenon.

Document analysis : Document analysis is the examination of written or visual materials, such as letters, diaries, reports, newspaper articles, photographs, or videos. This method can provide insights into historical or cultural contexts, individual perspectives, and organizational processes. Researchers may use content analysis, discourse analysis, or other analytic techniques to interpret the meaning and significance of these documents.

Visual methods : Visual methods involve the use of visual materials, such as photographs, drawings, or videos, to explore and represent participants' experiences and perspectives. Techniques like photo elicitation, where participants are asked to take or select photographs related to the research topic and discuss their meaning, can encourage reflection and stimulate discussion. Visual methods can be particularly useful in capturing non-verbal information, promoting cross-cultural understanding, and engaging with hard-to-reach populations.

Importance of qualitative research and qualitative data analysis

Qualitative research and qualitative data analysis play a vital role in advancing knowledge, informing policies, and improving practices in various fields, such as education, healthcare, business, and social work. The unique insights and in-depth understanding generated through qualitative research can accomplish a number of goals.

Inform decision-making

Qualitative research helps decision-makers better understand the needs, preferences, and concerns of different stakeholders, such as customers, employees, or community members. This can lead to more effective and tailored policies, programs, or interventions that address real-world challenges.

Enhance innovation

By exploring people's experiences, motivations, and aspirations, qualitative research can uncover new ideas, opportunities, and trends that can drive innovation in products, services, or processes.

Foster empathy and cultural competence

Qualitative research can increase our empathy and understanding of diverse populations, cultures, and contexts. This can enhance our ability to communicate, collaborate, and work effectively with people from different backgrounds.

Complement quantitative research

Qualitative research can complement quantitative research by providing rich contextual information and in-depth insights into the underlying mechanisms, processes, or factors that may explain the patterns or relationships observed in quantitative data.

Facilitate social change

Qualitative research can give voice to marginalized or underrepresented groups, highlight social injustices or inequalities, and inspire actions and reforms that promote social change and well-being.

Challenges of conducting qualitative research

While qualitative research offers valuable insights and understanding of human experiences, it also presents some challenges that researchers must navigate. Acknowledging and addressing these challenges can help ensure the rigor, credibility, and relevance of qualitative research. In this section, we will discuss some common challenges that researchers may encounter when conducting qualitative research and offer suggestions on how to overcome them.

Subjectivity and bias

One of the primary challenges in qualitative research is managing subjectivity and potential biases that may arise from the researcher's personal beliefs, values, and experiences. Since qualitative research relies on the researcher's interpretation of the data , there is a risk that the researcher's subjectivity may influence the findings.

Researchers can minimize the impact of subjectivity and bias by maintaining reflexivity , or ongoing self-awareness and critical reflection on their role, assumptions, and influences in the research process. This may involve keeping a reflexive journal, engaging in peer debriefing , and discussing potential biases with research participants during member checking .

Data collection and quality

Collecting high-quality data in qualitative research can be challenging, particularly when dealing with sensitive topics, hard-to-reach populations, or complex social phenomena. Ensuring the trustworthiness of qualitative data collection is essential to producing credible and meaningful findings.

Researchers can enhance data quality by employing various strategies, such as purposive or theoretical sampling, triangulation of data sources, methods or researchers, and establishing rapport and trust with research participants.

Data analysis and interpretation

The analysis and interpretation of qualitative data can be a complex, time-consuming, and sometimes overwhelming process. Researchers must make sense of large amounts of diverse and unstructured data, while also ensuring the rigor, transparency, and consistency of their analysis.

Researchers can facilitate data analysis and interpretation by adopting systematic and well-established approaches, such as thematic analysis , grounded theory , or content analysis . Utilizing qualitative data analysis software , like ATLAS.ti, can also help manage and analyze data more efficiently and rigorously.

Qualitative research often involves exploring sensitive issues or working with vulnerable populations, which raises various ethical considerations , such as privacy, confidentiality , informed consent , and potential harm to participants.

Researchers should be familiar with the ethical guidelines and requirements of their discipline, institution, or funding agency, and should obtain ethical approval from relevant review boards or committees before conducting the research. Researchers should also maintain open communication with participants, respect their autonomy and dignity, and protect their well-being throughout the research process.

Generalizability and transferability

Qualitative research typically focuses on in-depth exploration of specific cases or contexts, which may limit the generalizability or transferability of the findings to other settings or populations. However, the goal of qualitative research is not to produce statistically generalizable results but rather to provide a rich, contextualized, and nuanced understanding of the phenomena under study.

Researchers can enhance the transferability of their findings by providing rich descriptions of the research context, participants, and methods, and by discussing the potential applicability or relevance of the findings to other settings or populations. Readers can then assess the transferability of the findings based on the similarity of their own context to the one described in the research.

By addressing these challenges and adopting rigorous and transparent research practices, qualitative researchers can contribute valuable and meaningful insights that advance knowledge, inform policies, and improve practices in various fields and contexts.

Qualitative and quantitative research approaches are often seen as distinct and even opposing paradigms. However, these two approaches can be complementary, providing a more comprehensive understanding of complex social phenomena when combined. In this section, we will discuss how qualitative research can complement quantitative research and enhance the overall depth, breadth, and rigor of research findings.

Exploring and understanding context

Quantitative research excels at identifying patterns, trends, and relationships among variables using numerical data, while qualitative research provides rich and nuanced insights into the context, meaning, and underlying processes that shape these patterns or relationships. By integrating qualitative research with quantitative research, researchers can explore not only the "what" or "how many" but also the "why" and "how" of the phenomena under study.

For example, a quantitative study in health services research might reveal a correlation between social media usage and mental health outcomes, while a qualitative study could help explain the reasons behind this correlation by exploring users' experiences, motivations, and perceptions of social media. Qualitative and quantitative data in this case complement each other to contribute to a more robust theory and more informed policy implications.

Generating and refining hypotheses

Qualitative research can inform the development and refinement of hypotheses for quantitative research by identifying new concepts, variables, or relationships that emerge from the data. This can lead to more focused, relevant, and innovative quantitative research questions and hypotheses. For instance, a qualitative study on employee motivation might uncover the importance of meaningful work and supportive relationships with supervisors as key factors influencing motivation. These findings could then be incorporated into a quantitative study to test the relationships between these factors and employee motivation.

Validating and triangulating findings

Combining qualitative and quantitative research methods can enhance the credibility and trustworthiness of research findings through validation and triangulation. Validation involves comparing the findings from different methods to assess their consistency and convergence, while triangulation involves using multiple methods, data sources, or researchers to gain a more comprehensive understanding of the phenomena under study.

For example, a researcher might use both quantitative surveys and qualitative interviews in a mixed methods research design to assess the effectiveness of a health intervention. If both methods yield similar findings, this can increase confidence in the results. If the findings differ, the researcher can further investigate the reasons for these discrepancies and refine their understanding of the intervention's effectiveness.

Enhancing communication and dissemination

Qualitative research can enhance the communication and dissemination of quantitative research findings by providing vivid narratives, case studies, or examples that bring the data to life and make it more accessible and engaging for diverse audiences, such as policymakers, practitioners, or the public.

For example, a quantitative study on the impact of a community-based program might report the percentage of participants who experienced improvements in various outcomes. By adding qualitative data, such as quotes or stories from participants, the researcher can illustrate the human impact of the program and make the findings more compelling and relatable.

In conclusion, qualitative research can complement and enrich quantitative research in various ways, leading to a more comprehensive, contextualized, and rigorous understanding of complex social phenomena. By integrating qualitative and quantitative research methods, researchers can harness the strengths of both approaches to produce more robust, relevant, and impactful findings that inform theory, policy, and practice.

Qualitative research findings are typically reported in various formats, depending on the audience, purpose, and context of the research. Common ways to report qualitative research include dissertations, journal articles, market research reports, and needs assessment reports. Each format has its own structure and emphasis, tailored to meet the expectations and requirements of its target audience.

Dissertations and theses : Doctoral,master's, or bachelor students often conduct qualitative research as part of their dissertation or thesis projects. In this format, researchers provide a comprehensive account of their research questions , methodology, data collection , data analysis , and findings. Dissertations are expected to make a significant contribution to the existing body of knowledge and demonstrate the researcher's mastery of the subject matter.

Journal articles : Researchers frequently disseminate their qualitative research findings through articles published in academic journals . These articles are typically structured in a way that includes an introduction, literature review, methodology, results, and discussion sections. In addition, articles often undergo a peer-review process before being published in the academic journal. Journal articles focus on communicating the study's purpose, methods, and findings in a concise and coherent manner, providing enough detail for other researchers to evaluate the rigor and validity of the research so that they can cite the article and build on it in their own studies.

Market research reports : Market research often employs qualitative methods to gather insights into consumer behavior, preferences, and attitudes. Market research reports present the findings of these studies to clients, typically businesses or organizations interested in understanding their target audience or market trends. These reports focus on providing actionable insights and recommendations based on the qualitative data, helping clients make informed decisions and develop effective marketing strategies.

Needs assessment reports : Needs assessment is a process used to identify gaps or areas of improvement in a specific context, such as healthcare, education, or social services. Qualitative research methods can be used to collect data on the needs, challenges, and experiences of the target population. Needs assessment reports present the findings of this research, highlighting the identified needs and providing recommendations for addressing them. These reports are used by organizations and policymakers to inform the development and implementation of targeted interventions and policies.

Other formats : In addition to the aforementioned formats, qualitative research findings can also be reported in conference presentations, white papers, policy briefs, blog posts, or multimedia presentations. The choice of format depends on the target audience and the intended purpose of the research, as well as the researcher's preferences and resources. Regardless of the format, it is important for researchers to present their findings in a clear, accurate, and engaging manner, ensuring that their work is accessible and relevant to their audience.

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Collecting and Analyzing Qualitative Data

Brent Wolff, Frank Mahoney, Anna Leena Lohiniva, and Melissa Corkum

• Choosing When to Apply Qualitative Methods
• Commonly Used Qualitative Methods in Field Investigations
• Sampling and Recruitment for Qualitative Research
• Managing, Condensing, Displaying, and Interpreting Qualitative Data
• Coding and Analysis Requirements

Qualitative research methods are a key component of field epidemiologic investigations because they can provide insight into the perceptions, values, opinions, and community norms where investigations are being conducted ( 1,2 ). Open-ended inquiry methods, the mainstay of qualitative interview techniques, are essential in formative research for exploring contextual factors and rationales for risk behaviors that do not fit neatly into predefined categories. For example, during the 2014–2015 Ebola virus disease outbreaks in parts of West Africa, understanding the cultural implications of burial practices within different communities was crucial to designing and monitoring interventions for safe burials ( Box 10.1 ). In program evaluations, qualitative methods can assist the investigator in diagnosing what went right or wrong as part of a process evaluation or in troubleshooting why a program might not be working as well as expected. When designing an intervention, qualitative methods can be useful in exploring dimensions of acceptability to increase the chances of intervention acceptance and success. When performed in conjunction with quantitative studies, qualitative methods can help the investigator confirm, challenge, or deepen the validity of conclusions than either component might have yielded alone ( 1,2 ).

Qualitative research was used extensively in response to the Ebola virus disease outbreaks in parts of West Africa to understand burial practices and to design culturally appropriate strategies to ensure safe burials. Qualitative studies were also used to monitor key aspects of the response.

In October 2014, Liberia experienced an abrupt and steady decrease in case counts and deaths in contrast with predicted disease models of an increased case count. At the time, communities were resistant to entering Ebola treatment centers, raising the possibility that patients were not being referred for care and communities might be conducting occult burials.

To assess what was happening at the community level, the Liberian Emergency Operations Center recruited epidemiologists from the US Department of Health and Human Services/Centers for Disease Control and Prevention and the African Union to investigate the problem.

Teams conducted in-depth interviews and focus group discussions with community leaders, local funeral directors, and coffin makers and learned that communities were not conducting occult burials and that the overall number of burials was less than what they had experienced in previous years. Other key findings included the willingness of funeral directors to cooperate with disease response efforts, the need for training of funeral home workers, and considerable community resistance to cremation practices. These findings prompted the Emergency Operations Center to open a burial ground for Ebola decedents, support enhanced testing of burials in the private sector, and train private-sector funeral workers regarding safe burial practices.

Source: Melissa Corkum, personal communication.

Similar to quantitative approaches, qualitative research seeks answers to specific questions by using rigorous approaches to collecting and compiling information and producing findings that can be applicable beyond the study population. The fundamental difference in approaches lies in how they translate real-life complexities of initial observations into units of analysis. Data collected in qualitative studies typically are in the form of text or visual images, which provide rich sources of insight but also tend to be bulky and time-consuming to code and analyze. Practically speaking, qualitative study designs tend to favor small, purposively selected samples ideal for case studies or in-depth analysis ( 1 ). The combination of purposive sampling and open-ended question formats deprive qualitative study designs of the power to quantify and generalize conclusions, one of the key limitations of this approach.

Qualitative scientists might argue, however, that the generalizability and precision possible through probabilistic sampling and categorical outcomes are achieved at the cost of enhanced validity, nuance, and naturalism that less structured approaches offer ( 3 ). Open-ended techniques are particularly useful for understanding subjective meanings and motivations underlying behavior. They enable investigators to be equally adept at exploring factors observed and unobserved, intentions as well as actions, internal meanings as well as external consequences, options considered but not taken, and unmeasurable as well as measurable outcomes. These methods are important when the source of or solution to a public health problem is rooted in local perceptions rather than objectively measurable characteristics selected by outside observers ( 3 ). Ultimately, such approaches have the ability to go beyond quantifying questions of how much or how many to take on questions of how or why from the perspective and in the words of the study subjects themselves ( 1,2 ).

Another key advantage of qualitative methods for field investigations is their flexibility ( 4 ). Qualitative designs not only enable but also encourage flexibility in the content and flow of questions to challenge and probe for deeper meanings or follow new leads if they lead to deeper understanding of an issue (5). It is not uncommon for topic guides to be adjusted in the course of fieldwork to investigate emerging themes relevant to answering the original study question. As discussed herein, qualitative study designs allow flexibility in sample size to accommodate the need for more or fewer interviews among particular groups to determine the root cause of an issue (see the section on Sampling and Recruitment in Qualitative Research). In the context of field investigations, such methods can be extremely useful for investigating complex or fast-moving situations where the dimensions of analysis cannot be fully anticipated.

Ultimately, the decision whether to include qualitative research in a particular field investigation depends mainly on the nature of the research question itself. Certain types of research topics lend themselves more naturally to qualitative rather than other approaches ( Table 10.1 ). These include exploratory investigations when not enough is known about a problem to formulate a hypothesis or develop a fixed set of questions and answer codes. They include research questions where intentions matter as much as actions and “why?” or “why not?” questions matter as much as precise estimation of measured outcomes. Qualitative approaches also work well when contextual influences, subjective meanings, stigma, or strong social desirability biases lower faith in the validity of responses coming from a relatively impersonal survey questionnaire interview.

The availability of personnel with training and experience in qualitative interviewing or observation is critical for obtaining the best quality data but is not absolutely required for rapid assessment in field settings. Qualitative interviewing requires a broader set of skills than survey interviewing. It is not enough to follow a topic guide like a questionnaire, in order, from top to bottom. A qualitative interviewer must exercise judgment to decide when to probe and when to move on, when to encourage, challenge, or follow relevant leads even if they are not written in the topic guide. Ability to engage with informants, connect ideas during the interview, and think on one’s feet are common characteristics of good qualitative interviewers. By far the most important qualification in conducting qualitative fieldwork is a firm grasp of the research objectives; with this qualification, a member of the research team armed with curiosity and a topic guide can learn on the job with successful results.

