primary and secondary objectives in research methodology

• Technical Support
• Technical Papers
• Knowledge Base
• Question Library

Call our friendly, no-pressure support team.

Primary vs Secondary Research: Differences, Methods, Sources, and More

Table of Contents

Primary vs Secondary Research – What’s the Difference?

In the search for knowledge and data to inform decisions, researchers and analysts rely on a blend of research sources. These sources are broadly categorized into primary and secondary research, each serving unique purposes and offering different insights into the subject matter at hand. But what exactly sets them apart?

Primary research is the process of gathering fresh data directly from its source. This approach offers real-time insights and specific information tailored to specific objectives set by stakeholders. Examples include surveys, interviews, and observational studies.

Secondary research , on the other hand, involves the analysis of existing data, most often collected and presented by others. This type of research is invaluable for understanding broader trends, providing context, or validating hypotheses. Common sources include scholarly articles, industry reports, and data compilations.

The crux of the difference lies in the origin of the information: primary research yields firsthand data which can be tailored to a specific business question, whilst secondary research synthesizes what's already out there. In essence, primary research listens directly to the voice of the subject, whereas secondary research hears it secondhand .

When to Use Primary and Secondary Research

Selecting the appropriate research method is pivotal and should be aligned with your research objectives. The choice between primary and secondary research is not merely procedural but strategic, influencing the depth and breadth of insights you can uncover.

Primary research shines when you need up-to-date, specific information directly relevant to your study. It's the go-to for fresh insights, understanding consumer behavior, or testing new theories. Its bespoke nature makes it indispensable for tailoring questions to get the exact answers you need.

Ready to Start Gathering Primary Research Data?

Get started with our free survey research tool today! In just a few minutes, you can create powerful surveys with our easy-to-use interface.

Start Survey Research for Free or Request a Product Tour

Secondary research is your first step into the research world. It helps set the stage by offering a broad understanding of the topic. Before diving into costly primary research, secondary research can validate the need for further investigation or provide a solid background to build upon. It's especially useful for identifying trends, benchmarking, and situating your research within the existing body of knowledge.

Combining both methods can significantly enhance your research. Starting with secondary research lays the groundwork and narrows the focus, whilst subsequent primary research delves deep into specific areas of interest, providing a well-rounded, comprehensive understanding of the topic.

Primary vs Secondary Research Methods

In the landscape of market research, the methodologies employed can significantly influence the insights and conclusions drawn. Let's delve deeper into the various methods underpinning both primary and secondary research, shedding light on their unique applications and the distinct insights they offer.

Primary Research Methods:

• Surveys: Surveys are a cornerstone of primary research, offering a quantitative approach to gathering data directly from the target audience. By employing structured questionnaires, researchers can collect a vast array of data ranging from customer preferences to behavioral patterns. This method is particularly valuable for acquiring statistically significant data that can inform decision-making processes and strategy development. The application of statistical approaches for analysing this data, such as key drivers analysis, MaxDiff or conjoint analysis can also further enhance any collected data.
• One on One Interviews: Interviews provide a qualitative depth to primary research, allowing for a nuanced exploration of participants' attitudes, experiences, and motivations. Conducted either face-to-face or remotely, interviews enable researchers to delve into the complexities of human behavior, offering rich insights that surveys alone may not uncover. This method is instrumental in exploring new areas of research or obtaining detailed information on specific topics.
• Focus Groups: Focus groups bring together a small, diverse group of participants to discuss and provide feedback on a particular subject, product, or idea. This interactive setting fosters a dynamic exchange of ideas, revealing consumers' perceptions, experiences, and preferences. Focus groups are invaluable for testing concepts, exploring market trends, and understanding the factors that influence consumer decisions.
• Ethnographic Studies: Ethnographic studies involve the systematic watching, recording, and analysis of behaviors and events in their natural setting. This method offers an unobtrusive way to gather authentic data on how people interact with products, services, or environments, providing insights that can lead to more user-centered design and marketing strategies.

Secondary Research Methods:

• Literature Reviews: Literature reviews involve the comprehensive examination of existing research and publications on a given topic. This method enables researchers to synthesize findings from a range of sources, providing a broad understanding of what is already known about a subject and identifying gaps in current knowledge.
• Meta-Analysis: Meta-analysis is a statistical technique that combines the results of multiple studies to arrive at a comprehensive conclusion. This method is particularly useful in secondary research for aggregating findings across different studies, offering a more robust understanding of the evidence on a particular topic.
• Content Analysis: Content analysis is a method for systematically analyzing texts, media, or other content to quantify patterns, themes, or biases . This approach allows researchers to assess the presence of certain words, concepts, or sentiments within a body of work, providing insights into trends, representations, and societal norms. This can be performed across a range of sources including social media, customer forums or review sites.
• Historical Research: Historical research involves the study of past events, trends, and behaviors through the examination of relevant documents and records. This method can provide context and understanding of current trends and inform future predictions, offering a unique perspective that enriches secondary research.

Each of these methods, whether primary or secondary, plays a crucial role in the mosaic of market research, offering distinct pathways to uncovering the insights necessary to drive informed decisions and strategies.

Primary vs Secondary Sources in Research

Both primary and secondary sources of research form the backbone of the insight generation process, when both are utilized in tandem it can provide the perfect steppingstone for the generation of real insights. Let’s explore how each category serves its unique purpose in the research ecosystem.

Primary Research Data Sources

Primary research data sources are the lifeblood of firsthand research, providing raw, unfiltered insights directly from the source. These include:

• Customer Satisfaction Survey Results: Direct feedback from customers about their satisfaction with a product or service. This data is invaluable for identifying strengths to build on and areas for improvement and typically renews each month or quarter so that metrics can be tracked over time.
• NPS Rating Scores from Customers: Net Promoter Score (NPS) provides a straightforward metric to gauge customer loyalty and satisfaction. This quantitative data can reveal much about customer sentiment and the likelihood of referrals.
• Ad-hoc Surveys: Ad-hoc surveys can be about any topic which requires investigation, they are typically one off surveys which zero in on one particular business objective. Ad-hoc projects are useful for situations such as investigating issues identified in other tracking surveys, new product development, ad testing, brand messaging, and many other kinds of projects.
• A Field Researcher’s Notes: Detailed observations from fieldwork can offer nuanced insights into user behaviors, interactions, and environmental factors that influence those interactions. These notes are a goldmine for understanding the context and complexities of user experiences.
• Recordings Made During Focus Groups: Audio or video recordings of focus group discussions capture the dynamics of conversation, including reactions, emotions, and the interplay of ideas. Analyzing these recordings can uncover nuanced consumer attitudes and perceptions that might not be evident in survey data alone.

These primary data sources are characterized by their immediacy and specificity, offering a direct line to the subject of study. They enable researchers to gather data that is specifically tailored to their research objectives, providing a solid foundation for insightful analysis and strategic decision-making.