Semi-Structured Interviews

Semi-structured interviews can be conducted with single participants (in-depth or individual key informants) or with groups (focus group discussions [FGDs] or key informant groups). These interviews follow a suggested topic guide rather than a fixed questionnaire format. Topic guides typically consist of a limited number ( 10– 15 ) of broad, open-ended questions followed by bulleted points to facilitate optional probing. The conversational back-and-forth nature of a semi-structured format puts the researcher and researched (the interview participants) on more equal footing than allowed by more structured formats. Respondents, the term used in the case of quantitative questionnaire interviews, become informants in the case of individual semi-structured in-depth interviews (IDIs) or participants in the case of FGDs. Freedom to probe beyond initial responses enables interviewers to actively engage with the interviewee to seek clarity, openness, and depth by challenging informants to reach below layers of self-presentation and social desirability. In this respect, interviewing is sometimes compared with peeling an onion, with the first version of events accessible to the public, including survey interviewers, and deeper inner layers accessible to those who invest the time and effort to build rapport and gain trust. (The theory of the active interview suggests that all interviews involve staged social encounters where the interviewee is constantly assessing interviewer intentions and adjusting his or her responses accordingly [ 1 ]. Consequently good rapport is important for any type of interview. Survey formats give interviewers less freedom to divert from the preset script of questions and formal probes.)

Individual In-Depth Interviews and Key-Informant Interviews

The most common forms of individual semi-structured interviews are IDIs and key informant interviews (KIIs). IDIs are conducted among informants typically selected for first-hand experience (e.g., service users, participants, survivors) relevant to the research topic. These are typically conducted as one-on-one face-to-face interviews (two-on-one if translators are needed) to maximize rapport-building and confidentiality. KIIs are similar to IDIs but focus on individual persons with special knowledge or influence (e.g., community leaders or health authorities) that give them broader perspective or deeper insight into the topic area ( Box 10.2 ). Whereas IDIs tend to focus on personal experiences, context, meaning, and implications for informants, KIIs tend to steer away from personal questions in favor of expert insights or community perspectives. IDIs enable flexible sampling strategies and represent the interviewing reference standard for confidentiality, rapport, richness, and contextual detail. However, IDIs are time-and labor-intensive to collect and analyze. Because confidentiality is not a concern in KIIs, these interviews might be conducted as individual or group interviews, as required for the topic area.

Focus Group Discussions and Group Key Informant Interviews

FGDs are semi-structured group interviews in which six to eight participants, homogeneous with respect to a shared experience, behavior, or demographic characteristic, are guided through a topic guide by a trained moderator ( 6 ). (Advice on ideal group interview size varies. The principle is to convene a group large enough to foster an open, lively discussion of the topic, and small enough to ensure all participants stay fully engaged in the process.) Over the course of discussion, the moderator is expected to pose questions, foster group participation, and probe for clarity and depth. Long a staple of market research, focus groups have become a widely used social science technique with broad applications in public health, and they are especially popular as a rapid method for assessing community norms and shared perceptions.

Focus groups have certain useful advantages during field investigations. They are highly adaptable, inexpensive to arrange and conduct, and often enjoyable for participants. Group dynamics effectively tap into collective knowledge and experience to serve as a proxy informant for the community as a whole. They are also capable of recreating a microcosm of social norms where social, moral, and emotional dimensions of topics are allowed to emerge. Skilled moderators can also exploit the tendency of small groups to seek consensus to bring out disagreements that the participants will work to resolve in a way that can lead to deeper understanding. There are also limitations on focus group methods. Lack of confidentiality during group interviews means they should not be used to explore personal experiences of a sensitive nature on ethical grounds. Participants may take it on themselves to volunteer such information, but moderators are generally encouraged to steer the conversation back to general observations to avoid putting pressure on other participants to disclose in a similar way. Similarly, FGDs are subject by design to strong social desirability biases. Qualitative study designs using focus groups sometimes add individual interviews precisely to enable participants to describe personal experiences or personal views that would be difficult or inappropriate to share in a group setting. Focus groups run the risk of producing broad but shallow analyses of issues if groups reach comfortable but superficial consensus around complex topics. This weakness can be countered by training moderators to probe effectively and challenge any consensus that sounds too simplistic or contradictory with prior knowledge. However, FGDs are surprisingly robust against the influence of strongly opinionated participants, highly adaptable, and well suited to application in study designs where systematic comparisons across different groups are called for.

Like FGDs, group KIIs rely on positive chemistry and the stimulating effects of group discussion but aim to gather expert knowledge or oversight on a particular topic rather than lived experience of embedded social actors. Group KIIs have no minimum size requirements and can involve as few as two or three participants.

Egypt’s National Infection Prevention and Control (IPC) program undertook qualitative research to gain an understanding of the contextual behaviors and motivations of healthcare workers in complying with IPC guidelines. The study was undertaken to guide the development of effective behavior change interventions in healthcare settings to improve IPC compliance.

Key informant interviews and focus group discussions were conducted in two governorates among cleaning staff, nursing staff, and physicians in different types of healthcare facilities. The findings highlighted social and cultural barriers to IPC compliance, enabling the IPC program to design responses. For example,

• Informants expressed difficulty in complying with IPC measures that forced them to act outside their normal roles in an ingrained hospital culture. Response: Role models and champions were introduced to help catalyze change.
• Informants described fatalistic attitudes that undermined energy and interest in modifying behavior. Response: Accordingly, interventions affirming institutional commitment to change while challenging fatalistic assumptions were developed.
• Informants did not perceive IPC as effective. Response: Trainings were amended to include scientific evidence justifying IPC practices.
• Informants perceived hygiene as something they took pride in and were judged on. Response: Public recognition of optimal IPC practice was introduced to tap into positive social desirability and professional pride in maintaining hygiene in the work environment.

Qualitative research identified sources of resistance to quality clinical practice in Egypt’s healthcare settings and culturally appropriate responses to overcome that resistance.

____________________ Source: Anna Leena Lohiniva, personal communication.

Visualization Methods

Visualization methods have been developed as a way to enhance participation and empower interviewees relative to researchers during group data collection ( 7 ). Visualization methods involve asking participants to engage in collective problem- solving of challenges expressed through group production of maps, diagrams, or other images. For example, participants from the community might be asked to sketch a map of their community and to highlight features of relevance to the research topic (e.g., access to health facilities or sites of risk concentrations). Body diagramming is another visualization tool in which community members are asked to depict how and where a health threat affects the human body as a way of understanding folk conceptions of health, disease, treatment, and prevention. Ensuing debate and dialogue regarding construction of images can be recorded and analyzed in conjunction with the visual image itself. Visualization exercises were initially designed to accommodate groups the size of entire communities, but they can work equally well with smaller groups corresponding to the size of FGDs or group KIIs.

Selecting a Sample of Study Participants

Fundamental differences between qualitative and quantitative approaches to research emerge most clearly in the practice of sampling and recruitment of study participants. Qualitative samples are typically small and purposive. In-depth interview informants are usually selected on the basis of unique characteristics or personal experiences that make them exemplary for the study, if not typical in other respects. Key informants are selected for their unique knowledge or influence in the study domain. Focus group mobilization often seeks participants who are typical with respect to others in the community having similar exposure or shared characteristics. Often, however, participants in qualitative studies are selected because they are exceptional rather than simply representative. Their value lies not in their generalizability but in their ability to generate insight into the key questions driving the study.

Determining Sample Size

Sample size determination for qualitative studies also follows a different logic than that used for probability sample surveys. For example, whereas some qualitative methods specify ideal ranges of participants that constitute a valid observation (e.g., focus groups), there are no rules on how many observations it takes to attain valid results. In theory, sample size in qualitative designs should be determined by the saturation principle , where interviews are conducted until additional interviews yield no additional insights into the topic of research ( 8 ). Practically speaking, designing a study with a range in number of interviews is advisable for providing a level of flexibility if additional interviews are needed to reach clear conclusions.

Recruiting Study Participants

Recruitment strategies for qualitative studies typically involve some degree of participant self-selection (e.g., advertising in public spaces for interested participants) and purposive selection (e.g., identification of key informants). Purposive selection in community settings often requires authorization from local authorities and assistance from local mobilizers before the informed consent process can begin. Clearly specifying eligibility criteria is crucial for minimizing the tendency of study mobilizers to apply their own filters regarding who reflects the community in the best light. In addition to formal eligibility criteria, character traits (e.g., articulate and interested in participating) and convenience (e.g., not too far away) are legitimate considerations for whom to include in the sample. Accommodations to personality and convenience help to ensure the small number of interviews in a typical qualitative design yields maximum value for minimum investment. This is one reason why random sampling of qualitative informants is not only unnecessary but also potentially counterproductive.

Analysis of qualitative data can be divided into four stages: data management, data condensation, data display, and drawing and verifying conclusions ( 9 ).

Managing Qualitative Data

From the outset, developing a clear organization system for qualitative data is important. Ideally, naming conventions for original data files and subsequent analysis should be recorded in a data dictionary file that includes dates, locations, defining individual or group characteristics, interviewer characteristics, and other defining features. Digital recordings of interviews or visualization products should be reviewed to ensure fidelity of analyzed data to original observations. If ethics agreements require that no names or identifying characteristics be recorded, all individual names must be removed from final transcriptions before analysis begins. If data are analyzed by using textual data analysis software, maintaining careful version control over the data files is crucial, especially when multiple coders are involved.

Condensing Qualitative Data

Condensing refers to the process of selecting, focusing, simplifying, and abstracting the data available at the time of the original observation, then transforming the condensed data into a data set that can be analyzed. In qualitative research, most of the time investment required to complete a study comes after the fieldwork is complete. A single hour of taped individual interview can take a full day to transcribe and additional time to translate if necessary. Group interviews can take even longer because of the difficulty of transcribing active group input. Each stage of data condensation involves multiple decisions that require clear rules and close supervision. A typical challenge is finding the right balance between fidelity to the rhythm and texture of original language and clarity of the translated version in the language of analysis. For example, discussions among groups with little or no education should not emerge after the transcription (and translation) process sounding like university graduates. Judgment must be exercised about which terms should be translated and which terms should be kept in vernacular because there is no appropriate term in English to capture the richness of its meaning.

Displaying Qualitative Data

After the initial condensation, qualitative analysis depends on how the data are displayed. Decisions regarding how data are summarized and laid out to facilitate comparison influence the depth and detail of the investigation’s conclusions. Displays might range from full verbatim transcripts of interviews to bulleted summaries or distilled summaries of interview notes. In a field setting, a useful and commonly used display format is an overview chart in which key themes or research questions are listed in rows in a word processer table or in a spreadsheet and individual informant or group entry characteristics are listed across columns. Overview charts are useful because they allow easy, systematic comparison of results.

Drawing and Verifying Conclusions

Analyzing qualitative data is an iterative and ideally interactive process that leads to rigorous and systematic interpretation of textual or visual data. At least four common steps are involved:

• Reading and rereading. The core of qualitative analysis is careful, systematic, and repeated reading of text to identify consistent themes and interconnections emerging from the data. The act of repeated reading inevitably yields new themes, connections, and deeper meanings from the first reading. Reading the full text of interviews multiple times before subdividing according to coded themes is key to appreciating the full context and flow of each interview before subdividing and extracting coded sections of text for separate analysis.
• Coding. A common technique in qualitative analysis involves developing codes for labeling sections of text for selective retrieval in later stages of analysis and verification. Different approaches can be used for textual coding. One approach, structural coding , follows the structure of the interview guide. Another approach, thematic coding , labels common themes that appear across interviews, whether by design of the topic guide or emerging themes assigned based on further analysis. To avoid the problem of shift and drift in codes across time or multiple coders, qualitative investigators should develop a standard codebook with written definitions and rules about when codes should start and stop. Coding is also an iterative process in which new codes that emerge from repeated reading are layered on top of existing codes. Development and refinement of the codebook is inseparably part of the analysis.
• Analyzing and writing memos. As codes are being developed and refined, answers to the original research question should begin to emerge. Coding can facilitate that process through selective text retrieval during which similarities within and between coding categories can be extracted and compared systematically. Because no p values can be derived in qualitative analyses to mark the transition from tentative to firm conclusions, standard practice is to write memos to record evolving insights and emerging patterns in the data and how they relate to the original research questions. Writing memos is intended to catalyze further thinking about the data, thus initiating new connections that can lead to further coding and deeper understanding.
• Verifying conclusions. Analysis rigor depends as much on the thoroughness of the cross-examination and attempt to find alternative conclusions as on the quality of original conclusions. Cross-examining conclusions can occur in different ways. One way is encouraging regular interaction between analysts to challenge conclusions and pose alternative explanations for the same data. Another way is quizzing the data (i.e., retrieving coded segments by using Boolean logic to systematically compare code contents where they overlap with other codes or informant characteristics). If alternative explanations for initial conclusions are more difficult to justify, confidence in those conclusions is strengthened.

Above all, qualitative data analysis requires sufficient time and immersion in the data. Computer textual software programs can facilitate selective text retrieval and quizzing the data, but discerning patterns and arriving at conclusions can be done only by the analysts. This requirement involves intensive reading and rereading, developing codebooks and coding, discussing and debating, revising codebooks, and recoding as needed until clear patterns emerge from the data. Although quality and depth of analysis is usually proportional to the time invested, a number of techniques, including some mentioned earlier, can be used to expedite analysis under field conditions.

• Detailed notes instead of full transcriptions. Assigning one or two note-takers to an interview can be considered where the time needed for full transcription and translation is not feasible. Even if plans are in place for full transcriptions after fieldwork, asking note-takers to submit organized summary notes is a useful technique for getting real-time feedback on interview content and making adjustments to topic guides or interviewer training as needed.
• Summary overview charts for thematic coding. (See discussion under “Displaying Data.”) If there is limited time for full transcription and/or systematic coding of text interviews using textual analysis software in the field, an overview chart is a useful technique for rapid manual coding.
• Thematic extract files. This is a slightly expanded version of manual thematic coding that is useful when full transcriptions of interviews are available. With use of a word processing program, files can be sectioned according to themes, or separate files can be created for each theme. Relevant extracts from transcripts or analyst notes can be copied and pasted into files or sections of files corresponding to each theme. This is particularly useful for storing appropriate quotes that can be used to illustrate thematic conclusions in final reports or manuscripts.
• Teamwork. Qualitative analysis can be performed by a single analyst, but it is usually beneficial to involve more than one. Qualitative conclusions involve subjective judgment calls. Having more than one coder or analyst working on a project enables more interactive discussion and debate before reaching consensus on conclusions.
• Systematic coding.
• Selective retrieval of coded segments.
• Verifying conclusions (“quizzing the data”).
• Working on larger data sets with multiple separate files.
• Working in teams with multiple coders to allow intercoder reliability to be measured and monitored.

The most widely used software packages (e.g., NVivo [QSR International Pty. Ltd., Melbourne, VIC, Australia] and ATLAS.ti [Scientific Software Development GmbH, Berlin, Germany]) evolved to include sophisticated analytic features covering a wide array of applications but are relatively expensive in terms of license cost and initial investment in time and training. A promising development is the advent of free or low-cost Web-based services (e.g., Dedoose [Sociocultural Research Consultants LLC, Manhattan Beach, CA]) that have many of the same analytic features on a more affordable subscription basis and that enable local research counterparts to remain engaged through the analysis phase (see Teamwork criteria). The start-up costs of computer-assisted analysis need to be weighed against their analytic benefits, which tend to decline with the volume and complexity of data to be analyzed. For rapid situational analyses or small scale qualitative studies (e.g. fewer than 30 observations as an informal rule of thumb), manual coding and analysis using word processing or spreadsheet programs is faster and sufficient to enable rigorous analysis and verification of conclusions.