Secondary Research Data Sources

In contrast, secondary research data sources offer a broader perspective, compiling and synthesizing information from various origins. These sources include:

• Books, Magazines, Scholarly Journals: Published works provide comprehensive overviews, detailed analyses, and theoretical frameworks that can inform research topics, offering depth and context that enriches primary data.
• Market Research Reports: These reports aggregate data and analyses on industry trends, consumer behavior, and market dynamics, providing a macro-level view that can guide primary research directions and validate findings.
• Government Reports: Official statistics and reports from government agencies offer authoritative data on a wide range of topics, from economic indicators to demographic trends, providing a reliable basis for secondary analysis.
• White Papers, Private Company Data: White papers and reports from businesses and consultancies offer insights into industry-specific research, best practices, and market analyses. These sources can be invaluable for understanding the competitive landscape and identifying emerging trends.

Secondary data sources serve as a compass, guiding researchers through the vast landscape of information to identify relevant trends, benchmark against existing data, and build upon the foundation of existing knowledge. They can significantly expedite the research process by leveraging the collective wisdom and research efforts of others.

By adeptly navigating both primary and secondary sources, researchers can construct a well-rounded research project that combines the depth of firsthand data with the breadth of existing knowledge. This holistic approach ensures a comprehensive understanding of the research topic, fostering informed decisions and strategic insights.

Examples of Primary and Secondary Research in Marketing

In the realm of marketing, both primary and secondary research methods play critical roles in understanding market dynamics, consumer behavior, and competitive landscapes. By comparing examples across both methodologies, we can appreciate their unique contributions to strategic decision-making.

Example 1: New Product Development

Primary Research: Direct Consumer Feedback through Surveys and Focus Groups

• Objective: To gauge consumer interest in a new product concept and identify preferred features.
• Process: Surveys distributed to a target demographic to collect quantitative data on consumer preferences, and focus groups conducted to dive deeper into consumer attitudes and desires.
• Insights: Direct insights into consumer needs, preferences for specific features, and willingness to pay. These insights help in refining product design and developing a targeted marketing strategy.

Secondary Research: Market Analysis Reports

• Objective: To understand the existing market landscape, including competitor products and market trends.
• Process: Analyzing published market analysis reports and industry studies to gather data on market size, growth trends, and competitive offerings.
• Insights: Provides a broader understanding of the market, helping to position the new product strategically against competitors and align it with current trends.

Example 2: Brand Positioning

Primary Research: Brand Perception Analysis through Surveys

• Objective: To understand how the brand is perceived by consumers and identify potential areas for repositioning.
• Process: Conducting surveys that ask consumers to describe the brand in their own words, rate it against various attributes, and compare it to competitors.
• Insights: Direct feedback on brand strengths and weaknesses from the consumer's perspective, offering actionable data for adjusting brand messaging and positioning.

Secondary Research: Social Media Sentiment Analysis

• Objective: To analyze public sentiment towards the brand and its competitors.
• Process: Utilizing software tools to analyze mentions, hashtags, and discussions related to the brand and its competitors across social media platforms.
• Insights: Offers an overview of public perception and emerging trends in consumer sentiment, which can validate findings from primary research or highlight areas needing further investigation.

Example 3: Market Expansion Strategy

Primary Research: Consumer Demand Studies in New Markets

• Objective: To assess demand and consumer preferences in a new geographic market.
• Process: Conducting surveys and interviews with potential consumers in the target market to understand their needs, preferences, and cultural nuances.
• Insights: Provides specific insights into the new market’s consumer behavior, preferences, and potential barriers to entry, guiding market entry strategies.

Secondary Research: Economic and Demographic Analysis

• Objective: To evaluate the economic viability and demographic appeal of the new market.
• Process: Reviewing existing economic reports, demographic data, and industry trends relevant to the target market.
• Insights: Offers a macro view of the market's potential, including economic conditions, demographic trends, and consumer spending patterns, which can complement insights gained from primary research.

By leveraging both primary and secondary research, marketers can form a comprehensive understanding of their market, consumers, and competitors, facilitating informed decision-making and strategic planning. Each method brings its strengths to the table, with primary research offering direct consumer insights and secondary research providing a broader context within which to interpret those insights.

What Are the Pros and Cons of Primary and Secondary Research?

When it comes to market research, both primary and secondary research offer unique advantages and face certain limitations. Understanding these can help researchers and businesses make informed decisions on which approach to utilize for their specific needs. Below is a comparative table highlighting the pros and cons of each research type.

Navigating the Pros and Cons

• Balance Your Research Needs: Consider starting with secondary research to gain a broad understanding of the subject matter, then delve into primary research for specific, targeted insights that are tailored to your precise needs.
• Resource Allocation: Evaluate your budget, time, and resource availability. Primary research can offer more specific and actionable data but requires more resources. Secondary research is more accessible but may lack the specificity or recency you need.
• Quality and Relevance: Assess the quality and relevance of available secondary sources before deciding if primary research is necessary. Sometimes, the existing data might suffice, especially for preliminary market understanding or trend analysis.
• Combining Both for Comprehensive Insights: Often, the most effective research strategy involves a combination of both primary and secondary research. This approach allows for a more comprehensive understanding of the market, leveraging the broad perspective provided by secondary sources and the depth and specificity of primary data.

Free Survey Maker Tool

Get access to our free and intuitive survey maker. In just a few minutes, you can create powerful surveys with its easy-to-use interface.

Try our Free Survey Maker or Request a Product Tour

Sawtooth Software

3210 N Canyon Rd Ste 202

Provo UT 84604-6508

United States of America

Support: [email protected]

Consulting: [email protected]

Sales: [email protected]

Products & Services

Support & Resources

Open Access is an initiative that aims to make scientific research freely available to all. To date our community has made over 100 million downloads. It’s based on principles of collaboration, unobstructed discovery, and, most importantly, scientific progression. As PhD students, we found it difficult to access the research we needed, so we decided to create a new Open Access publisher that levels the playing field for scientists across the world. How? By making research easy to access, and puts the academic needs of the researchers before the business interests of publishers.

We are a community of more than 103,000 authors and editors from 3,291 institutions spanning 160 countries, including Nobel Prize winners and some of the world’s most-cited researchers. Publishing on IntechOpen allows authors to earn citations and find new collaborators, meaning more people see your work not only from your own field of study, but from other related fields too.

Brief introduction to this section that descibes Open Access especially from an IntechOpen perspective

Want to get in touch? Contact our London head office or media team here

Our team is growing all the time, so we’re always on the lookout for smart people who want to help us reshape the world of scientific publishing.

Home > Books > Cyberspace

Research Design and Methodology

Submitted: 23 January 2019 Reviewed: 08 March 2019 Published: 07 August 2019

DOI: 10.5772/intechopen.85731

Cite this chapter

There are two ways to cite this chapter:

From the Edited Volume

Edited by Evon Abu-Taieh, Abdelkrim El Mouatasim and Issam H. Al Hadid

To purchase hard copies of this book, please contact the representative in India: CBS Publishers & Distributors Pvt. Ltd. www.cbspd.com | [email protected]

Chapter metrics overview

30,681 Chapter Downloads

Impact of this chapter

Total Chapter Downloads on intechopen.com

Total Chapter Views on intechopen.com

Overall attention for this chapters

There are a number of approaches used in this research method design. The purpose of this chapter is to design the methodology of the research approach through mixed types of research techniques. The research approach also supports the researcher on how to come across the research result findings. In this chapter, the general design of the research and the methods used for data collection are explained in detail. It includes three main parts. The first part gives a highlight about the dissertation design. The second part discusses about qualitative and quantitative data collection methods. The last part illustrates the general research framework. The purpose of this section is to indicate how the research was conducted throughout the study periods.

• research design
• methodology
• data sources

Author Information

Kassu jilcha sileyew *.

• School of Mechanical and Industrial Engineering, Addis Ababa Institute of Technology, Addis Ababa University, Addis Ababa, Ethiopia

*Address all correspondence to: [email protected]

1. Introduction

Research methodology is the path through which researchers need to conduct their research. It shows the path through which these researchers formulate their problem and objective and present their result from the data obtained during the study period. This research design and methodology chapter also shows how the research outcome at the end will be obtained in line with meeting the objective of the study. This chapter hence discusses the research methods that were used during the research process. It includes the research methodology of the study from the research strategy to the result dissemination. For emphasis, in this chapter, the author outlines the research strategy, research design, research methodology, the study area, data sources such as primary data sources and secondary data, population consideration and sample size determination such as questionnaires sample size determination and workplace site exposure measurement sample determination, data collection methods like primary data collection methods including workplace site observation data collection and data collection through desk review, data collection through questionnaires, data obtained from experts opinion, workplace site exposure measurement, data collection tools pretest, secondary data collection methods, methods of data analysis used such as quantitative data analysis and qualitative data analysis, data analysis software, the reliability and validity analysis of the quantitative data, reliability of data, reliability analysis, validity, data quality management, inclusion criteria, ethical consideration and dissemination of result and its utilization approaches. In order to satisfy the objectives of the study, a qualitative and quantitative research method is apprehended in general. The study used these mixed strategies because the data were obtained from all aspects of the data source during the study time. Therefore, the purpose of this methodology is to satisfy the research plan and target devised by the researcher.

2. Research design

The research design is intended to provide an appropriate framework for a study. A very significant decision in research design process is the choice to be made regarding research approach since it determines how relevant information for a study will be obtained; however, the research design process involves many interrelated decisions [ 1 ].

This study employed a mixed type of methods. The first part of the study consisted of a series of well-structured questionnaires (for management, employee’s representatives, and technician of industries) and semi-structured interviews with key stakeholders (government bodies, ministries, and industries) in participating organizations. The other design used is an interview of employees to know how they feel about safety and health of their workplace, and field observation at the selected industrial sites was undertaken.

Hence, this study employs a descriptive research design to agree on the effects of occupational safety and health management system on employee health, safety, and property damage for selected manufacturing industries. Saunders et al. [ 2 ] and Miller [ 3 ] say that descriptive research portrays an accurate profile of persons, events, or situations. This design offers to the researchers a profile of described relevant aspects of the phenomena of interest from an individual, organizational, and industry-oriented perspective. Therefore, this research design enabled the researchers to gather data from a wide range of respondents on the impact of safety and health on manufacturing industries in Ethiopia. And this helped in analyzing the response obtained on how it affects the manufacturing industries’ workplace safety and health. The research overall design and flow process are depicted in Figure 1 .

Research methods and processes (author design).

3. Research methodology

To address the key research objectives, this research used both qualitative and quantitative methods and combination of primary and secondary sources. The qualitative data supports the quantitative data analysis and results. The result obtained is triangulated since the researcher utilized the qualitative and quantitative data types in the data analysis. The study area, data sources, and sampling techniques were discussed under this section.

3.1 The study area

According to Fraenkel and Warren [ 4 ] studies, population refers to the complete set of individuals (subjects or events) having common characteristics in which the researcher is interested. The population of the study was determined based on random sampling system. This data collection was conducted from March 07, 2015 to December 10, 2016, from selected manufacturing industries found in Addis Ababa city and around. The manufacturing companies were selected based on their employee number, established year, and the potential accidents prevailing and the manufacturing industry type even though all criterions were difficult to satisfy.

3.2 Data sources

3.2.1 primary data sources.

It was obtained from the original source of information. The primary data were more reliable and have more confidence level of decision-making with the trusted analysis having direct intact with occurrence of the events. The primary data sources are industries’ working environment (through observation, pictures, and photograph) and industry employees (management and bottom workers) (interview, questionnaires and discussions).

3.2.2 Secondary data

Desk review has been conducted to collect data from various secondary sources. This includes reports and project documents at each manufacturing sectors (more on medium and large level). Secondary data sources have been obtained from literatures regarding OSH, and the remaining data were from the companies’ manuals, reports, and some management documents which were included under the desk review. Reputable journals, books, different articles, periodicals, proceedings, magazines, newsletters, newspapers, websites, and other sources were considered on the manufacturing industrial sectors. The data also obtained from the existing working documents, manuals, procedures, reports, statistical data, policies, regulations, and standards were taken into account for the review.

In general, for this research study, the desk review has been completed to this end, and it had been polished and modified upon manuals and documents obtained from the selected companies.

4. Population and sample size

4.1 population.

The study population consisted of manufacturing industries’ employees in Addis Ababa city and around as there are more representative manufacturing industrial clusters found. To select representative manufacturing industrial sector population, the types of the industries expected were more potential to accidents based on random and purposive sampling considered. The population of data was from textile, leather, metal, chemicals, and food manufacturing industries. A total of 189 sample sizes of industries responded to the questionnaire survey from the priority areas of the government. Random sample sizes and disproportionate methods were used, and 80 from wood, metal, and iron works; 30 from food, beverage, and tobacco products; 50 from leather, textile, and garments; 20 from chemical and chemical products; and 9 from other remaining 9 clusters of manufacturing industries responded.

4.2 Questionnaire sample size determination

A simple random sampling and purposive sampling methods were used to select the representative manufacturing industries and respondents for the study. The simple random sampling ensures that each member of the population has an equal chance for the selection or the chance of getting a response which can be more than equal to the chance depending on the data analysis justification. Sample size determination procedure was used to get optimum and reasonable information. In this study, both probability (simple random sampling) and nonprobability (convenience, quota, purposive, and judgmental) sampling methods were used as the nature of the industries are varied. This is because of the characteristics of data sources which permitted the researchers to follow the multi-methods. This helps the analysis to triangulate the data obtained and increase the reliability of the research outcome and its decision. The companies’ establishment time and its engagement in operation, the number of employees and the proportion it has, the owner types (government and private), type of manufacturing industry/production, types of resource used at work, and the location it is found in the city and around were some of the criteria for the selections.

The determination of the sample size was adopted from Daniel [ 5 ] and Cochran [ 6 ] formula. The formula used was for unknown population size Eq. (1) and is given as

where n  = sample size, Z  = statistic for a level of confidence, P  = expected prevalence or proportion (in proportion of one; if 50%, P  = 0.5), and d  = precision (in proportion of one; if 6%, d  = 0.06). Z statistic ( Z ): for the level of confidence of 95%, which is conventional, Z value is 1.96. In this study, investigators present their results with 95% confidence intervals (CI).