Qualitative methods belong to a branch of social science inquiry that emphasizes the importance of context, subjective meanings, and motivations in understanding human behavior patterns. Qualitative approaches definitionally rely on open-ended, semistructured, non-numeric strategies for asking questions and recording responses. Conclusions are drawn from systematic visual or textual analysis involving repeated reading, coding, and organizing information into structured and emerging themes. Because textual analysis is relatively time-and skill-intensive, qualitative samples tend to be small and purposively selected to yield the maximum amount of information from the minimum amount of data collection. Although qualitative approaches cannot provide representative or generalizable findings in a statistical sense, they can offer an unparalleled level of detail, nuance, and naturalistic insight into the chosen subject of study. Qualitative methods enable investigators to “hear the voice” of the researched in a way that questionnaire methods, even with the occasional open-ended response option, cannot.

Whether or when to use qualitative methods in field epidemiology studies ultimately depends on the nature of the public health question to be answered. Qualitative approaches make sense when a study question about behavior patterns or program performance leads with why, why not , or how . Similarly, they are appropriate when the answer to the study question depends on understanding the problem from the perspective of social actors in real-life settings or when the object of study cannot be adequately captured, quantified, or categorized through a battery of closed-ended survey questions (e.g., stigma or the foundation of health beliefs). Another justification for qualitative methods occurs when the topic is especially sensitive or subject to strong social desirability biases that require developing trust with the informant and persistent probing to reach the truth. Finally, qualitative methods make sense when the study question is exploratory in nature, where this approach enables the investigator the freedom and flexibility to adjust topic guides and probe beyond the original topic guides.

Given that the conditions just described probably apply more often than not in everyday field epidemiology, it might be surprising that such approaches are not incorporated more routinely into standard epidemiologic training. Part of the answer might have to do with the subjective element in qualitative sampling and analysis that seems at odds with core scientific values of objectivity. Part of it might have to do with the skill requirements for good qualitative interviewing, which are generally more difficult to find than those required for routine survey interviewing.

For the field epidemiologist unfamiliar with qualitative study design, it is important to emphasize that obtaining important insights from applying basic approaches is possible, even without a seasoned team of qualitative researchers on hand to do the work. The flexibility of qualitative methods also tends to make them forgiving with practice and persistence. Beyond the required study approvals and ethical clearances, the basic essential requirements for collecting qualitative data in field settings start with an interviewer having a strong command of the research question, basic interactive and language skills, and a healthy sense of curiosity, armed with a simple open-ended topic guide and a tape recorder or note-taker to capture the key points of the discussion. Readily available manuals on qualitative study design, methods, and analysis can provide additional guidance to improve the quality of data collection and analysis.

• Patton MQ. Qualitative research and evaluation methods: integrating theory and practice . 4th ed. Thousand Oaks, CA: Sage; 2015.
• Hennink M, Hutter I, Bailey A. Qualitative research methods . Thousand Oaks, CA: Sage; 2010.
• Lincoln YS, Guba EG. The constructivist credo . Walnut Creek, CA: Left Coast Press; 2013.
• Mack N, Woodsong C, MacQueen KM, Guest G, Namey E. Qualitative research methods: a data collectors field guide. https://www.fhi360.org/sites/default/files/media/documents/Qualitative%20Research%20Methods%20-%20A%20Data%20Collector%27s%20Field%20Guide.pdf
• Kvale S, Brinkmann S. Interviews: learning the craft of qualitative research . Thousand Oaks, CA: Sage; 2009:230–43.
• Krueger RA, Casey MA. Focus groups: a practical guide for applied research . Thousand Oaks, CA: Sage; 2014.
• Margolis E, Pauwels L. The Sage handbook of visual research methods . Thousand Oaks, CA: Sage; 2011.
• Mason M. Sample size and saturation in PhD studies using qualitative interviews. Forum : Qualitative Social Research/Sozialforschung. 2010;11(3).
• Miles MB, Huberman AM, Saldana J. Qualitative data analysis: a methods sourcebook . 3rd ed. Thousand Oaks, CA: Sage; 2014.
• Silver C, Lewins A. Using software in qualitative research: a step-by-step guide . Thousand Oaks, CA; Sage: 2014.

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Home » Data Collection – Methods Types and Examples

Data Collection – Methods Types and Examples

Table of Contents

Data Collection

Definition:

Data collection is the process of gathering and collecting information from various sources to analyze and make informed decisions based on the data collected. This can involve various methods, such as surveys, interviews, experiments, and observation.

In order for data collection to be effective, it is important to have a clear understanding of what data is needed and what the purpose of the data collection is. This can involve identifying the population or sample being studied, determining the variables to be measured, and selecting appropriate methods for collecting and recording data.

Types of Data Collection

Types of Data Collection are as follows:

Primary Data Collection

Primary data collection is the process of gathering original and firsthand information directly from the source or target population. This type of data collection involves collecting data that has not been previously gathered, recorded, or published. Primary data can be collected through various methods such as surveys, interviews, observations, experiments, and focus groups. The data collected is usually specific to the research question or objective and can provide valuable insights that cannot be obtained from secondary data sources. Primary data collection is often used in market research, social research, and scientific research.

Secondary Data Collection

Secondary data collection is the process of gathering information from existing sources that have already been collected and analyzed by someone else, rather than conducting new research to collect primary data. Secondary data can be collected from various sources, such as published reports, books, journals, newspapers, websites, government publications, and other documents.

Qualitative Data Collection

Qualitative data collection is used to gather non-numerical data such as opinions, experiences, perceptions, and feelings, through techniques such as interviews, focus groups, observations, and document analysis. It seeks to understand the deeper meaning and context of a phenomenon or situation and is often used in social sciences, psychology, and humanities. Qualitative data collection methods allow for a more in-depth and holistic exploration of research questions and can provide rich and nuanced insights into human behavior and experiences.

Quantitative Data Collection

Quantitative data collection is a used to gather numerical data that can be analyzed using statistical methods. This data is typically collected through surveys, experiments, and other structured data collection methods. Quantitative data collection seeks to quantify and measure variables, such as behaviors, attitudes, and opinions, in a systematic and objective way. This data is often used to test hypotheses, identify patterns, and establish correlations between variables. Quantitative data collection methods allow for precise measurement and generalization of findings to a larger population. It is commonly used in fields such as economics, psychology, and natural sciences.

Data Collection Methods

Data Collection Methods are as follows:

Surveys involve asking questions to a sample of individuals or organizations to collect data. Surveys can be conducted in person, over the phone, or online.

Interviews involve a one-on-one conversation between the interviewer and the respondent. Interviews can be structured or unstructured and can be conducted in person or over the phone.

Focus Groups

Focus groups are group discussions that are moderated by a facilitator. Focus groups are used to collect qualitative data on a specific topic.

Observation

Observation involves watching and recording the behavior of people, objects, or events in their natural setting. Observation can be done overtly or covertly, depending on the research question.

Experiments

Experiments involve manipulating one or more variables and observing the effect on another variable. Experiments are commonly used in scientific research.

Case Studies

Case studies involve in-depth analysis of a single individual, organization, or event. Case studies are used to gain detailed information about a specific phenomenon.

Secondary Data Analysis

Secondary data analysis involves using existing data that was collected for another purpose. Secondary data can come from various sources, such as government agencies, academic institutions, or private companies.

How to Collect Data

The following are some steps to consider when collecting data:

• Define the objective : Before you start collecting data, you need to define the objective of the study. This will help you determine what data you need to collect and how to collect it.
• Identify the data sources : Identify the sources of data that will help you achieve your objective. These sources can be primary sources, such as surveys, interviews, and observations, or secondary sources, such as books, articles, and databases.
• Determine the data collection method : Once you have identified the data sources, you need to determine the data collection method. This could be through online surveys, phone interviews, or face-to-face meetings.
• Develop a data collection plan : Develop a plan that outlines the steps you will take to collect the data. This plan should include the timeline, the tools and equipment needed, and the personnel involved.
• Test the data collection process: Before you start collecting data, test the data collection process to ensure that it is effective and efficient.
• Collect the data: Collect the data according to the plan you developed in step 4. Make sure you record the data accurately and consistently.
• Analyze the data: Once you have collected the data, analyze it to draw conclusions and make recommendations.
• Report the findings: Report the findings of your data analysis to the relevant stakeholders. This could be in the form of a report, a presentation, or a publication.
• Monitor and evaluate the data collection process: After the data collection process is complete, monitor and evaluate the process to identify areas for improvement in future data collection efforts.
• Ensure data quality: Ensure that the collected data is of high quality and free from errors. This can be achieved by validating the data for accuracy, completeness, and consistency.
• Maintain data security: Ensure that the collected data is secure and protected from unauthorized access or disclosure. This can be achieved by implementing data security protocols and using secure storage and transmission methods.
• Follow ethical considerations: Follow ethical considerations when collecting data, such as obtaining informed consent from participants, protecting their privacy and confidentiality, and ensuring that the research does not cause harm to participants.
• Use appropriate data analysis methods : Use appropriate data analysis methods based on the type of data collected and the research objectives. This could include statistical analysis, qualitative analysis, or a combination of both.
• Record and store data properly: Record and store the collected data properly, in a structured and organized format. This will make it easier to retrieve and use the data in future research or analysis.
• Collaborate with other stakeholders : Collaborate with other stakeholders, such as colleagues, experts, or community members, to ensure that the data collected is relevant and useful for the intended purpose.

Applications of Data Collection

Data collection methods are widely used in different fields, including social sciences, healthcare, business, education, and more. Here are some examples of how data collection methods are used in different fields:

• Social sciences : Social scientists often use surveys, questionnaires, and interviews to collect data from individuals or groups. They may also use observation to collect data on social behaviors and interactions. This data is often used to study topics such as human behavior, attitudes, and beliefs.
• Healthcare : Data collection methods are used in healthcare to monitor patient health and track treatment outcomes. Electronic health records and medical charts are commonly used to collect data on patients’ medical history, diagnoses, and treatments. Researchers may also use clinical trials and surveys to collect data on the effectiveness of different treatments.
• Business : Businesses use data collection methods to gather information on consumer behavior, market trends, and competitor activity. They may collect data through customer surveys, sales reports, and market research studies. This data is used to inform business decisions, develop marketing strategies, and improve products and services.
• Education : In education, data collection methods are used to assess student performance and measure the effectiveness of teaching methods. Standardized tests, quizzes, and exams are commonly used to collect data on student learning outcomes. Teachers may also use classroom observation and student feedback to gather data on teaching effectiveness.
• Agriculture : Farmers use data collection methods to monitor crop growth and health. Sensors and remote sensing technology can be used to collect data on soil moisture, temperature, and nutrient levels. This data is used to optimize crop yields and minimize waste.
• Environmental sciences : Environmental scientists use data collection methods to monitor air and water quality, track climate patterns, and measure the impact of human activity on the environment. They may use sensors, satellite imagery, and laboratory analysis to collect data on environmental factors.
• Transportation : Transportation companies use data collection methods to track vehicle performance, optimize routes, and improve safety. GPS systems, on-board sensors, and other tracking technologies are used to collect data on vehicle speed, fuel consumption, and driver behavior.

Examples of Data Collection

Examples of Data Collection are as follows:

• Traffic Monitoring: Cities collect real-time data on traffic patterns and congestion through sensors on roads and cameras at intersections. This information can be used to optimize traffic flow and improve safety.
• Social Media Monitoring : Companies can collect real-time data on social media platforms such as Twitter and Facebook to monitor their brand reputation, track customer sentiment, and respond to customer inquiries and complaints in real-time.
• Weather Monitoring: Weather agencies collect real-time data on temperature, humidity, air pressure, and precipitation through weather stations and satellites. This information is used to provide accurate weather forecasts and warnings.
• Stock Market Monitoring : Financial institutions collect real-time data on stock prices, trading volumes, and other market indicators to make informed investment decisions and respond to market fluctuations in real-time.
• Health Monitoring : Medical devices such as wearable fitness trackers and smartwatches can collect real-time data on a person’s heart rate, blood pressure, and other vital signs. This information can be used to monitor health conditions and detect early warning signs of health issues.

Purpose of Data Collection

The purpose of data collection can vary depending on the context and goals of the study, but generally, it serves to:

• Provide information: Data collection provides information about a particular phenomenon or behavior that can be used to better understand it.
• Measure progress : Data collection can be used to measure the effectiveness of interventions or programs designed to address a particular issue or problem.
• Support decision-making : Data collection provides decision-makers with evidence-based information that can be used to inform policies, strategies, and actions.
• Identify trends : Data collection can help identify trends and patterns over time that may indicate changes in behaviors or outcomes.
• Monitor and evaluate : Data collection can be used to monitor and evaluate the implementation and impact of policies, programs, and initiatives.

When to use Data Collection

Data collection is used when there is a need to gather information or data on a specific topic or phenomenon. It is typically used in research, evaluation, and monitoring and is important for making informed decisions and improving outcomes.

Data collection is particularly useful in the following scenarios:

• Research : When conducting research, data collection is used to gather information on variables of interest to answer research questions and test hypotheses.
• Evaluation : Data collection is used in program evaluation to assess the effectiveness of programs or interventions, and to identify areas for improvement.
• Monitoring : Data collection is used in monitoring to track progress towards achieving goals or targets, and to identify any areas that require attention.
• Decision-making: Data collection is used to provide decision-makers with information that can be used to inform policies, strategies, and actions.
• Quality improvement : Data collection is used in quality improvement efforts to identify areas where improvements can be made and to measure progress towards achieving goals.

Characteristics of Data Collection

Data collection can be characterized by several important characteristics that help to ensure the quality and accuracy of the data gathered. These characteristics include:

• Validity : Validity refers to the accuracy and relevance of the data collected in relation to the research question or objective.
• Reliability : Reliability refers to the consistency and stability of the data collection process, ensuring that the results obtained are consistent over time and across different contexts.
• Objectivity : Objectivity refers to the impartiality of the data collection process, ensuring that the data collected is not influenced by the biases or personal opinions of the data collector.
• Precision : Precision refers to the degree of accuracy and detail in the data collected, ensuring that the data is specific and accurate enough to answer the research question or objective.
• Timeliness : Timeliness refers to the efficiency and speed with which the data is collected, ensuring that the data is collected in a timely manner to meet the needs of the research or evaluation.
• Ethical considerations : Ethical considerations refer to the ethical principles that must be followed when collecting data, such as ensuring confidentiality and obtaining informed consent from participants.

Advantages of Data Collection

There are several advantages of data collection that make it an important process in research, evaluation, and monitoring. These advantages include:

• Better decision-making : Data collection provides decision-makers with evidence-based information that can be used to inform policies, strategies, and actions, leading to better decision-making.
• Improved understanding: Data collection helps to improve our understanding of a particular phenomenon or behavior by providing empirical evidence that can be analyzed and interpreted.
• Evaluation of interventions: Data collection is essential in evaluating the effectiveness of interventions or programs designed to address a particular issue or problem.
• Identifying trends and patterns: Data collection can help identify trends and patterns over time that may indicate changes in behaviors or outcomes.
• Increased accountability: Data collection increases accountability by providing evidence that can be used to monitor and evaluate the implementation and impact of policies, programs, and initiatives.
• Validation of theories: Data collection can be used to test hypotheses and validate theories, leading to a better understanding of the phenomenon being studied.
• Improved quality: Data collection is used in quality improvement efforts to identify areas where improvements can be made and to measure progress towards achieving goals.