The expected sample number was 267 at the marginal error of 6% for 95% confidence interval of manufacturing industries. However, the collected data indicated that only 189 populations were used for the analysis after rejecting some data having more missing values in the responses from the industries. Hence, the actual data collection resulted in 71% response rate. The 267 population were assumed to be satisfactory and representative for the data analysis.

4.3 Workplace site exposure measurement sample determination

The sample size for the experimental exposure measurements of physical work environment has been considered based on the physical data prepared for questionnaires and respondents. The response of positive were considered for exposure measurement factors to be considered for the physical environment health and disease causing such as noise intensity, light intensity, pressure/stress, vibration, temperature/coldness, or hotness and dust particles on 20 workplace sites. The selection method was using random sampling in line with purposive method. The measurement of the exposure factors was done in collaboration with Addis Ababa city Administration and Oromia Bureau of Labour and Social Affair (AACBOLSA). Some measuring instruments were obtained from the Addis Ababa city and Oromia Bureau of Labour and Social Affair.

5. Data collection methods

Data collection methods were focused on the followings basic techniques. These included secondary and primary data collections focusing on both qualitative and quantitative data as defined in the previous section. The data collection mechanisms are devised and prepared with their proper procedures.

5.1 Primary data collection methods

Primary data sources are qualitative and quantitative. The qualitative sources are field observation, interview, and informal discussions, while that of quantitative data sources are survey questionnaires and interview questions. The next sections elaborate how the data were obtained from the primary sources.

5.1.1 Workplace site observation data collection

Observation is an important aspect of science. Observation is tightly connected to data collection, and there are different sources for this: documentation, archival records, interviews, direct observations, and participant observations. Observational research findings are considered strong in validity because the researcher is able to collect a depth of information about a particular behavior. In this dissertation, the researchers used observation method as one tool for collecting information and data before questionnaire design and after the start of research too. The researcher made more than 20 specific observations of manufacturing industries in the study areas. During the observations, it found a deeper understanding of the working environment and the different sections in the production system and OSH practices.

5.1.2 Data collection through interview

Interview is a loosely structured qualitative in-depth interview with people who are considered to be particularly knowledgeable about the topic of interest. The semi-structured interview is usually conducted in a face-to-face setting which permits the researcher to seek new insights, ask questions, and assess phenomena in different perspectives. It let the researcher to know the in-depth of the present working environment influential factors and consequences. It has provided opportunities for refining data collection efforts and examining specialized systems or processes. It was used when the researcher faces written records or published document limitation or wanted to triangulate the data obtained from other primary and secondary data sources.

This dissertation is also conducted with a qualitative approach and conducting interviews. The advantage of using interviews as a method is that it allows respondents to raise issues that the interviewer may not have expected. All interviews with employees, management, and technicians were conducted by the corresponding researcher, on a face-to-face basis at workplace. All interviews were recorded and transcribed.

5.1.3 Data collection through questionnaires

The main tool for gaining primary information in practical research is questionnaires, due to the fact that the researcher can decide on the sample and the types of questions to be asked [ 2 ].

In this dissertation, each respondent is requested to reply to an identical list of questions mixed so that biasness was prevented. Initially the questionnaire design was coded and mixed up from specific topic based on uniform structures. Consequently, the questionnaire produced valuable data which was required to achieve the dissertation objectives.

The questionnaires developed were based on a five-item Likert scale. Responses were given to each statement using a five-point Likert-type scale, for which 1 = “strongly disagree” to 5 = “strongly agree.” The responses were summed up to produce a score for the measures.

5.1.4 Data obtained from experts’ opinion

The data was also obtained from the expert’s opinion related to the comparison of the knowledge, management, collaboration, and technology utilization including their sub-factors. The data obtained in this way was used for prioritization and decision-making of OSH, improving factor priority. The prioritization of the factors was using Saaty scales (1–9) and then converting to Fuzzy set values obtained from previous researches using triangular fuzzy set [ 7 ].

5.1.5 Workplace site exposure measurement

The researcher has measured the workplace environment for dust, vibration, heat, pressure, light, and noise to know how much is the level of each variable. The primary data sources planned and an actual coverage has been compared as shown in Table 1 .

Planned versus actual coverage of the survey.

The response rate for the proposed data source was good, and the pilot test also proved the reliability of questionnaires. Interview/discussion resulted in 87% of responses among the respondents; the survey questionnaire response rate obtained was 71%, and the field observation response rate was 90% for the whole data analysis process. Hence, the data organization quality level has not been compromised.

This response rate is considered to be representative of studies of organizations. As the study agrees on the response rate to be 30%, it is considered acceptable [ 8 ]. Saunders et al. [ 2 ] argued that the questionnaire with a scale response of 20% response rate is acceptable. Low response rate should not discourage the researchers, because a great deal of published research work also achieves low response rate. Hence, the response rate of this study is acceptable and very good for the purpose of meeting the study objectives.

5.1.6 Data collection tool pretest

The pretest for questionnaires, interviews, and tools were conducted to validate that the tool content is valid or not in the sense of the respondents’ understanding. Hence, content validity (in which the questions are answered to the target without excluding important points), internal validity (in which the questions raised answer the outcomes of researchers’ target), and external validity (in which the result can generalize to all the population from the survey sample population) were reflected. It has been proved with this pilot test prior to the start of the basic data collections. Following feedback process, a few minor changes were made to the originally designed data collect tools. The pilot test made for the questionnaire test was on 10 sample sizes selected randomly from the target sectors and experts.

5.2 Secondary data collection methods

The secondary data refers to data that was collected by someone other than the user. This data source gives insights of the research area of the current state-of-the-art method. It also makes some sort of research gap that needs to be filled by the researcher. This secondary data sources could be internal and external data sources of information that may cover a wide range of areas.

Literature/desk review and industry documents and reports: To achieve the dissertation’s objectives, the researcher has conducted excessive document review and reports of the companies in both online and offline modes. From a methodological point of view, literature reviews can be comprehended as content analysis, where quantitative and qualitative aspects are mixed to assess structural (descriptive) as well as content criteria.

A literature search was conducted using the database sources like MEDLINE; Emerald; Taylor and Francis publications; EMBASE (medical literature); PsycINFO (psychological literature); Sociological Abstracts (sociological literature); accident prevention journals; US Statistics of Labor, European Safety and Health database; ABI Inform; Business Source Premier (business/management literature); EconLit (economic literature); Social Service Abstracts (social work and social service literature); and other related materials. The search strategy was focused on articles or reports that measure one or more of the dimensions within the research OSH model framework. This search strategy was based on a framework and measurement filter strategy developed by the Consensus-Based Standards for the Selection of Health Measurement Instruments (COSMIN) group. Based on screening, unrelated articles to the research model and objectives were excluded. Prior to screening, researcher (principal investigator) reviewed a sample of more than 2000 articles, websites, reports, and guidelines to determine whether they should be included for further review or reject. Discrepancies were thoroughly identified and resolved before the review of the main group of more than 300 articles commenced. After excluding the articles based on the title, keywords, and abstract, the remaining articles were reviewed in detail, and the information was extracted on the instrument that was used to assess the dimension of research interest. A complete list of items was then collated within each research targets or objectives and reviewed to identify any missing elements.

6. Methods of data analysis

Data analysis method follows the procedures listed under the following sections. The data analysis part answered the basic questions raised in the problem statement. The detailed analysis of the developed and developing countries’ experiences on OSH regarding manufacturing industries was analyzed, discussed, compared and contrasted, and synthesized.

6.1 Quantitative data analysis

Quantitative data were obtained from primary and secondary data discussed above in this chapter. This data analysis was based on their data type using Excel, SPSS 20.0, Office Word format, and other tools. This data analysis focuses on numerical/quantitative data analysis.

Before analysis, data coding of responses and analysis were made. In order to analyze the data obtained easily, the data were coded to SPSS 20.0 software as the data obtained from questionnaires. This task involved identifying, classifying, and assigning a numeric or character symbol to data, which was done in only one way pre-coded [ 9 , 10 ]. In this study, all of the responses were pre-coded. They were taken from the list of responses, a number of corresponding to a particular selection was given. This process was applied to every earlier question that needed this treatment. Upon completion, the data were then entered to a statistical analysis software package, SPSS version 20.0 on Windows 10 for the next steps.

Under the data analysis, exploration of data has been made with descriptive statistics and graphical analysis. The analysis included exploring the relationship between variables and comparing groups how they affect each other. This has been done using cross tabulation/chi square, correlation, and factor analysis and using nonparametric statistic.

6.2 Qualitative data analysis

Qualitative data analysis used for triangulation of the quantitative data analysis. The interview, observation, and report records were used to support the findings. The analysis has been incorporated with the quantitative discussion results in the data analysis parts.

6.3 Data analysis software

The data were entered using SPSS 20.0 on Windows 10 and analyzed. The analysis supported with SPSS software much contributed to the finding. It had contributed to the data validation and correctness of the SPSS results. The software analyzed and compared the results of different variables used in the research questionnaires. Excel is also used to draw the pictures and calculate some analytical solutions.

7. The reliability and validity analysis of the quantitative data

7.1 reliability of data.

The reliability of measurements specifies the amount to which it is without bias (error free) and hence ensures consistent measurement across time and across the various items in the instrument [ 8 ]. In reliability analysis, it has been checked for the stability and consistency of the data. In the case of reliability analysis, the researcher checked the accuracy and precision of the procedure of measurement. Reliability has numerous definitions and approaches, but in several environments, the concept comes to be consistent [ 8 ]. The measurement fulfills the requirements of reliability when it produces consistent results during data analysis procedure. The reliability is determined through Cranach’s alpha as shown in Table 2 .

Internal consistency and reliability test of questionnaires items.

K stands for knowledge; M, management; T, technology; C, collaboration; P, policy, standards, and regulation; H, hazards and accident conditions; PPE, personal protective equipment.

7.2 Reliability analysis

Cronbach’s alpha is a measure of internal consistency, i.e., how closely related a set of items are as a group [ 11 ]. It is considered to be a measure of scale reliability. The reliability of internal consistency most of the time is measured based on the Cronbach’s alpha value. Reliability coefficient of 0.70 and above is considered “acceptable” in most research situations [ 12 ]. In this study, reliability analysis for internal consistency of Likert-scale measurement after deleting 13 items was found similar; the reliability coefficients were found for 76 items were 0.964 and for the individual groupings made shown in Table 2 . It was also found internally consistent using the Cronbach’s alpha test. Table 2 shows the internal consistency of the seven major instruments in which their reliability falls in the acceptable range for this research.

7.3 Validity

Face validity used as defined by Babbie [ 13 ] is an indicator that makes it seem a reasonable measure of some variables, and it is the subjective judgment that the instrument measures what it intends to measure in terms of relevance [ 14 ]. Thus, the researcher ensured, in this study, when developing the instruments that uncertainties were eliminated by using appropriate words and concepts in order to enhance clarity and general suitability [ 14 ]. Furthermore, the researcher submitted the instruments to the research supervisor and the joint supervisor who are both occupational health experts, to ensure validity of the measuring instruments and determine whether the instruments could be considered valid on face value.

In this study, the researcher was guided by reviewed literature related to compliance with the occupational health and safety conditions and data collection methods before he could develop the measuring instruments. In addition, the pretest study that was conducted prior to the main study assisted the researcher to avoid uncertainties of the contents in the data collection measuring instruments. A thorough inspection of the measuring instruments by the statistician and the researcher’s supervisor and joint experts, to ensure that all concepts pertaining to the study were included, ensured that the instruments were enriched.

8. Data quality management

Insight has been given to the data collectors on how to approach companies, and many of the questionnaires were distributed through MSc students at Addis Ababa Institute of Technology (AAiT) and manufacturing industries’ experience experts. This made the data quality reliable as it has been continually discussed with them. Pretesting for questionnaire was done on 10 workers to assure the quality of the data and for improvement of data collection tools. Supervision during data collection was done to understand how the data collectors are handling the questionnaire, and each filled questionnaires was checked for its completeness, accuracy, clarity, and consistency on a daily basis either face-to-face or by phone/email. The data expected in poor quality were rejected out of the acting during the screening time. Among planned 267 questionnaires, 189 were responded back. Finally, it was analyzed by the principal investigator.

9. Inclusion criteria

The data were collected from the company representative with the knowledge of OSH. Articles written in English and Amharic were included in this study. Database information obtained in relation to articles and those who have OSH area such as interventions method, method of accident identification, impact of occupational accidents, types of occupational injuries/disease, and impact of occupational accidents, and disease on productivity and costs of company and have used at least one form of feedback mechanism. No specific time period was chosen in order to access all available published papers. The questionnaire statements which are similar in the questionnaire have been rejected from the data analysis.

10. Ethical consideration

Ethical clearance was obtained from the School of Mechanical and Industrial Engineering, Institute of Technology, Addis Ababa University. Official letters were written from the School of Mechanical and Industrial Engineering to the respective manufacturing industries. The purpose of the study was explained to the study subjects. The study subjects were told that the information they provided was kept confidential and that their identities would not be revealed in association with the information they provided. Informed consent was secured from each participant. For bad working environment assessment findings, feedback will be given to all manufacturing industries involved in the study. There is a plan to give a copy of the result to the respective study manufacturing industries’ and ministries’ offices. The respondents’ privacy and their responses were not individually analyzed and included in the report.

11. Dissemination and utilization of the result

The result of this study will be presented to the Addis Ababa University, AAiT, School of Mechanical and Industrial Engineering. It will also be communicated to the Ethiopian manufacturing industries, Ministry of Labor and Social Affair, Ministry of Industry, and Ministry of Health from where the data was collected. The result will also be availed by publication and online presentation in Google Scholars. To this end, about five articles were published and disseminated to the whole world.