Limitations of Data Collection

While data collection has several advantages, it also has some limitations that must be considered. These limitations include:

• Bias : Data collection can be influenced by the biases and personal opinions of the data collector, which can lead to inaccurate or misleading results.
• Sampling bias : Data collection may not be representative of the entire population, resulting in sampling bias and inaccurate results.
• Cost : Data collection can be expensive and time-consuming, particularly for large-scale studies.
• Limited scope: Data collection is limited to the variables being measured, which may not capture the entire picture or context of the phenomenon being studied.
• Ethical considerations : Data collection must follow ethical principles to protect the rights and confidentiality of the participants, which can limit the type of data that can be collected.
• Data quality issues: Data collection may result in data quality issues such as missing or incomplete data, measurement errors, and inconsistencies.
• Limited generalizability : Data collection may not be generalizable to other contexts or populations, limiting the generalizability of the findings.

About the author

Muhammad Hassan

Researcher, Academic Writer, Web developer

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Manage Your Research Data: Qualitative Data

• Data Management Plans
• Using the DMPTool
• FERPA & HIPAA
• File Formats & Naming
• Qualitative Data
• Data Analysis Software
• Why Share Data?
• Copyright & Intellectual Property
• Additional Assistance

What is Qualitative Data?

Research data is any information collected, created, or examined to produce original research results. This includes qualitative data, which is material gathered for textual, conceptual, or qualitative studies. Examples of qualitative data may include:

• audio or text files from interviews, focus groups, surveys, oral histories
• image or video files of people, animals, or scenes
• direct observations, such as field notes
• written documents, such as books, news articles and webpages

Unlike quantitative data (codes, tabular data, observational data), Qualitative data is not reproducible. 

Which Qualitative Data Should be Kept & Shared?

The value of your data comes from 1) its usefulness for other researchers to explore and 2) its archival or historical value for future generations. When deciding what to keep, ask yourself:

• Are there other copies?
• Could someone approximate your conclusions based on what you’ve written or recorded?
• What ethical or legal guidelines has your funding agency, IRB, or discipline provided?

But what about confidentiality?

It’s a big deal. Science is moving towards full sharing of data, but there are exceptions for:

• Sensitive data: names, dates, locations, and sensitive topics can all be obscured or removed.
• Ethics: what did you promise in an IRB application, or directly to your participants?
• Disclosure risk: the risk of a break-in goes up the more you store or share files digitally.

Qualitative Data Management Tutorial

• Managing Qualitative Social Science Data An interactive online course This interactive on-line course includes four modules, each with multiple lessons. Together they constitute a complete course on managing qualitative data; each lesson is also designed to function as a stand-alone resource that can be completed individually.

More Discussion on Qualitative Data

• Ethnographic Field Data 2: When Not-Sharing is Caring Celia Emmelhainz, writing for Savage Minds, discusses the ethical dilemmas of sharing qualitative data, and increasing security for ethnographic data.
• Protecting Respondent Confidentiality in Qualitative Research Abstract: For qualitative researchers, maintaining respondent confidentiality while presenting rich, detailed accounts of social life presents unique challenges. These challenges are not adequately addressed in the literature on research ethics and research methods. Using an example from a study of breast cancer survivors, I argue that by carefully considering the audience for one’s research and by re-envisioning the informed consent process, qualitative researchers can avoid confidentiality dilemmas that might otherwise lead them not to report rich, detailed data.

Content for this page has been adapted from Managing and Sharing Qualitative Research Data 101 , with permission from Celia Emmelhainz, Anthropology and Qualitative Research Librarian, UC Berkeley.

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Saturation in qualitative research: exploring its conceptualization and operationalization

Benjamin saunders.

1 Institute for Primary Care and Health Sciences, Keele University, Keele, Staffordshire ST5 5BG UK

Tom Kingstone

Shula baker, jackie waterfield.

2 School of Health Sciences, Queen Margaret University, Edinburgh, EH21 6UU UK

Bernadette Bartlam

Heather burroughs, clare jinks.

Saturation has attained widespread acceptance as a methodological principle in qualitative research. It is commonly taken to indicate that, on the basis of the data that have been collected or analysed hitherto, further data collection and/or analysis are unnecessary. However, there appears to be uncertainty as to how saturation should be conceptualized, and inconsistencies in its use. In this paper, we look to clarify the nature, purposes and uses of saturation, and in doing so add to theoretical debate on the role of saturation across different methodologies. We identify four distinct approaches to saturation, which differ in terms of the extent to which an inductive or a deductive logic is adopted, and the relative emphasis on data collection, data analysis, and theorizing. We explore the purposes saturation might serve in relation to these different approaches, and the implications for how and when saturation will be sought. In examining these issues, we highlight the uncertain logic underlying saturation—as essentially a predictive statement about the unobserved based on the observed, a judgement that, we argue, results in equivocation, and may in part explain the confusion surrounding its use. We conclude that saturation should be operationalized in a way that is consistent with the research question(s), and the theoretical position and analytic framework adopted, but also that there should be some limit to its scope, so as not to risk saturation losing its coherence and potency if its conceptualization and uses are stretched too widely.

Introduction

In broad terms, saturation is used in qualitative research as a criterion for discontinuing data collection and/or analysis. 1 Its origins lie in grounded theory (Glaser and Strauss 1967 ), but in one form or another it now commands acceptance across a range of approaches to qualitative research. Indeed, saturation is often proposed as an essential methodological element within such work. Fusch and Ness ( 2015 : p. 1408) claim categorically that ‘failure to reach saturation has an impact on the quality of the research conducted’; 2 Morse ( 2015 : p. 587) notes that saturation is ‘the most frequently touted guarantee of qualitative rigor offered by authors’; and Guest et al. ( 2006 : p. 60) refer to it as having become ‘the gold standard by which purposive sample sizes are determined in health science research.’ A number of authors refer to saturation as a ‘rule’ (Denny 2009 ; Sparkes et al. 2011 ), or an ‘edict’ (Morse 1995 ), of qualitative research, and it features in a number of generic quality criteria for qualitative methods (Leininger 1994 ; Morse et al. 2002 ).

However, despite having apparently attained something of the status of orthodoxy, saturation is defined within the literature in varying ways—or is sometimes undefined—and raises a number of problematic conceptual and methodological issues (Dey 1999 ; Bowen 2008 ; O’Reilly and Parker 2013 ). Drawing on a number of examples in the literature, this paper seeks to explore some of these issues in relation to three core questions:

‘What?’—in what way(s) is saturation defined?

‘where and why’—in what types of qualitative research, and for what purpose, should saturation be sought, ‘when and how’—at what stage in the research is saturation sought, and how can we assess if it has been achieved.

In addressing these questions, we will explore the implications of different models of saturation—and the theoretical and methodological assumptions that underpin them—for the varying purposes saturation may serve across different qualitative approaches. In doing so, the paper will contribute to the small but growing literature that has critically examined the concept of saturation (e.g. Bowen 2008 ; O’Reilly and Parker 2013 ; Walker 2012 ; Morse 2015 ; Nelson 2016 ), aiming to extend the discussion around its conceptualization and use. We will argue not only for greater transparency in the reporting of saturation, as others have done (Bowen 2008 ; Francis et al. 2010 ), but also for a more thorough consideration on the part of qualitative researchers regarding how saturation relates to the research question(s) they are addressing, in addition to the theoretical and analytical approach they have adopted, with due recognition of potential inconsistencies and contradictions in its use.

In their original treatise on grounded theory, Glaser and Strauss ( 1967 : p. 61) defined saturation in these terms:

The criterion for judging when to stop sampling the different groups pertinent to a category is the category’s theoretical saturation . Saturation means that no additional data are being found whereby the sociologist can develop properties of the category. As he sees similar instances over and over again, the researcher becomes empirically confident that a category is saturated. He goes out of his way to look for groups that stretch diversity of data as far as possible, just to make certain that saturation is based on the widest possible range of data on the category.

Here, the decision to be made relates to further sampling, and the determinant of adequate sampling has to do with the degree of development of a theoretical category in the process of analysis. Saturation is therefore closely related to the notion of theoretical sampling—the idea that sampling is guided by ‘the necessary similarities and contrasts required by the emerging theory’ (Dey 1999 : p. 30)—and causes the researcher to ‘combine sampling, data collection and data analysis, rather than treating them as separate stages in a linear process’ (Bryman 2012 : p. 18).

Also writing from a grounded theory standpoint, Urquhart ( 2013 : p. 194) defines saturation as: ‘the point in coding when you find that no new codes occur in the data. There are mounting instances of the same codes, but no new ones’, whilst Given ( 2016 : p. 135) considers saturation as the point at which ‘additional data do not lead to any new emergent themes’. A similar position regarding the (non)emergence of new codes or themes has been taken by others (e.g. Birks and Mills 2015 ; Olshansky 2015 ). 3 These definitions show a change of emphasis, and suggest a second model of saturation. Whilst the focus remains at the level of analysis, the decision to be made appears to relate to the emergence of new codes or themes, rather than the degree of development of those already identified. Moreover, Urqhart ( 2013 ) and Birks and Mills ( 2015 ) relate saturation primarily to the termination of analysis, rather than to the collection of new data.

According to Starks and Trinidad ( 2007 : p. 1375), however, theoretical saturation occurs ‘when the complete range of constructs that make up the theory is fully represented by the data’. Whilst not wholly explicit, this definition suggests a third model of saturation with a different directional logic: not ‘given the data, do we have analytical or theoretical adequacy?’, but ‘given the theory, do we have sufficient data to illustrate it?’ 4

If we move outside the grounded theory literature, 5 a fourth perspective becomes apparent in which there are references to data saturation, rather than theoretical saturation (e.g. Fusch and Ness 2015 ). 6 This view of saturation seems to centre on the question of how much data (usually number of interviews) is needed until nothing new is apparent, or what Sandelowski ( 2008 : p. 875) calls ‘informational redundancy’ (e.g. Francis et al. 2010 ; Guest et al. 2006 ). Grady ( 1998 : p. 26) provides a similar description of data saturation as the point at which:

New data tend to be redundant of data already collected. In interviews, when the researcher begins to hear the same comments again and again, data saturation is being reached… It is then time to stop collecting information and to start analysing what has been collected.

Whilst several others have defined data saturation in a similar way (e.g. Hill et al. 2014 : p. 2; Middlemiss et al. 2015 ; Jackson et al. 2015 ), Legard et al. ( 2003 ) seem to adopt a narrower, more individual-oriented perspective on data saturation, whereby saturation operates not at the level of the dataset as a whole, but in relation to the data provided by an individual participant; i.e. it is achieved at a particular point within a specific interview:

Probing needs to continue until the researcher feels they have reached saturation, a full understanding of the participant’s perspective (Legard et al. 2003 : p. 152).

From this perspective, the researcher’s response to the data—through which decisions are made about whether or not any new ‘information’ is being generated—is not necessarily perceived as forming part of the analysis itself. Thus, in this model, the process of saturation is located principally at the level of data collection and is thereby separated from a fuller process of data analysis, and hence from theory.

Four different models of saturation seem therefore to exist (Table  1 ). The first of these, rooted in traditional grounded theory, uses the development of categories and the emerging theory in the analysis process as the criterion for additional data collection, driven by the notion of theoretical sampling; using a term in common use, but with a more specific definitional focus, this model could thus be labelled as theoretical saturation . The second model takes a similar approach, but saturation focuses on the identification of new codes or themes, and is based on the number of such codes or themes rather than the completeness of existing theoretical categories. This can be termed inductive thematic saturation . In this model, saturation appears confined to the level of analysis; its implication for data collection is at best implicit. In the third model, a reversal of the preceding logic is suggested, whereby data is collected so as to exemplify theory, at the level of lower-order codes or themes, rather than to develop or refine theory. This model can be termed a priori thematic saturation , as it points to the idea of pre-determined theoretical categories and leads us away from the inductive logic characteristic of grounded theory. Finally, the fourth model—which, again aligning with the term already in common use, we will refer to as data saturation —sees saturation as a matter of identifying redundancy in the data, with no necessary reference to the theory linked to these data; saturation appears to be distinct from formal data analysis.

Table 1

Models of saturation and their principal foci in the research process

‘Hybrid’ forms of saturation

Some authors appear to espouse interpretations of saturation that combine two or more of the models defined above, making its conceptualization less distinct. For example, Goulding ( 2005 ) suggests that both data and theory should be saturated within grounded theory, and Drisko ( 1997 : p. 192) defines saturation in terms of ‘the comprehensiveness of both the data collection and analysis’. Similarly, Morse’s view of saturation seems to embody elements of both theoretical and data saturation. She links saturation with the idea of replication, in a way that suggests a process of data saturation:

However, when the domain has been fully sampled – when all data have been collected – then replication of data occurs and, with this replication… the signal of saturation (Morse 1995 : p. 148).

Morse notes elsewhere that she is able to tell when her students have achieved saturation, as they begin to talk about the data in more generalized terms and ‘can readily supply examples when asked. These students know their data’ (Morse 2015 : p. 588). This too suggests a form of data saturation. However, Morse also proposes that saturation is lacking when ‘there are too few examples in each category to identify the characteristics of concepts, and to develop theory’ (Morse 2015 : p. 588). This perspective seems to be located firmly in the idea of theory development (as other parts of the quoted papers by Morse make clear), though a heavy emphasis is placed at the level of the data and the way in which the data exemplify theory, thereby seeming to evoke both data and theoretical saturation.

Hennink et al. ( 2017 ) go further, appearing to combine elements of all four models of saturation. They firstly identify ‘code saturation’, the point at which ‘no additional issues are identified and the codebook begins to stabilize’ ( 2017 : p. 4), which seems to combine elements of both inductive thematic saturation and data saturation. However, within this approach saturation is discussed as relating not only to codes developed inductively, but also to a priori codes, which echoes the third model: a priori thematic saturation. They go on to distinguish ‘code saturation’ from ‘meaning saturation’; in the latter, the analyst attempts to ‘fully understand conceptual codes or the conceptual dimensions of… concrete codes’ ( 2017 : p. 14). This focus on saturating the dimensions of codes seems more akin to theoretical saturation; however, their analysis remains at the level of codes, rather than theoretical categories developed from these codes, and Hennink et al. explicitly position their approach outside grounded theory methods.

Morse ( 2015 : p. 587) takes the view that saturation is ‘present in all qualitative research’ and as previously noted, it is commonly considered as the ‘gold standard’ for determining sample size in qualitative research, with little distinction between different types of qualitative research. We question this perspective, and would instead argue—as is suggested by the different models of saturation considered in the previous section—that saturation has differing relevance, and a different meaning, depending on the role of theory, a viewpoint somewhat supported by other commentators who have questioned its application across the spectrum of qualitative methods (Walker 2012 ; O’Reilly and Parker 2013 ; van Manen et al. 2016 ).

In a largely deductive approach (i.e. one that relies wholly or predominantly on applying pre-identified codes, themes or other analytical categories to the data, rather than allowing these to emerge inductively) saturation may refer to the extent to which pre-determined codes or themes are adequately represented in the data—rather like the idea of the categories being sufficiently replete with instances, or ‘examples’, of data, as suggested in the a priori thematic saturation model outlined above. Thus, in their attempt to establish an adequate sample size for saturation, Francis et al. ( 2010 ) refer explicitly to research in which conceptual categories have been pre-established through existing theory, and it is significant in this respect that they link saturation with the notion of content validity. In contrast, within a more inductive approach (e.g. the inductive thematic saturation and theoretical saturation models outlined above), saturation suggests the extent to which ‘new’ codes or themes are identified within the data, and/or the extent to which new theoretical insights are gained from the data via this process.

In both the deductive and the inductive approach, we can make sense of the role of saturation, however much it differs in each case, because the underlying approach to analysis is essentially thematic, and usually occurs in the context of interview or focus group studies involving a number of informants. It is less straightforward to identify a role for saturation in qualitative approaches that are based on a biographical or narrative approach to analysis, or that, more generally, include a specific focus on accounts of individual informants (e.g. interpretative phenomenological analysis). In such studies, analysis tends to focus more on strands within individual accounts rather than on analytical themes ; these strands are essentially continuous, whereas themes are essentially recurrent. Accordingly, Marshall and Long ( 2010 ) suggest that saturation was not appropriate in their study of maternal coping processes, based on narrative methods. Elsewhere, however, a less straightforward picture emerges. Hawkins and Abrams ( 2007 ) utilized saturation in the context of a study based on life-history interviews with 39 formerly homeless mentally ill men and women. The authors state: ‘Of the 39 participants, six did not complete a second interview because they were unavailable, impaired, or the research team felt the first interview had achieved saturation’ (p. 2035), suggesting that judgments of saturation were made within each participant’s account. Power et al. ( 2015 ) adopted a story-telling approach to women’s experience of post-partum hospitalization, and recruitment continued until data saturation, which was established through ‘the repetition of responses’ (p. 372). Analysis was thematic, and it is not clear whether saturation was determined in relation to themes across participants’ stories, or within individual stories. Similarly, in a study of osteoarthritis in footballers, based on interpretative phenomenological analysis, Turner et al. ( 2002 ) employed saturation, which was defined both in terms of the emergence of themes from the analysis and a ‘consensus across views expressed’ (p. 298), which suggests that, notwithstanding the interpretive phenomenological analysis perspective adopted, saturation was sought more across than within cases. Hale et al. ( 2007 : p. 91) argue, however, that saturation is not normally an aim in interpretative phenomenological analysis, owing to the concern to obtain ‘full and rich personal accounts’, which highlights the particular analytical focus within individual accounts in this approach, and van Manen dissociates saturation from phenomenological research more generally (van Manen et al. 2016 ).

Considering the various types of research in which saturation might feature helps to clarify the purposes it is intended to fulfil. When used in a deductive approach to analysis, saturation serves to demonstrate the extent to which the data instantiate previously determined conceptual categories, whereas in more inductive approaches, and grounded theory in particular, it says something about the adequacy of sampling in relation to theory development (although we have seen that there are differing accounts of how specifically this should be achieved). In narrative research, a role for saturation is harder to discern. Rather than the sufficient development of theory, it might be seen to indicate the ‘completeness’ of a biographical account. However, one could question whether the point at which a participant’s story is interpreted as being ‘complete’—having presumably conveyed everything seen to be relevant to the focus of the study—is, in fact, usefully described by the concept of saturation, given the distance that this moves us away from the operationalization of saturation in broadly thematic approaches. This might, furthermore, lead us to ask whether there is the risk of saturation losing its coherence and utility if its potential conceptualization and uses are stretched too widely.

The same issue is relevant with regard to a number of other, less obvious, purposes that have been proposed for saturation. For example, it has been claimed to demonstrate the trustworthiness of coding (Damschroder et al. 2007 )—but as saturation will be a direct and automatic consequence of one’s coding decisions, it is not clear how it can be an independent measure of their quality. Dubé et al. ( 2016 ) suggest that saturation says something about (though not conclusively) the ability to extrapolate findings, and Boddy ( 2016 : p. 428) claims that ‘once saturation is reached, the results must be capable of some degree of generalisation’; this seems to move us away from the notion of the theoretical adequacy of an analysis, and the explanatory scope of a theory, toward a much more empirical sense of generalizability. The use of saturation in these two cases could perhaps indicate a degree of confusion in some studies about the meaning of saturation and its purpose, even when taking into account the differing models of saturation outlined earlier. Therefore, we would suggest that for saturation to be conceptually meaningful and practically useful there should be some limit to the purposes to which it can be applied.

Perspectives taken on saturation

The perspective taken on what is meant by saturation within a given study will have implications for when it will be sought. Taking the fourth model of saturation identified earlier—the data saturation approach, as based on the notion of informational redundancy—it is clear that saturation can be identified at an early stage in the process, as from this perspective saturation is often seen as separate from, and preceding, formal analysis. Decisions about when further data collection is unnecessary are commonly based on the researcher’s sense of what they are hearing within interviews, and this decision can therefore be made prior to coding and category development. In a focus group study of HIV perceptions in Ghana, Ganle ( 2016 ) used the notion of saturation to determine when each focus group discussion should terminate. Such a decision would seem, however, to relate to only a very preliminary stage of analysis and is likely to be driven by only a rudimentary sense of any emergent theory. A similar point can be made in relation to Hancock el al.’s ( 2016 ) study of male nurses’ views on selecting a nursing speciality. They talk of logging each instance in which their focus group participants ‘discussed a theme’, with saturation then judged in relation to the number of times themes were discussed. Though not elaborated upon, this appears to imply a very narrow definition of a theme as something that can be somehow ‘observed’ during the course of a focus group. However, interpretations at this stage regarding what might constitute a theme, before even beginning to consider whether identified themes are saturated, will be superficial at best. Moreover, conclusions reached at this stage may not be particularly informative as regards subsequent theory development—pieces of data that appear to be very similar when first considered may be found to exemplify different theoretical constructs on detailed analysis, and correspondingly, data that are empirically dissimilar may turn out to have much in common theoretically. Judgments at this stage will also relate to a framework of themes and categories that is theoretically immature, and that may be subject to considerable modification; for example, the changes that may occur during the successive stages of open, selective and theoretical coding in grounded theory (Glaser 1978 ).

With regard to the second model identified, inductive thematic saturation, the fact that the focus is more explicitly on reaching saturation at the level of analysis—i.e. in relation to the (non-)emergence of new codes or themes—might suggest it will be achieved at a later stage than in data saturation approaches (notwithstanding the concurrent nature of data-collection and analysis in many qualitative approaches). However, focusing on the emergence or otherwise of codes rather than on their theoretical development still points us towards saturation being achieved at a relatively early stage. Hennink et al. ( 2017 ) highlight this in a study on patient retention in HIV care, in which they found that saturation of codes was achieved at an earlier point than saturation of the ‘dimensions, nuances, or insights’ related to codes. Hennink et al. argue that an approach to saturation relying only on the number of codes ‘misses the point of saturation’ ( 2017 : p. 15) owing to a lack of understanding of the ‘meaning’ of these codes.

In contrast to data saturation and inductive thematic saturation, the first model of saturation considered, theoretical saturation—as based on the grounded theory notion of determining when the properties of theoretical categories are adequately developed—indicates that the process of analysis is at a more advanced stage and at a higher level of theoretical generality. Accordingly, Zhao and Davey ( 2015 : p. 1178) refer to a form of saturation determined by ‘theoretical completeness’ and ceased sampling ‘when dimensions and gaps of each category of the grounded theory had been explicated,’ and Bowen ( 2008 ) gives a detailed account of how evidence of saturation emerged at the level of thematic categories and the broader process of theory construction.

Saturation as event or process

A key issue underlying the identification of saturation is the extent to which it is viewed as an event or a process. Commonly, saturation is referred to as a ‘point’ (e.g. Otmar et al. 2011 ; Jassim and Whitford 2014 ; Kazley et al. 2015 ), suggesting that it should be thought of as a discrete event that can be recognized as such by the analyst. Strauss and Corbin ( 1998 : p. 136), however, talk about saturation as a ‘matter of degree’, arguing that there will always be the potential for ‘the “new” to emerge’. They suggest that saturation should be more concerned with reaching the point where further data collection becomes ‘counter-productive’, and where the ‘new’ does not necessarily add anything to the overall story or theory. Mason ( 2010 ) makes a similar argument, talking of the point at which there are ‘diminishing returns’ from further data-collection, and a number of researchers seem to take this more incremental approach to saturation. Aiken et al. ( 2015 : p. 154), for example, refer in their interview study of unintended pregnancy to being ‘confident of having achieved or at least closely approached thematic saturation.’ Nelson ( 2016 ), echoing Dey’s ( 1999 ) earlier view, argues that the term ‘saturation’ is itself problematic, as it intuitively lends itself to thinking in terms of a fixed point and a sense of ‘completeness’. He thus argues that ‘conceptual depth’ may be a more appropriate term—at least from a grounded theory perspective—whereby the researcher considers whether sufficient depth of understanding has been achieved in relation to emergent theoretical categories.

On this incremental reading of saturation, the analysis does not suddenly become ‘rich’ or ‘insightful’ after that one additional interview, but presumably becomes rich er or more insightful. The question will then be ‘how much saturation is enough?’, rather than ‘has saturation occurred?’ 7 This is a less straightforward question, but one that much better highlights the fact that this can only be a matter of the analyst’s decision—saturation is an ongoing, cumulative judgment that one makes, and perhaps never completes, 8 rather than something that can be pinpointed at a specific juncture.

Uncertainty and equivocation

A desire to identify a specific point in time at which saturation is achieved seems often to give rise to a degree of uncertainty or equivocation. In a number of studies, saturation is claimed, but further data collection takes place in an apparent attempt to ‘confirm’ (Jassim and Whitford 2014 : p. 191; Forsberg et al. 2000 : p. 328) or ‘validate’ (Vandecasteele et al. 2015 : p. 2789) this claim; for example:

After the 10th interview, there were no new themes generated from the interviews. Therefore, it was deemed that the data collection had reached a saturation point. We continued data collection for two more interviews to ensure and confirm that there are no new themes emerging (Jassim and Whitford ( 2014 : pp. 190–191).

Furthermore, a reluctance to rely on evidence of saturation sometimes indicates that saturation is being used in at best an unclear, or at worst an inconsistent or incoherent, fashion. For example, Hill et al. ( 2014 : p. 2), whilst espousing the principle of saturation, seem not fully to trust it:

Saturation was monitored continuously throughout recruitment. For completeness we chose to fully recruit to all participant groups to reduce the chance of missed themes.

Similarly, Jackson et al. ( 2000 : p. 1406) claim that saturation had been established, but then appear to retreat somewhat from this conclusion:

Following analysis of eight sets of data, data saturation was established… however, two additional participants were recruited to ensure data saturation was achieved.

Constantinou et al. ( 2017 ) propose that, given the potential for uncertainty about the point at which saturation is reached, attention should focus more on providing evidence that saturation has been reached, than on concerns about the point at which this occurred. Thus, rather curiously, they propose that it ‘does not hurt to include all interviews from the initial sampling’ ( 2017 : p. 13). This view is inherently problematic, however, as not only does it imply that saturation is a retrospective consideration following the completion of data collection, rather than as guiding ongoing sampling decisions, but one could also argue that saturation loses its relevance if all data are included regardless of whether or not they contribute further insights or add to conceptual understanding. This approach appears to indicate a preoccupation with having enough data to show evidence of saturation, i.e. not too few interviews, rather than saturation aiding decisions about the adequacy of the sample.

Whilst the above suggests ambivalence towards assessing the point at which saturation is achieved, others report having made the conscious decision to continue sampling beyond saturation, appearing to seek additional objective evidence to bolster their sampling decisions. For instance, in investigating staff and patient views on a stroke unit, Tutton et al. ( 2012 : p. 2063) talk of how, despite having achieved saturation, ‘increased observation may have increased the degree of immersion in the lives of those on the unit’, whilst Naegeli et al. ( 2013 : p. 3) look to gain ‘more in-depth understanding… beyond the saturation point’. Similar points are made by Kennedy et al. ( 2012 : p. 859), who talk of looking for ‘novel aspects’ after the achievement of saturation, and Poletti et al. ( 2007 : p. 511), who propose the need to ‘fill gaps in the data’ following saturation. These examples suggest a view that there is something of theoretical importance that is not captured by saturation, though it is unclear from the explanations given as to exactly what this is. 9

Another indication of an ambivalent view taken on saturation is suggested by Mason’s ( 2010 ) observation that sample sizes in studies based on interviews are commonly multiples of ten. This suggests that, in practice, rules of thumb or other a priori guidelines are commonly used in preference to an adaptive approach such as saturation. Quite frequently, studies that adopt the criterion of saturation propose at the same time a prior sample size (e.g. McNulty et al. 2015 ; Long-Sutehall et al. 2011 ). In a similar way, Niccolai et al. ( 2016 ) sought saturation during their analysis, but also state (p. 843) that:

An a priori sample size of 30 to 40 was selected based on recommendations for qualitative studies of this nature… and the anticipated complexity and desired level of depth for our research questions.

Fusch and Ness ( 2015 : p. 1409) appear to endorse this somewhat inconsistent approach when advocating that the researcher should choose a sample size that has ‘the best opportunity for the researcher to reach data saturation’. 10

This tentative and equivocal commitment to saturation may reflect a practical response to the demands of funding bodies and ethics committees for a clear statement of sample size prior to starting a study (O’Reilly and Parker 2013 )—perceived obligations that, in practice, may be given priority over methodological considerations. However, it may also arise from the specific but somewhat uncertain logic that underlies saturation. Determining that further data collection or analysis is unnecessary on the basis of what has been concluded from data gathered hitherto is essentially a statement about the unobserved (what would have happened if the process of data collection and/or analysis had proceeded) based on the observed (the data collection and/or analysis that has taken place hitherto). Furthermore, if saturation is used in relation to negative case analysis in grounded theory (i.e. sources of data that may question or disconfirm aspects of the emergent theory) the logic becomes more tenuous—a statement about the unobserved based on the unobserved. 11 In either case, an uncertain predictive claim is made about the nature of data yet to be collected, and furthermore a claim that could only be tested if the decision to halt data collection were to be overturned. Additionally, the underlying reasoning makes specific assumptions about the way in which the analysis will generate theory, and the earlier in the process of theory development that this occurs the less warranted such assumptions may be. Accordingly, researchers who confidently propose saturation as a criterion for sampling at the outset of a study may become less certain as to how it should be operationalized once the study is in progress, and may therefore be reluctant to abide by it.

This paper has offered a critical reflection on the concept of saturation and its use in qualitative research, contributing to the small body of literature that has examined the complexities of the concept and its underlying assumptions. Drawing on recent examples of its use, saturation has been discussed in relation to three key sets of questions: What? Where and why? When and how?

Extending previous literature that has highlighted the variability in the use of saturation (O’Reilly and Parker 2013 ; Walker 2012 ), we have scrutinized the different ways in which it has been operationalized in the research literature, identifying four models of saturation, each of which appears to make different core assumptions about what saturation is, and about what exactly is being saturated. These have been labelled as: theoretical saturation, inductive thematic saturation, a priori thematic saturation, and data saturation. Moving forward, the identification and recognition of these different models of saturation may aid qualitative researchers in untangling some of the inconsistencies and contradictions that characterize its use.

Saturation’s apparent position as a ‘gold standard’ in assessing quality and its near universal application in qualitative research have been previously questioned (Guest et al. 2006 ; O’Reilly and Parker 2013 ; Malterud et al. 2016 ). Similarly, doubts have been raised regarding its common adoption as a sole criterion of the adequacy of data collection and analysis (Charmaz 2005 ), or of the adequacy of theory development: ‘Elegance, precision, coherence, and clarity are traditional criteria for evaluating theory, somewhat swamped by the metaphorical emphasis on saturation’ (Dey 2007 : p. 186). On the basis of such critiques, we have examined how saturation might be considered in relation to different theoretical and analytical approaches. Whilst we concur with the argument that saturation should not be afforded unquestioned status, polarization of saturation as either applicable or non-applicable to different approaches, as has been suggested (Walker 2012 ), may be too simplistic. Instead we propose that saturation has differing relevance, and a different meaning, depending on the role of theory, the analytic approach adopted, and so forth, and thus may usefully serve different purposes for different types of research—purposes that need to be clearly articulated by the researcher.