12. Conclusion

The research methodology and design indicated overall process of the flow of the research for the given study. The data sources and data collection methods were used. The overall research strategies and framework are indicated in this research process from problem formulation to problem validation including all the parameters. It has laid some foundation and how research methodology is devised and framed for researchers. This means, it helps researchers to consider it as one of the samples and models for the research data collection and process from the beginning of the problem statement to the research finding. Especially, this research flow helps new researchers to the research environment and methodology in particular.

Conflict of interest

There is no “conflict of interest.”

• 1. Aaker A, Kumar VD, George S. Marketing Research. New York: John Wiley & Sons Inc; 2000
• 2. Saunders M, Lewis P, Thornhill A. Research Methods for Business Student. 5th ed. Edinburgh Gate: Pearson Education Limited; 2009
• 3. Miller P. Motivation in the Workplace. Work and Organizational Psychology. Oxford: Blackwell Publishers; 1991
• 4. Fraenkel FJ, Warren NE. How to Design and Evaluate Research in Education. 4th ed. New York: McGraw-Hill; 2002
• 5. Danniel WW. Biostatist: A Foundation for Analysis in the Health Science. 7th ed. New York: John Wiley & Sons; 1999
• 6. Cochran WG. Sampling Techniques. 3rd ed. New York: John Wiley & Sons; 1977
• 7. Saaty TL. The Analytical Hierarchy Process. Pittsburg: PWS Publications; 1990
• 8. Sekaran U, Bougie R. Research Methods for Business: A Skill Building Approach. 5th ed. New Delhi: John Wiley & Sons, Ltd; 2010. pp. 1-468
• 9. Luck DJ, Rubin RS. Marketing Research. 7th ed. New Jersey: Prentice-Hall International; 1987
• 10. Wong TC. Marketing Research. Oxford, UK: Butterworth-Heinemann; 1999
• 11. Cronbach LJ. Coefficient alpha and the internal structure of tests. Psychometrika. 1951; 16 :297-334
• 12. Tavakol M, Dennick R. Making sense of Cronbach’s alpha. International Journal of Medical Education. 2011; 2 :53-55. DOI: 10.5116/ijme.4dfb.8dfd
• 13. Babbie E. The Practice of Social Research. 12th ed. Belmont, CA: Wadsworth; 2010
• 14. Polit DF, Beck CT. Generating and Assessing Evidence for Nursing Practice. 8th ed. Williams and Wilkins: Lippincott; 2008

© 2019 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution 3.0 License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Continue reading from the same book

Edited by Evon Abu-Taieh

Published: 17 June 2020

By Sabína Gáliková Tolnaiová and Slavomír Gálik

1001 downloads

By Carlos Pedro Gonçalves

1540 downloads

By Konstantinos-George Thanos, Andrianna Polydouri, A...

1040 downloads

Root out friction in every digital experience, super-charge conversion rates, and optimize digital self-service

Uncover insights from any interaction, deliver AI-powered agent coaching, and reduce cost to serve

Increase revenue and loyalty with real-time insights and recommendations delivered to teams on the ground

Know how your people feel and empower managers to improve employee engagement, productivity, and retention

Take action in the moments that matter most along the employee journey and drive bottom line growth

Whatever they’re are saying, wherever they’re saying it, know exactly what’s going on with your people

Get faster, richer insights with qual and quant tools that make powerful market research available to everyone

Run concept tests, pricing studies, prototyping + more with fast, powerful studies designed by UX research experts

Track your brand performance 24/7 and act quickly to respond to opportunities and challenges in your market

Explore the platform powering Experience Management

• Free Account
• For Digital
• For Customer Care
• For Human Resources
• For Researchers
• Financial Services
• All Industries

Popular Use Cases

• Customer Experience
• Employee Experience
• Employee Exit Interviews
• Net Promoter Score
• Voice of Customer
• Customer Success Hub
• Product Documentation
• Training & Certification
• XM Institute
• Popular Resources
• Customer Stories

Market Research

• Artificial Intelligence
• Partnerships
• Marketplace

The annual gathering of the experience leaders at the world’s iconic brands building breakthrough business results, live in Salt Lake City.

• English/AU & NZ
• Español/Europa
• Español/América Latina
• Português Brasileiro
• REQUEST DEMO
• Experience Management
• Primary vs Secondary Research

Try Qualtrics for free

Primary vs secondary research – what’s the difference.

14 min read Find out how primary and secondary research are different from each other, and how you can use them both in your own research program.

Primary vs secondary research: in a nutshell

The essential difference between primary and secondary research lies in who collects the data.

• Primary research definition

When you conduct primary research, you’re collecting data by doing your own surveys or observations.

• Secondary research definition:

In secondary research, you’re looking at existing data from other researchers, such as academic journals, government agencies or national statistics.

Free Ebook: The Qualtrics Handbook of Question Design

When to use primary vs secondary research

Primary research and secondary research both offer value in helping you gather information.

Each research method can be used alone to good effect. But when you combine the two research methods, you have the ingredients for a highly effective market research strategy. Most research combines some element of both primary methods and secondary source consultation.

So assuming you’re planning to do both primary and secondary research – which comes first? Counterintuitive as it sounds, it’s more usual to start your research process with secondary research, then move on to primary research.

Secondary research can prepare you for collecting your own data in a primary research project. It can give you a broad overview of your research area, identify influences and trends, and may give you ideas and avenues to explore that you hadn’t previously considered.

Given that secondary research can be done quickly and inexpensively, it makes sense to start your primary research process with some kind of secondary research. Even if you’re expecting to find out what you need to know from a survey of your target market, taking a small amount of time to gather information from secondary sources is worth doing.

Primary research

Primary market research is original research carried out when a company needs timely, specific data about something that affects its success or potential longevity.

Primary research data collection might be carried out in-house by a business analyst or market research team within the company, or it may be outsourced to a specialist provider, such as an agency or consultancy. While outsourcing primary research involves a greater upfront expense, it’s less time consuming and can bring added benefits such as researcher expertise and a ‘fresh eyes’ perspective that avoids the risk of bias and partiality affecting the research data.

Primary research gives you recent data from known primary sources about the particular topic you care about, but it does take a little time to collect that data from scratch, rather than finding secondary data via an internet search or library visit.

Primary research involves two forms of data collection:

• Exploratory research This type of primary research is carried out to determine the nature of a problem that hasn’t yet been clearly defined. For example, a supermarket wants to improve its poor customer service and needs to understand the key drivers behind the customer experience issues. It might do this by interviewing employees and customers, or by running a survey program or focus groups.
• Conclusive research This form of primary research is carried out to solve a problem that the exploratory research – or other forms of primary data – has identified. For example, say the supermarket’s exploratory research found that employees weren’t happy. Conclusive research went deeper, revealing that the manager was rude, unreasonable, and difficult, making the employees unhappy and resulting in a poor employee experience which in turn led to less than excellent customer service. Thanks to the company’s choice to conduct primary research, a new manager was brought in, employees were happier and customer service improved.