Whilst arguing for flexibility in terms of the purpose and use of saturation, we also suggest that there must be some limit to this range of purposes. Some of the ways in which saturation has been operationalized, we would suggest, risk stretching or diluting its meaning to the point where it becomes too widely encompassing, thereby undermining its coherence and utility.

When and how saturation may be judged to have been reached will differ depending on the type of study, as well as assumptions about whether it represents a distinct event or an ongoing process. The view of saturation as an event has been problematized by others (Strauss and Corbin 1998 ; Dey 1999 ; Nelson 2016 ), and we have explored the implications of conceptualizing saturation in this way, arguing that it appears to give rise to a degree of uncertainty and equivocation, in part driven by the uncertain logic of the concept itself—as a statement about the unobserved based on the observed. This uncertainty appears to give rise to inconsistencies and contradictions in its use, which we would argue could be resolved, at least in part, if saturation were to be considered as a matter of degree, rather than simply as something either attained or unattained. However, whilst considering saturation in incremental terms may increase researchers’ confidence in making claims to it, we suggest it is only through due consideration of the specific purpose for which saturation is being used, and what one is hoping to saturate, that the uncertainty around the concept can be resolved.

In highlighting and examining these areas of complexity, this paper has extended previous discussions of saturation in the literature. Whilst consideration of the concept has led some commentators to argue for the need for qualitative researchers to provide a more thorough and transparent reporting of how they achieved saturation in their research, thus allowing readers to assess the validity of this claim (Bowen 2008 ; Francis et al. 2010 ), our arguments go beyond this. We contend that there is a need not only for more transparent reporting, but also for a more thorough re-evaluation of how saturation is conceptualized and operationalized, including recognition of potential inconsistencies and contradictions in the use of the concept—this re-evaluation can be guided through attending to the four approaches we have identified and their implications for the purposes and uses of saturation. This may lead to a more consistent use of saturation, not in terms of its always being used in the same way, but in relation to consistency between the theoretical position and analytic framework adopted, allowing saturation to be used in such a way as to best meet the aims and objectives of the research. It is through consideration of such complexities in the context of specific approaches that saturation can have most value, enabling it to move away from its increasingly elevated yet uneasy position as a taken-for-granted convention of qualitative research.

Acknowledgements

This paper has been informed by discussions with members of the social sciences group of the Institute for Primary Care and Health Sciences at Keele University. TK is funded by South Staffordshire and Shropshire NHS Foundation Trust. CJ is partly funded by NIHR Collaborations for Leadership in Applied Health Research and Care West Midlands (CLAHRC, West Midlands); the views expressed are those of the authors and not necessarily those of the NHS, the NIHR or the Department of Health.

Compliance with ethical standards

Conflicts of interest.

All authors declare to have no conflicts of interest.

1 Although primarily employed in primary research, principles of saturation have also been applied to qualitative synthesis (Garrett et al. 2012 ; Lipworth et al. 2013 ). However, our focus here is on its use in primary studies.

2 These authors proceed to make the more extreme claim that saturation ‘is important in any study, whether quantitative, qualitative, or mixed methods’ (Fusch and Ness 2015 : p. 1411).

3 It should be noted that Birks and Mills ( 2015 ) also state that, as part of theoretical saturation, ‘Categories are clearly articulated with sharply defined and dimensionalized properties’, suggesting a somewhat broader view of saturation, in which the nature of emerging themes is important, rather than just the fact of their (non)emergence.

4 This evokes Glaser’s criticism of Strauss’s approach to sampling, which he regards as conventional, rather than theoretical, sampling: ‘In conventional sampling the analyst questions, guesses and uses experience to go where he thinks he will have the data to test his hypotheses and find the theory that he has preconceived. Discovery to Strauss does not mean induction and emergence, it means finding his theory in data so that it can be tested’ (Glaser 1992 : p. 103).

5 Charmaz ( 2008 , 2014 ) is critical of the extension of the notion of saturation beyond the context of grounded theory, and in particular of its extension into what we here refer to as data saturation.

6 Few authors draw an explicit distinction between data and theoretical saturation—among the exceptions are Bowen ( 2008 ), Sandelowski ( 2008 ), O’Reilly and Parker ( 2013 ), and Hennink et al. ( 2017 ).

7 Hence, Dey ( 1999 : p. 117) suggests the term ‘sufficiency’ in preference to ‘saturation’.

8 This reflects Glaser and Strauss’s ( 1967 : p. 40) view of theory generation: ‘one is constantly alert to emergent perspectives that will change and help develop his theory. These perspectives can easily occur even on the final day of study or even when the manuscript is reviewed in page proof; so the published word is not the final one, but only a pause in the never-ending process of generating theory’.

9 On occasions, a reason for going beyond saturation appears to be ethical rather than methodological. Despite reaching saturation, France et al. ( 2008 : p. 22) note that owing to their ‘commitment to and respect for all the women who wanted to participate in the study, data collection did not end until all had been interviewed.’ Similarly, Kennedy et al. ( 2012 : p. 858) report that they exceeded saturation as this appeared to be ‘more ethical than purposefully choosing individuals to re-interview, or only interviewing until saturation’.

10 Bloor and Wood ( 2006 : p. 165) suggest that this tendency may stem from researchers feeling obliged to abide by sample sizes previously declared to funding bodies or ethics committees, whilst making claims to saturation in order to retain a sense of methodological credibility. Some authors—e.g. Guest et al. ( 2006 ), Francis et al. ( 2010 ), Hennink et al. ( 2017 )—have attempted for formulate procedures whereby the specific number of participants required to achieve saturation is calculated in advance.

11 The first logic is counter-inductive—future non-occurrences of data, codes or theoretical insights are posited on the basis of prior occurrences. In relation to negative case analysis, however, the logic becomes inductive—future non-occurrences are posited on the basis of prior non-occurrences.

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Understanding qualitative measurement: The what, why, and how

Last updated

30 January 2024

Reviewed by

You’ll need to collect data to determine the success of any project, from product launches to employee culture initiatives. How that data is collected is just as important as what it reveals.

There are many ways to gather and analyze data, from in-person interviews to emailed surveys. Qualitative research focuses on telling a story with the information collected, while quantitative research involves collecting, analyzing, and presenting hard datasets.

Data gathered through qualitative measurement describes traits or characteristics. You can collect it in different ways, including interviews and observation, and it can be in the form of descriptive words.

While gathering and analyzing data through qualitative measurement can be challenging, especially if you’re working with limited resources or a smaller team, the insights you get at the end of the project are often well worth the effort.

• What is qualitative measurement?

Qualitative measures can be particularly helpful in understanding how a phenomenon or action affects individuals and groups.

• Why is qualitative data important?

Through data, you can understand how to better serve your customers and employees and anticipate shifts in your business.

The data will provide a deeper understanding of your customers, empowering you to make decisions that positively benefit your company in the long run. Qualitative data helps you see patterns and trends so you can make actionable changes. It can also answer questions posed by your project so you can provide company stakeholders with helpful information and insights.

• How to collect qualitative data

Your ideal method for collecting qualitative data will depend on the resources you have at your disposal, the size of your team, and your project’s timeline.

You might select one method or a mixture of several. For instance, you could opt to send out surveys following a focus group session to receive additional feedback on one or two specific areas of interest.

Analyze your available resources and discuss options with project stakeholders before committing to one particular plan.

The following are some examples of the methods you could use:

Individual interviews

In-depth interviews are one of the most popular methods of collecting qualitative data. They are usually conducted in person, but you could also use video software.

During interviews, a researcher asks the person questions, logging their answers as they go.

Focus groups

Focus groups are a powerful way to observe and document a group of people, making them a common method for collecting qualitative data. They provide researchers with a direct way to interact with participants, listening to them while they share their insights and experiences and recording responses without the interference of software or third-party systems.

However, while focus groups and interviews are two of the most popular methods, they might not be right for every situation or company.

Direct observation

Direct observation allows researchers to see participants in their natural setting, offering an intriguing “real-life” angle to data collection. This method can provide rich, detailed information about the individuals or groups you are studying.

You can conduct surveys in person or online through web software or email. They can also be as detailed or general as your project requires. To get the most information from your surveys, use open-ended questions that encourage respondents to share their thoughts and opinions on the subject.

Diaries and journals

Product launches or employee experience initiatives are two examples of projects that could benefit from diaries and journals as a form of qualitative data gathering.

Diaries and journals enable participants to record their thoughts and feelings on a particular topic. By later examining the diary entries, project managers and stakeholders can better understand their reactions and opinions on the project and the questions asked.

• Examples of qualitative data

Qualitative data is non-numeric information. It’s descriptive, often including adjectives to paint a picture of a situation or object. Qualitative data can be used to describe a person or place, as you can see in the examples below:

The employee prefers black coffee to sweet beverages.

The cat is black and fluffy.

The brown leather couch is worn and faded.

There are many ways to collate qualitative data, but remember to use appropriate language when communicating it to other project stakeholders. Qualitative data isn’t flowery, but neither does it shy away from descriptors to comprehensively paint a picture.

• How to measure qualitative data

To measure qualitative data, define a clear project scope ahead of time. Know what questions you want answered and what people you need to speak to to make that happen. While not every result can be tallied, by understanding the questions and project scope well in advance, you’ll be better prepared to analyze what you’re querying.

Define the method you wish to use for your project. Whether you opt for surveys, focus groups, or a mixture of methods, employ the approach that will yield the most valuable data.

Work within your means and be realistic about the resources you can dedicate to data collection. For example, if you only have one or two employees to dedicate to the project, don’t commit to multiple focus group meetings with large groups of participants, as it might not be feasible.

• What’s the difference between qualitative and quantitative measurements?

Qualitative measurements are descriptive. You can’t measure them with a ruler, scale, or other numeric value, nor can you express them with a numeric value.

In contrast, quantitative measurements are numeric in nature and can be counted.

• When to use qualitative vs. quantitative measurements

Both qualitative and quantitative measurements can be valuable. Which to use greatly depends on the nature of your project.

If you’re looking to confirm a theory, such as determining which variety of body butter was sold most during a specific month, quantitative measurements will likely give you the answers you need.

To learn more about concepts and experiences, such as which advertising campaign your target customers prefer, opt for qualitative measurement.

You don’t have to commit to one or the other exclusively. Many businesses use a mixed-method approach to research, combining elements of both quantitative and qualitative measurements. Know the questions you want to answer and proceed accordingly with what makes the most sense for your goals.

• What are the best ways to communicate qualitative data?

Communicating the qualitative data you’ve gathered can be tricky. The information is subjective, and many project stakeholders or other involved parties may have an easier time understanding and reacting to numeric data.

To effectively communicate qualitative data, you’ll need to create a compelling storyline that offers context and relevant details.

It can also help to describe the data collection method you used. This not only helps set the stage for your story but gives those listening insight into research methodologies they may be unfamiliar with.

Finally, allow plenty of time for questions. Regardless of whether you’re speaking to your company’s CEO or a fellow project manager, you should be prepared to respond to questions with additional, relevant information.

How can qualitative measurement be expressed through data?

Qualitative data is non-numeric. It is most often expressed through descriptions since it is surveyed or observed rather than counted.

• Challenges associated with qualitative measurement

Any in-depth study or research project requires a time commitment. Depending on the research method you employ, other resources might be required. For instance, you might need to compensate the participants of a focus group in some way.

The time and resources required to undertake qualitative measurement could make it prohibitive for many companies, especially small ones with only a few employees. Outsourcing can also be expensive.

Conducting a cost–benefit analysis could help you decide if qualitative measurement is a worthwhile undertaking or one that should be delayed as you plan and prepare.

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What is data saturation in qualitative research.

8 min read A crucial milestone in qualitative research, data saturation means you can end the data collection phase and move on to your analysis. Here we explain exactly what it means, the telltale signs that you’ve reached it, and how to get there as efficiently as possible.

Author:  Will Webster

Subject Matter Expert:  Jess Oliveros

Data saturation is a point in data collection when new information no longer brings fresh insights to the research questions.

Reaching data saturation means you’ve collected enough data to confidently understand the patterns and themes within the dataset – you’ve got what you need to draw conclusions and make your points. Think of it like a conversation where everything that can be said has been said, and now it’s just repetition.

Why is data saturation most relevant to qualitative research? Because qualitative research is about understanding something deeply, and you can reach a critical mass when trying to do that. Quantitative research, on the other hand, deals in numbers and with predetermined sample sizes , so the concept of data saturation is less relevant.

Free eBook: Qualitative research design handbook

How to know when data saturation is reached

At the point of data saturation, you start to notice that the information you’re collecting is just reinforcing what you already know rather than providing new insights.

Knowing when you’ve reached this point is fairly subjective – there’s no formula or equation that can be applied. But there are some telltale signs that can apply to any qualitative research project .

When one or multiple of these signs are present, it’s a good time to begin finalizing the data collection phase and move on to a more detailed analysis.

Recurring themes

You start to notice that new data doesn’t bring up new themes or ideas. Instead, it echoes what you’ve already recorded.

This is a sign that you’ve likely tapped into all the main ideas related to your research question.

No new data

When interviews or surveys start to feel like you’re reading from the same script with each participant, you’ve probably reached the limit of diversity in responses. New participants will probably only confirm what you already know.

You’ve collected enough instances and evidence for each category of your analysis that you can support each theme with multiple examples. In other words, your data has become saturated with a depth and richness that illustrates each finding.

Full understanding

You reach a level of familiarity with the subject matter that allows you to accurately predict what your participants will say next. If this is the case, you’ve likely reached data saturation.

Consistency

The data starts to show consistent patterns that support a coherent story. Crucially, inconsistencies and outliers don’t challenge your thinking and significantly alter the narrative you’ve formed.

This consistency across the data set strengthens the validity of your findings.

Is data saturation the goal of qualitative research?

In a word, no. But it’s often a critical milestone.

The true goal of qualitative research is to gain a deep understanding of the subject matter; data saturation indicates that you’ve gathered enough information to achieve that understanding.

That said, working to achieve data saturation in the most efficient way possible should be a goal of your research project.

How can a qualitative research project reach data saturation?

Reaching data saturation is a pivotal point in qualitative research as a sign that you’ve generated comprehensive and reliable findings.

There’s no exact science for reaching this point, but it does consistently demand two things: an adequate sample size and well-screened participants.

Adequate sample size

Achieving data saturation in qualitative research heavily relies on determining an appropriate sample size .

This is less about hitting a specific number and more about ensuring that the range of participants is broad enough to capture the diverse perspectives your research needs – while being focused enough to allow for thorough analysis.

Flexibility is crucial in this process. For example, in a study exploring patient experiences in a hospital, starting with a small group of patients from various departments might be the initial plan. However, as the interviews progress, if new themes continue to emerge, it might indicate the need to broaden the sample size to include more patients or even healthcare providers for a more comprehensive understanding.

An iterative approach like this can help your research to capture the complexity of people’s experiences without overwhelming the research with redundant information. The goal is to reach a point where additional interviews yield little new information, signaling that the range of experiences has been adequately captured.

While yes, it’s important to stay flexible and iterate as you go, it’s always wise to make use of research solutions that can make recommendations on suggested sample size . Such tools can also monitor crucial metrics like completion rate and audience size to keep your research project on track to reach data saturation.

Well-screened participants

In qualitative research, the depth and validity of your findings are of course totally influenced by your participants. This is where the importance of well-screened participants becomes very clear.

In any research project that addresses a complex social issue – from public health strategy to educational reform – having participants who can provide a range of lived experiences and viewpoints is crucial. Generating the best result isn’t about finding a random assortment of individuals, but instead about forming a carefully selected research panel whose experiences and perspectives directly relate to the research questions.