Examples of primary research

All of the following are forms of primary research data.

• Customer satisfaction survey results
• Employee experience pulse survey results
• NPS rating scores from your customers
• A field researcher’s notes
• Data from weather stations in a local area
• Recordings made during focus groups

Primary research methods

There are a number of primary research methods to choose from, and they are already familiar to most people. The ones you choose will depend on your budget, your time constraints, your research goals and whether you’re looking for quantitative or qualitative data.

A survey can be carried out online, offline, face to face or via other media such as phone or SMS. It’s relatively cheap to do, since participants can self-administer the questionnaire in most cases. You can automate much of the process if you invest in good quality survey software.

Primary research interviews can be carried out face to face, over the phone or via video calling. They’re more time-consuming than surveys, and they require the time and expense of a skilled interviewer and a dedicated room, phone line or video calling setup. However, a personal interview can provide a very rich primary source of data based not only on the participant’s answers but also on the observations of the interviewer.

Focus groups

A focus group is an interview with multiple participants at the same time. It often takes the form of a discussion moderated by the researcher. As well as taking less time and resources than a series of one-to-one interviews, a focus group can benefit from the interactions between participants which bring out more ideas and opinions. However this can also lead to conversations going off on a tangent, which the moderator must be able to skilfully avoid by guiding the group back to the relevant topic.

Secondary research

Secondary research is research that has already been done by someone else prior to your own research study.

Secondary research is generally the best place to start any research project as it will reveal whether someone has already researched the same topic you’re interested in, or a similar topic that helps lay some of the groundwork for your research project.

Even if your preliminary secondary research doesn’t turn up a study similar to your own research goals, it will still give you a stronger knowledge base that you can use to strengthen and refine your research hypothesis. You may even find some gaps in the market you didn’t know about before.

The scope of secondary research resources is extremely broad. Here are just a few of the places you might look for relevant information.

Books and magazines

A public library can turn up a wealth of data in the form of books and magazines – and it doesn’t cost a penny to consult them.

Market research reports

Secondary research from professional research agencies can be highly valuable, as you can be confident the data collection methods and data analysis will be sound

Scholarly journals, often available in reference libraries

Peer-reviewed journals have been examined by experts from the relevant educational institutions, meaning there has been an extra layer of oversight and careful consideration of the data points before publication.

Government reports and studies

Public domain data, such as census data, can provide relevant information for your research project, not least in choosing the appropriate research population for a primary research method. If the information you need isn’t readily available, try contacting the relevant government agencies.

White papers

Businesses often produce white papers as a means of showcasing their expertise and value in their field. White papers can be helpful in secondary research methods, although they may not be as carefully vetted as academic papers or public records.

Trade or industry associations

Associations may have secondary data that goes back a long way and offers a general overview of a particular industry. This data collected over time can be very helpful in laying the foundations of your particular research project.

Private company data

Some businesses may offer their company data to those conducting research in return for fees or with explicit permissions. However, if a business has data that’s closely relevant to yours, it’s likely they are a competitor and may flat out refuse your request.

Learn more about secondary research

Examples of secondary research data

These are all forms of secondary research data in action:

• A newspaper report quoting statistics sourced by a journalist
• Facts from primary research articles quoted during a debate club meeting
• A blog post discussing new national figures on the economy
• A company consulting previous research published by a competitor

Secondary research methods

Literature reviews.

A core part of the secondary research process, involving data collection and constructing an argument around multiple sources. A literature review involves gathering information from a wide range of secondary sources on one topic and summarizing them in a report or in the introduction to primary research data.

Content analysis

This systematic approach is widely used in social science disciplines. It uses codes for themes, tropes or key phrases which are tallied up according to how often they occur in the secondary data. The results help researchers to draw conclusions from qualitative data.

Data analysis using digital tools

You can analyze large volumes of data using software that can recognize and categorize natural language. More advanced tools will even be able to identify relationships and semantic connections within the secondary research materials.

Comparing primary vs secondary research

We’ve established that both primary research and secondary research have benefits for your business, and that there are major differences in terms of the research process, the cost, the research skills involved and the types of data gathered. But is one of them better than the other?

The answer largely depends on your situation. Whether primary or secondary research wins out in your specific case depends on the particular topic you’re interested in and the resources you have available. The positive aspects of one method might be enough to sway you, or the drawbacks – such as a lack of credible evidence already published, as might be the case in very fast-moving industries – might make one method totally unsuitable.

Here’s an at-a-glance look at the features and characteristics of primary vs secondary research, illustrating some of the key differences between them.

What are the pros and cons of primary research?

Primary research provides original data and allows you to pinpoint the issues you’re interested in and collect data from your target market – with all the effort that entails.

Benefits of primary research:

• Tells you what you need to know, nothing irrelevant
• Yours exclusively – once acquired, you may be able to sell primary data or use it for marketing
• Teaches you more about your business
• Can help foster new working relationships and connections between silos
• Primary research methods can provide upskilling opportunities – employees gain new research skills

Limitations of primary research:

• Lacks context from other research on related subjects
• Can be expensive
• Results aren’t ready to use until the project is complete
• Any mistakes you make in in research design or implementation could compromise your data quality
• May not have lasting relevance – although it could fulfill a benchmarking function if things change

What are the pros and cons of secondary research?

Secondary research relies on secondary sources, which can be both an advantage and a drawback. After all, other people are doing the work, but they’re also setting the research parameters.

Benefits of secondary research:

• It’s often low cost or even free to access in the public domain
• Supplies a knowledge base for researchers to learn from
• Data is complete, has been analyzed and checked, saving you time and costs
• It’s ready to use as soon as you acquire it

Limitations of secondary research

• May not provide enough specific information
• Conducting a literature review in a well-researched subject area can become overwhelming
• No added value from publishing or re-selling your research data
• Results are inconclusive – you’ll only ever be interpreting data from another organization’s experience, not your own
• Details of the research methodology are unknown
• May be out of date – always check carefully the original research was conducted

Related resources

Business research methods 12 min read, qualitative research interviews 11 min read, market intelligence 10 min read, marketing insights 11 min read, ethnographic research 11 min read, qualitative vs quantitative research 13 min read, qualitative research questions 11 min read, request demo.

Ready to learn more about Qualtrics?

Want to create or adapt books like this? Learn more about how Pressbooks supports open publishing practices.

Unit 3: Getting to, and thinking critically about, the [Secondary] Research

11 *primary vs secondary research.

Students tend to get a wee bit discombobulated in this chapter and I’ll tell you why — it’s those STINKIN’ PRIMARY SOURCES !

In 1305 we’re talking about social scientific r esearch methods and primary RESEARCH . Although our humanistic friends give us some great advice about how to evaluate a primary source , a primary source is pretty darned different from primary  research.

Learning Objectives

What is the difference between primary and secondary research? When and how to utilize each type of research? And how is primary research different from a primary source??