Achieving this means looking beyond surface criteria, like age or occupation, and instead delving into qualities that are relevant to the study, like experiences, attitudes or behaviors. This ensures that the data collected is rich and deeply rooted in real-world contexts, and will ultimately set you on a faster route to data saturation.

At the same time, if you find that your participants aren’t providing the depth or range of insights expected, you probably need to reevaluate your screening criteria. It’s unlikely that you’ll get it right first time – as with determining sample size, don’t be afraid of an iterative process.

To expedite this process, researchers can use digital tools to build ever-richer pictures of respondents , driving more targeted research and more tailored interactions.

Elevate your qualitative research skills

Mastering qualitative research involves more than knowing concepts like data saturation – it’s about grasping the entire research journey. To do this, you need to dive deep into the world of qualitative research where understanding the ‘why’ behind the ‘what’ is key.

‘Qualitative research design handbook’ is your guide through this journey.

It covers everything from the essence of qualitative analysis to the intricacies of survey design and data collection. You’ll learn how to apply qualitative techniques effectively, ensuring your research is both rich and insightful.

• Uncover the secrets of qualitative analysis
• Design surveys that get to the heart of the matter
• Learn strategic data collection
• Master effective application of techniques

Get your hands on this invaluable resource to refine your research skills. Download our eBook now and step up your qualitative research game.

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Ways to conduct data gathering

Get the best results by understanding the most effective ways to collect data.

The most important part of any market research effort is data gathering. The practice of data collection is more than just asking for information from a random sample of the population. Understanding the methods and processes involved in data gathering ensures you have reliable, rich data to inform your business decisions.

In this article, we’ll discuss the types of data, steps for collecting data, methods of data gathering, privacy considerations, and more. 

What is data gathering?

Data gathering is the first and most important step in the research process, regardless of the type of research being conducted. It entails collecting, measuring, and analyzing information about a specific subject and is used by businesses to make informed decisions. 

There are established processes for effective data gathering that use research to evaluate a previously defined hypothesis. We’ll discuss these later in this article.

Types of data

Before we can start the discussion on data gathering, we need to review the types of data you can collect. All data can be divided into two categories, qualitative or quantitative. Further, data can be classified as first, second, or third-party.

Qualitative data

This type of data can’t be measured or expressed as a number. It s less structured than quantitative data. Qualitative data is information acquired to understand more about a research subject’s underlying motivations—answering “how” and “why” questions. It is information that is descriptive in nature and can consist of words, pictures, or symbols, which is why it isn’t easily measurable.

Qualitative data is obtained through the answers to open-ended questions that allow study participants to answer in their own words. When asked on a survey, an open text box is used for answers.

Examples of questions that will yield qualitative data are: 

How do you feel about using products from XYZ brand?

You indicated that you prefer product A. Why is that your favorite laundry detergent?

Quantitative data

Quantitative data is structured and can be analyzed statistically. Expressed in numbers, the data can be used to measure variables. The results are objective and conclusive. Questions used to collect quantitative data are usually “how many,” “how much,” or “how often?”

Quantitative data can be measured by numerical variables, analyzed through statistical methods, and represented in charts and graphs. 

Examples of quantitative research questions are:

How often do you purchase laundry detergent?

• Once weekly
• Every two weeks
• Once a month

How many containers of laundry detergent do you purchase at one time?

• Another amount

Whether you need to conduct quantitative or qualitative research, SurveyMonkey Audience can connect you with the participants you need. This market research solution allows you to specify the demographics of your target audience and collect and analyze data efficiently and effectively.  

First-party data 

First-party or primary data is collected directly from your research participants. It’s valuable data because it is gathered straight from your sources—which eliminates the issues of misinterpretation and errors. First-party data is the most useful and reliable data for your research.

Common sources of first-party data are:

• Survey responses
• Web analytics
• Social media analytics
• Email analytics

Focus groups

• Experiments
• Observations

The information you can collect from first-party sources includes demographics, purchasing behaviors, interests, purchasing habits, likes, dislikes, etc.

Second-party data 

Second-party or secondary data is data that has already been collected by someone else in the past. It is less reliable because you cannot be certain of the methodology of the data collection. It also was performed with a different hypothesis in mind, so analyses may not align well with your research needs.

Common second-party data sources include:

• Previous research 
• Professional journal publications
• Public records

Second-party data may be collected before primary data to help find knowledge gaps or to augment primary research data.

Third-party data

With third-party data, you’re looking at data sets that are put together from various sources. This type of data has usually been gathered by companies that don’t have direct relationships with consumers and is often sold on data marketplaces. The main benefit of third-party data is that it offers more scale than other data types. 

Common sources of third-party data include:

• DSPs (Demand side platforms)
• Audience management platforms
• DMPs (Data management platforms)
• Public data exchanges

Steps to follow for data gathering

Before you begin data gathering, you need to define your objectives and goals. You must determine exactly what you are looking for so that you have a direction for your research. Then, use the following steps for efficient data gathering.

For example, your objective may be to find out how consumers view your brand. You may test for brand awareness , loyalty, recognition, and image to gather data that will help you determine your overall brand health .

Outline data to be collected

Once you have chosen a hypothesis, measurement, insight, exploration, or another goal for your research, it’s time to determine what information you need to meet your objective. Do you need quantitative data, qualitative data, or a mixed method to include both types?

Determine data gathering method

After you’ve decided what data you need to collect, you need to choose from the various types of data collection methods, settling on the one that is best suited to your research. Consider the information you need to collect, the timeframe for collection, sample size, and any other aspects of your research that factor into how data is collected. We’ll discuss data-gathering methods shortly, so keep this in mind.

Gather data

Once you’ve determined your goal, outlined the data you need, and chosen your data-gathering method, it’s time to start gathering your data. Follow your data collection methodology to ensure the validity of your data. 

If you use SurveyMonkey to collect survey data for your research, we’ll collect the responses for you. Your customizable data dashboard collects data in real-time, so you can view results as they come in.

For other data-gathering methods, you can use spreadsheets or similar tools to record data.

Analyze results

An analysis is the process of taking your raw data and turning it into actionable insights. These insights, depending on your research goals, will support and enhance your marketing efforts and enable you to make informed business decisions.

SurveyMonkey has several analysis features that help you dig deeper into the data. We provide multiple ways to filter results, charts, graphs, crosstab reports, sentiment analysis, benchmarks, and more. If you’ve collected your survey data with us, these analysis tools are right at your fingertips.

Methods of data gathering

As we’ve mentioned, there are several ways to gather data for your market research . Let’s look at the most common methods, including surveys, forms, interviews, focus groups, observation, and online tracking.

You can ask your customers directly for answers to your questions with surveys. Surveys can be created with a variety of question types designed to provide you with the answers you require for your research. Questions may be quantitative, qualitative, or a combination of both. Surveys may be conducted online, via email, telephone, or in person. The easiest, most efficient way to administer surveys is online.

Market research surveys are affordable and provide reliable information for your research. They can be used for:

• Brand tracking
• Feature importance
• Concept testing
• Packaging design analysis
• Usage and attitudes

At SurveyMonkey, we offer a variety of market research solutions that originate with surveys.

We also give many collection mode options to choose from, including mobile device surveys, SMS surveys, and QR codes.

Online forms are another way to reach your research participants. They may be set up to collect a wide range of information, both qualitative and quantitative.

Depending on what format and tool you use, you may have options for sorting and analyzing your data. 

Forms are useful for collecting demographic information, gated content, or handling registrations for events.

In-person interviews

Using a trained moderator to interview individuals on a one-to-one basis is a more expensive and time-consuming method for data gathering.

The benefits of in-person interviews include the ability to view nonverbal cues, ask clarifying questions, and use physical items to aid in the review of product features, etc. This method is also more in-depth and provides a high degree of confidence in the data.

A disadvantage of interviews, in addition to time and expense, is the potential for bias if the interviewee perceives that the interviewer will be pleased with a certain type of response.

Similar to in-person interviews, focus groups involve face-to-face discussions with a moderator or facilitator. Rather than being individual sessions, the discussions take place in a group. 

In focus groups, you run the risk of a participant with a strong opinion swaying the opinions of other group members. The moderator must be able to keep the group on-task and unbiased.

Customer observation

Observation can take the form of using a tool (analytics) to find out how users behave on your website or in-person observation. In-person observation may be observing how consumers move through your physical store. 

In-person observation is a slow, difficult method of data collection. If the subjects of your observation are aware of being observed, they may behave differently than they normally would. This method can only realistically be used for small sample sizes.

Online tracking

Tracking pixels and cookies are used to track users’ behaviors online. This reveals the content they are interested in and engage with. Both pixels and cookies are inexpensive (or free) and easy to implement. Once they are set, they gather data on their own and need little maintenance. But before you start preparing to use online tracking, you need to make sure you’re using this data-gathering method in a legal, ethical way.

Recent changes have led to changes in online tracking. You now need to ask users to indicate their preferences when they visit your website. If many of your users prefer not to accept cookies, it will make online tracking much less useful.

Privacy considerations

Certainly with online tracking, but also with other methods of data collection, you must be aware of the ethics and legalities of keeping your users' and participants’ information safe. Most internet users are wary of sharing personal information, so keep this in mind when preparing to gather data (especially if you are going to ask sensitive questions).

Ensure that your data-gathering method allows you to receive and store personal information safely. To avoid the issue, make your participant responses anonymous and refrain from asking for sensitive information.

Before you ask for any information, make your participants aware of how you will use their data and what you’re doing to ensure their privacy throughout the research process. This will help put participants at ease and allow them to feel safe and comfortable with your research process. This, in turn, will increase your response rate.

Also, before you begin your research, ensure that you and anyone involved in your research are aware of the legal implications and requirements for storing information. Compliance standards for data protection and privacy vary by location, so look up the standards where you’re performing your research. 

Prepare a privacy policy and make it available so your participants can review how their information will be protected.

Start data gathering now

In market research, data gathering is necessary to understand your target market. There are multiple methods for gathering data, but whatever method you choose, ensure that you’re prepared to collect and store the collected data safely and privately.

If you’re prepared to use the survey method to gather data, take a look at SurveyMonkey Audience . We’ll connect you with the target audience you want at the scale you need and collect the data safely. 

Audience is just one of our many market research solutions , designed to help you collect the data you need to make informed business decisions. Get started with your first survey today!

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Table of Contents

What is data collection, why do we need data collection, what are the different data collection methods, data collection tools, the importance of ensuring accurate and appropriate data collection, issues related to maintaining the integrity of data collection, what are common challenges in data collection, what are the key steps in the data collection process, data collection considerations and best practices, choose the right data science program, are you interested in a career in data science, what is data collection: methods, types, tools.

The process of gathering and analyzing accurate data from various sources to find answers to research problems, trends and probabilities, etc., to evaluate possible outcomes is Known as Data Collection. Knowledge is power, information is knowledge, and data is information in digitized form, at least as defined in IT. Hence, data is power. But before you can leverage that data into a successful strategy for your organization or business, you need to gather it. That’s your first step.

So, to help you get the process started, we shine a spotlight on data collection. What exactly is it? Believe it or not, it’s more than just doing a Google search! Furthermore, what are the different types of data collection? And what kinds of data collection tools and data collection techniques exist?

If you want to get up to speed about what is data collection process, you’ve come to the right place. 

Transform raw data into captivating visuals with Simplilearn's hands-on Data Visualization Courses and captivate your audience. Also, master the art of data management with Simplilearn's comprehensive data management courses  - unlock new career opportunities today!

Data collection is the process of collecting and evaluating information or data from multiple sources to find answers to research problems, answer questions, evaluate outcomes, and forecast trends and probabilities. It is an essential phase in all types of research, analysis, and decision-making, including that done in the social sciences, business, and healthcare.

Accurate data collection is necessary to make informed business decisions, ensure quality assurance, and keep research integrity.

During data collection, the researchers must identify the data types, the sources of data, and what methods are being used. We will soon see that there are many different data collection methods . There is heavy reliance on data collection in research, commercial, and government fields.

Before an analyst begins collecting data, they must answer three questions first:

• What’s the goal or purpose of this research?
• What kinds of data are they planning on gathering?
• What methods and procedures will be used to collect, store, and process the information?

Additionally, we can break up data into qualitative and quantitative types. Qualitative data covers descriptions such as color, size, quality, and appearance. Quantitative data, unsurprisingly, deals with numbers, such as statistics, poll numbers, percentages, etc.

Before a judge makes a ruling in a court case or a general creates a plan of attack, they must have as many relevant facts as possible. The best courses of action come from informed decisions, and information and data are synonymous.

The concept of data collection isn’t a new one, as we’ll see later, but the world has changed. There is far more data available today, and it exists in forms that were unheard of a century ago. The data collection process has had to change and grow with the times, keeping pace with technology.

Whether you’re in the world of academia, trying to conduct research, or part of the commercial sector, thinking of how to promote a new product, you need data collection to help you make better choices.

Now that you know what is data collection and why we need it, let's take a look at the different methods of data collection. While the phrase “data collection” may sound all high-tech and digital, it doesn’t necessarily entail things like computers, big data , and the internet. Data collection could mean a telephone survey, a mail-in comment card, or even some guy with a clipboard asking passersby some questions. But let’s see if we can sort the different data collection methods into a semblance of organized categories.

Primary and secondary methods of data collection are two approaches used to gather information for research or analysis purposes. Let's explore each data collection method in detail:

1. Primary Data Collection:

Primary data collection involves the collection of original data directly from the source or through direct interaction with the respondents. This method allows researchers to obtain firsthand information specifically tailored to their research objectives. There are various techniques for primary data collection, including:

a. Surveys and Questionnaires: Researchers design structured questionnaires or surveys to collect data from individuals or groups. These can be conducted through face-to-face interviews, telephone calls, mail, or online platforms.

b. Interviews: Interviews involve direct interaction between the researcher and the respondent. They can be conducted in person, over the phone, or through video conferencing. Interviews can be structured (with predefined questions), semi-structured (allowing flexibility), or unstructured (more conversational).

c. Observations: Researchers observe and record behaviors, actions, or events in their natural setting. This method is useful for gathering data on human behavior, interactions, or phenomena without direct intervention.

d. Experiments: Experimental studies involve the manipulation of variables to observe their impact on the outcome. Researchers control the conditions and collect data to draw conclusions about cause-and-effect relationships.

e. Focus Groups: Focus groups bring together a small group of individuals who discuss specific topics in a moderated setting. This method helps in understanding opinions, perceptions, and experiences shared by the participants.

2. Secondary Data Collection:

Secondary data collection involves using existing data collected by someone else for a purpose different from the original intent. Researchers analyze and interpret this data to extract relevant information. Secondary data can be obtained from various sources, including:

a. Published Sources: Researchers refer to books, academic journals, magazines, newspapers, government reports, and other published materials that contain relevant data.

b. Online Databases: Numerous online databases provide access to a wide range of secondary data, such as research articles, statistical information, economic data, and social surveys.

c. Government and Institutional Records: Government agencies, research institutions, and organizations often maintain databases or records that can be used for research purposes.

d. Publicly Available Data: Data shared by individuals, organizations, or communities on public platforms, websites, or social media can be accessed and utilized for research.

e. Past Research Studies: Previous research studies and their findings can serve as valuable secondary data sources. Researchers can review and analyze the data to gain insights or build upon existing knowledge.

Now that we’ve explained the various techniques, let’s narrow our focus even further by looking at some specific tools. For example, we mentioned interviews as a technique, but we can further break that down into different interview types (or “tools”).

Word Association

The researcher gives the respondent a set of words and asks them what comes to mind when they hear each word.

Sentence Completion

Researchers use sentence completion to understand what kind of ideas the respondent has. This tool involves giving an incomplete sentence and seeing how the interviewee finishes it.

Role-Playing

Respondents are presented with an imaginary situation and asked how they would act or react if it was real.

In-Person Surveys

The researcher asks questions in person.

Online/Web Surveys

These surveys are easy to accomplish, but some users may be unwilling to answer truthfully, if at all.

Mobile Surveys

These surveys take advantage of the increasing proliferation of mobile technology. Mobile collection surveys rely on mobile devices like tablets or smartphones to conduct surveys via SMS or mobile apps.

Phone Surveys

No researcher can call thousands of people at once, so they need a third party to handle the chore. However, many people have call screening and won’t answer.

Observation

Sometimes, the simplest method is the best. Researchers who make direct observations collect data quickly and easily, with little intrusion or third-party bias. Naturally, it’s only effective in small-scale situations.

Accurate data collecting is crucial to preserving the integrity of research, regardless of the subject of study or preferred method for defining data (quantitative, qualitative). Errors are less likely to occur when the right data gathering tools are used (whether they are brand-new ones, updated versions of them, or already available).

Among the effects of data collection done incorrectly, include the following -

• Erroneous conclusions that squander resources
• Decisions that compromise public policy
• Incapacity to correctly respond to research inquiries
• Bringing harm to participants who are humans or animals
• Deceiving other researchers into pursuing futile research avenues
• The study's inability to be replicated and validated

When these study findings are used to support recommendations for public policy, there is the potential to result in disproportionate harm, even if the degree of influence from flawed data collecting may vary by discipline and the type of investigation.

Let us now look at the various issues that we might face while maintaining the integrity of data collection.

In order to assist the errors detection process in the data gathering process, whether they were done purposefully (deliberate falsifications) or not, maintaining data integrity is the main justification (systematic or random errors).

Quality assurance and quality control are two strategies that help protect data integrity and guarantee the scientific validity of study results.

Each strategy is used at various stages of the research timeline:

• Quality control - tasks that are performed both after and during data collecting
• Quality assurance - events that happen before data gathering starts

Let us explore each of them in more detail now.

Quality Assurance

As data collecting comes before quality assurance, its primary goal is "prevention" (i.e., forestalling problems with data collection). The best way to protect the accuracy of data collection is through prevention. The uniformity of protocol created in the thorough and exhaustive procedures manual for data collecting serves as the best example of this proactive step. 

The likelihood of failing to spot issues and mistakes early in the research attempt increases when guides are written poorly. There are several ways to show these shortcomings:

• Failure to determine the precise subjects and methods for retraining or training staff employees in data collecting
• List of goods to be collected, in part
• There isn't a system in place to track modifications to processes that may occur as the investigation continues.
• Instead of detailed, step-by-step instructions on how to deliver tests, there is a vague description of the data gathering tools that will be employed.
• Uncertainty regarding the date, procedure, and identity of the person or people in charge of examining the data
• Incomprehensible guidelines for using, adjusting, and calibrating the data collection equipment.

Now, let us look at how to ensure Quality Control.

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Quality Control

Despite the fact that quality control actions (detection/monitoring and intervention) take place both after and during data collection, the specifics should be meticulously detailed in the procedures manual. Establishing monitoring systems requires a specific communication structure, which is a prerequisite. Following the discovery of data collection problems, there should be no ambiguity regarding the information flow between the primary investigators and staff personnel. A poorly designed communication system promotes slack oversight and reduces opportunities for error detection.

Direct staff observation conference calls, during site visits, or frequent or routine assessments of data reports to spot discrepancies, excessive numbers, or invalid codes can all be used as forms of detection or monitoring. Site visits might not be appropriate for all disciplines. Still, without routine auditing of records, whether qualitative or quantitative, it will be challenging for investigators to confirm that data gathering is taking place in accordance with the manual's defined methods. Additionally, quality control determines the appropriate solutions, or "actions," to fix flawed data gathering procedures and reduce recurrences.

Problems with data collection, for instance, that call for immediate action include:

• Fraud or misbehavior
• Systematic mistakes, procedure violations 
• Individual data items with errors
• Issues with certain staff members or a site's performance 

Researchers are trained to include one or more secondary measures that can be used to verify the quality of information being obtained from the human subject in the social and behavioral sciences where primary data collection entails using human subjects. 

For instance, a researcher conducting a survey would be interested in learning more about the prevalence of risky behaviors among young adults as well as the social factors that influence these risky behaviors' propensity for and frequency. Let us now explore the common challenges with regard to data collection.

There are some prevalent challenges faced while collecting data, let us explore a few of them to understand them better and avoid them.

Data Quality Issues

The main threat to the broad and successful application of machine learning is poor data quality. Data quality must be your top priority if you want to make technologies like machine learning work for you. Let's talk about some of the most prevalent data quality problems in this blog article and how to fix them.

Inconsistent Data

When working with various data sources, it's conceivable that the same information will have discrepancies between sources. The differences could be in formats, units, or occasionally spellings. The introduction of inconsistent data might also occur during firm mergers or relocations. Inconsistencies in data have a tendency to accumulate and reduce the value of data if they are not continually resolved. Organizations that have heavily focused on data consistency do so because they only want reliable data to support their analytics.

Data Downtime

Data is the driving force behind the decisions and operations of data-driven businesses. However, there may be brief periods when their data is unreliable or not prepared. Customer complaints and subpar analytical outcomes are only two ways that this data unavailability can have a significant impact on businesses. A data engineer spends about 80% of their time updating, maintaining, and guaranteeing the integrity of the data pipeline. In order to ask the next business question, there is a high marginal cost due to the lengthy operational lead time from data capture to insight.

Schema modifications and migration problems are just two examples of the causes of data downtime. Data pipelines can be difficult due to their size and complexity. Data downtime must be continuously monitored, and it must be reduced through automation.

Ambiguous Data

Even with thorough oversight, some errors can still occur in massive databases or data lakes. For data streaming at a fast speed, the issue becomes more overwhelming. Spelling mistakes can go unnoticed, formatting difficulties can occur, and column heads might be deceptive. This unclear data might cause a number of problems for reporting and analytics.

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Duplicate Data

Streaming data, local databases, and cloud data lakes are just a few of the sources of data that modern enterprises must contend with. They might also have application and system silos. These sources are likely to duplicate and overlap each other quite a bit. For instance, duplicate contact information has a substantial impact on customer experience. If certain prospects are ignored while others are engaged repeatedly, marketing campaigns suffer. The likelihood of biased analytical outcomes increases when duplicate data are present. It can also result in ML models with biased training data.

Too Much Data

While we emphasize data-driven analytics and its advantages, a data quality problem with excessive data exists. There is a risk of getting lost in an abundance of data when searching for information pertinent to your analytical efforts. Data scientists, data analysts, and business users devote 80% of their work to finding and organizing the appropriate data. With an increase in data volume, other problems with data quality become more serious, particularly when dealing with streaming data and big files or databases.

Inaccurate Data

For highly regulated businesses like healthcare, data accuracy is crucial. Given the current experience, it is more important than ever to increase the data quality for COVID-19 and later pandemics. Inaccurate information does not provide you with a true picture of the situation and cannot be used to plan the best course of action. Personalized customer experiences and marketing strategies underperform if your customer data is inaccurate.

Data inaccuracies can be attributed to a number of things, including data degradation, human mistake, and data drift. Worldwide data decay occurs at a rate of about 3% per month, which is quite concerning. Data integrity can be compromised while being transferred between different systems, and data quality might deteriorate with time.

Hidden Data

The majority of businesses only utilize a portion of their data, with the remainder sometimes being lost in data silos or discarded in data graveyards. For instance, the customer service team might not receive client data from sales, missing an opportunity to build more precise and comprehensive customer profiles. Missing out on possibilities to develop novel products, enhance services, and streamline procedures is caused by hidden data.

Finding Relevant Data

Finding relevant data is not so easy. There are several factors that we need to consider while trying to find relevant data, which include -

• Relevant Domain
• Relevant demographics
• Relevant Time period and so many more factors that we need to consider while trying to find relevant data.

Data that is not relevant to our study in any of the factors render it obsolete and we cannot effectively proceed with its analysis. This could lead to incomplete research or analysis, re-collecting data again and again, or shutting down the study.

Deciding the Data to Collect

Determining what data to collect is one of the most important factors while collecting data and should be one of the first factors while collecting data. We must choose the subjects the data will cover, the sources we will be used to gather it, and the quantity of information we will require. Our responses to these queries will depend on our aims, or what we expect to achieve utilizing your data. As an illustration, we may choose to gather information on the categories of articles that website visitors between the ages of 20 and 50 most frequently access. We can also decide to compile data on the typical age of all the clients who made a purchase from your business over the previous month.

Not addressing this could lead to double work and collection of irrelevant data or ruining your study as a whole.

Dealing With Big Data

Big data refers to exceedingly massive data sets with more intricate and diversified structures. These traits typically result in increased challenges while storing, analyzing, and using additional methods of extracting results. Big data refers especially to data sets that are quite enormous or intricate that conventional data processing tools are insufficient. The overwhelming amount of data, both unstructured and structured, that a business faces on a daily basis. 

The amount of data produced by healthcare applications, the internet, social networking sites social, sensor networks, and many other businesses are rapidly growing as a result of recent technological advancements. Big data refers to the vast volume of data created from numerous sources in a variety of formats at extremely fast rates. Dealing with this kind of data is one of the many challenges of Data Collection and is a crucial step toward collecting effective data. 

Low Response and Other Research Issues

Poor design and low response rates were shown to be two issues with data collecting, particularly in health surveys that used questionnaires. This might lead to an insufficient or inadequate supply of data for the study. Creating an incentivized data collection program might be beneficial in this case to get more responses.

Now, let us look at the key steps in the data collection process.

In the Data Collection Process, there are 5 key steps. They are explained briefly below -

1. Decide What Data You Want to Gather

The first thing that we need to do is decide what information we want to gather. We must choose the subjects the data will cover, the sources we will use to gather it, and the quantity of information that we would require. For instance, we may choose to gather information on the categories of products that an average e-commerce website visitor between the ages of 30 and 45 most frequently searches for. 

2. Establish a Deadline for Data Collection

The process of creating a strategy for data collection can now begin. We should set a deadline for our data collection at the outset of our planning phase. Some forms of data we might want to continuously collect. We might want to build up a technique for tracking transactional data and website visitor statistics over the long term, for instance. However, we will track the data throughout a certain time frame if we are tracking it for a particular campaign. In these situations, we will have a schedule for when we will begin and finish gathering data. 

3. Select a Data Collection Approach

We will select the data collection technique that will serve as the foundation of our data gathering plan at this stage. We must take into account the type of information that we wish to gather, the time period during which we will receive it, and the other factors we decide on to choose the best gathering strategy.

4. Gather Information

Once our plan is complete, we can put our data collection plan into action and begin gathering data. In our DMP, we can store and arrange our data. We need to be careful to follow our plan and keep an eye on how it's doing. Especially if we are collecting data regularly, setting up a timetable for when we will be checking in on how our data gathering is going may be helpful. As circumstances alter and we learn new details, we might need to amend our plan.

5. Examine the Information and Apply Your Findings

It's time to examine our data and arrange our findings after we have gathered all of our information. The analysis stage is essential because it transforms unprocessed data into insightful knowledge that can be applied to better our marketing plans, goods, and business judgments. The analytics tools included in our DMP can be used to assist with this phase. We can put the discoveries to use to enhance our business once we have discovered the patterns and insights in our data.

Let us now look at some data collection considerations and best practices that one might follow.

We must carefully plan before spending time and money traveling to the field to gather data. While saving time and resources, effective data collection strategies can help us collect richer, more accurate, and richer data.

Below, we will be discussing some of the best practices that we can follow for the best results -

1. Take Into Account the Price of Each Extra Data Point

Once we have decided on the data we want to gather, we need to make sure to take the expense of doing so into account. Our surveyors and respondents will incur additional costs for each additional data point or survey question.

2. Plan How to Gather Each Data Piece

There is a dearth of freely accessible data. Sometimes the data is there, but we may not have access to it. For instance, unless we have a compelling cause, we cannot openly view another person's medical information. It could be challenging to measure several types of information.

Consider how time-consuming and difficult it will be to gather each piece of information while deciding what data to acquire.

3. Think About Your Choices for Data Collecting Using Mobile Devices

Mobile-based data collecting can be divided into three categories -

• IVRS (interactive voice response technology) -  Will call the respondents and ask them questions that have already been recorded. 
• SMS data collection - Will send a text message to the respondent, who can then respond to questions by text on their phone. 
• Field surveyors - Can directly enter data into an interactive questionnaire while speaking to each respondent, thanks to smartphone apps.

We need to make sure to select the appropriate tool for our survey and responders because each one has its own disadvantages and advantages.

4. Carefully Consider the Data You Need to Gather

It's all too easy to get information about anything and everything, but it's crucial to only gather the information that we require. 

It is helpful to consider these 3 questions:

• What details will be helpful?
• What details are available?
• What specific details do you require?

5. Remember to Consider Identifiers

Identifiers, or details describing the context and source of a survey response, are just as crucial as the information about the subject or program that we are actually researching.

In general, adding more identifiers will enable us to pinpoint our program's successes and failures with greater accuracy, but moderation is the key.

6. Data Collecting Through Mobile Devices is the Way to Go

Although collecting data on paper is still common, modern technology relies heavily on mobile devices. They enable us to gather many various types of data at relatively lower prices and are accurate as well as quick. There aren't many reasons not to pick mobile-based data collecting with the boom of low-cost Android devices that are available nowadays.

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1. What is data collection with example?

Data collection is the process of collecting and analyzing information on relevant variables in a predetermined, methodical way so that one can respond to specific research questions, test hypotheses, and assess results. Data collection can be either qualitative or quantitative. Example: A company collects customer feedback through online surveys and social media monitoring to improve their products and services.

2. What are the primary data collection methods?

As is well known, gathering primary data is costly and time intensive. The main techniques for gathering data are observation, interviews, questionnaires, schedules, and surveys.

3. What are data collection tools?

The term "data collecting tools" refers to the tools/devices used to gather data, such as a paper questionnaire or a system for computer-assisted interviews. Tools used to gather data include case studies, checklists, interviews, occasionally observation, surveys, and questionnaires.

4. What’s the difference between quantitative and qualitative methods?

While qualitative research focuses on words and meanings, quantitative research deals with figures and statistics. You can systematically measure variables and test hypotheses using quantitative methods. You can delve deeper into ideas and experiences using qualitative methodologies.

5. What are quantitative data collection methods?

While there are numerous other ways to get quantitative information, the methods indicated above—probability sampling, interviews, questionnaire observation, and document review—are the most typical and frequently employed, whether collecting information offline or online.

6. What is mixed methods research?

User research that includes both qualitative and quantitative techniques is known as mixed methods research. For deeper user insights, mixed methods research combines insightful user data with useful statistics.

7. What are the benefits of collecting data?

Collecting data offers several benefits, including:

• Knowledge and Insight
• Evidence-Based Decision Making
• Problem Identification and Solution
• Validation and Evaluation
• Identifying Trends and Predictions
• Support for Research and Development
• Policy Development
• Quality Improvement
• Personalization and Targeting
• Knowledge Sharing and Collaboration

8. What’s the difference between reliability and validity?

Reliability is about consistency and stability, while validity is about accuracy and appropriateness. Reliability focuses on the consistency of results, while validity focuses on whether the results are actually measuring what they are intended to measure. Both reliability and validity are crucial considerations in research to ensure the trustworthiness and meaningfulness of the collected data and measurements.

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