• Primary vs Secondary Research

Research can be categorized into two types: Primary vs Secondary

Primary  Research is original research (e.g., qualitative, quantitative ) that a researcher performs to explore or answer a question. This is the type of research a scientist performs to answer their own questions with data that they collect themselves .

Ex. conducting scholarly interviews, surveys, analysis, observations, etc. Used to create new science (then published via journal or book).

Secondary Research is research has been conducted independently of you is used to further your own research or knowledge. Often published in journals and accessible through databases. By conducting secondary research, you can develop a combination or summary of existing research. These resources can be used to help form your research question or hypothesis, then conduct your own primary research. Mainly used for preparing primary research.

Ex. Traditional textbooks, market research, literature reviews

These two types of research can be distinguished by thinking of primary as the original person conducting their own new research and secondary as a summary or collection of existing original, primary research.

So, what’s the big deal about Secondary Research? 

Five main purposes of secondary research:.

• While reading secondary research regarding a topic, researchers can see what research has already been done and how they might attempt to further study the topic or what questions they might attempt to answer.
• Through reading compiled summaries of past research, researchers can see what problems or limitations other studies have encountered, and formulate ways to counteract that.
• By comparing to past research, researchers can create a plan for which methodology and sampling method they will use.
• Secondary research can provide a clearer picture of the population the researcher intends to reach, and which avenues are best to represent the population most effectively.
• Comparing your data to other study’s data can be helpful to see what new things may have been found in your study.  

No Primary research should be done without conducting Secondary research first!

TEXTBOOK CONTRIBUTION

Example contributed by: Monica Buchholz, Erica Cole, Charlie Boyle, Marvin Gutierrez, Edwin Leon

Here are some key points to remember about primary v secondary research compiled by Caroline McCullough and Abby Lundvall! (Fall 2022)

• What is a journal?
• Research through databases
• Publishing and Predators

Introduction to Social Scientific Research Methods in the field of Communication 3rd Ed - under construction for Fall 2023 Copyright © 2023 by Kate Magsamen-Conrad. All Rights Reserved.

Share This Book

• Privacy Policy

Buy Me a Coffee

Home » Research Objectives – Types, Examples and Writing Guide

Research Objectives – Types, Examples and Writing Guide

Table of Contents

Research Objectives

Research objectives refer to the specific goals or aims of a research study. They provide a clear and concise description of what the researcher hopes to achieve by conducting the research . The objectives are typically based on the research questions and hypotheses formulated at the beginning of the study and are used to guide the research process.

Types of Research Objectives

Here are the different types of research objectives in research:

• Exploratory Objectives: These objectives are used to explore a topic, issue, or phenomenon that has not been studied in-depth before. The aim of exploratory research is to gain a better understanding of the subject matter and generate new ideas and hypotheses .
• Descriptive Objectives: These objectives aim to describe the characteristics, features, or attributes of a particular population, group, or phenomenon. Descriptive research answers the “what” questions and provides a snapshot of the subject matter.
• Explanatory Objectives : These objectives aim to explain the relationships between variables or factors. Explanatory research seeks to identify the cause-and-effect relationships between different phenomena.
• Predictive Objectives: These objectives aim to predict future events or outcomes based on existing data or trends. Predictive research uses statistical models to forecast future trends or outcomes.
• Evaluative Objectives : These objectives aim to evaluate the effectiveness or impact of a program, intervention, or policy. Evaluative research seeks to assess the outcomes or results of a particular intervention or program.
• Prescriptive Objectives: These objectives aim to provide recommendations or solutions to a particular problem or issue. Prescriptive research identifies the best course of action based on the results of the study.
• Diagnostic Objectives : These objectives aim to identify the causes or factors contributing to a particular problem or issue. Diagnostic research seeks to uncover the underlying reasons for a particular phenomenon.
• Comparative Objectives: These objectives aim to compare two or more groups, populations, or phenomena to identify similarities and differences. Comparative research is used to determine which group or approach is more effective or has better outcomes.
• Historical Objectives: These objectives aim to examine past events, trends, or phenomena to gain a better understanding of their significance and impact. Historical research uses archival data, documents, and records to study past events.
• Ethnographic Objectives : These objectives aim to understand the culture, beliefs, and practices of a particular group or community. Ethnographic research involves immersive fieldwork and observation to gain an insider’s perspective of the group being studied.
• Action-oriented Objectives: These objectives aim to bring about social or organizational change. Action-oriented research seeks to identify practical solutions to social problems and to promote positive change in society.
• Conceptual Objectives: These objectives aim to develop new theories, models, or frameworks to explain a particular phenomenon or set of phenomena. Conceptual research seeks to provide a deeper understanding of the subject matter by developing new theoretical perspectives.
• Methodological Objectives: These objectives aim to develop and improve research methods and techniques. Methodological research seeks to advance the field of research by improving the validity, reliability, and accuracy of research methods and tools.
• Theoretical Objectives : These objectives aim to test and refine existing theories or to develop new theoretical perspectives. Theoretical research seeks to advance the field of knowledge by testing and refining existing theories or by developing new theoretical frameworks.
• Measurement Objectives : These objectives aim to develop and validate measurement instruments, such as surveys, questionnaires, and tests. Measurement research seeks to improve the quality and reliability of data collection and analysis by developing and testing new measurement tools.
• Design Objectives : These objectives aim to develop and refine research designs, such as experimental, quasi-experimental, and observational designs. Design research seeks to improve the quality and validity of research by developing and testing new research designs.
• Sampling Objectives: These objectives aim to develop and refine sampling techniques, such as probability and non-probability sampling methods. Sampling research seeks to improve the representativeness and generalizability of research findings by developing and testing new sampling techniques.

How to Write Research Objectives

Writing clear and concise research objectives is an important part of any research project, as it helps to guide the study and ensure that it is focused and relevant. Here are some steps to follow when writing research objectives:

• Identify the research problem : Before you can write research objectives, you need to identify the research problem you are trying to address. This should be a clear and specific problem that can be addressed through research.
• Define the research questions : Based on the research problem, define the research questions you want to answer. These questions should be specific and should guide the research process.
• Identify the variables : Identify the key variables that you will be studying in your research. These are the factors that you will be measuring, manipulating, or analyzing to answer your research questions.
• Write specific objectives: Write specific, measurable objectives that will help you answer your research questions. These objectives should be clear and concise and should indicate what you hope to achieve through your research.
• Use the SMART criteria: To ensure that your research objectives are well-defined and achievable, use the SMART criteria. This means that your objectives should be Specific, Measurable, Achievable, Relevant, and Time-bound.
• Revise and refine: Once you have written your research objectives, revise and refine them to ensure that they are clear, concise, and achievable. Make sure that they align with your research questions and variables, and that they will help you answer your research problem.

Example of Research Objectives

Examples of research objectives Could be:

Research Objectives for the topic of “The Impact of Artificial Intelligence on Employment”:

• To investigate the effects of the adoption of AI on employment trends across various industries and occupations.
• To explore the potential for AI to create new job opportunities and transform existing roles in the workforce.
• To examine the social and economic implications of the widespread use of AI for employment, including issues such as income inequality and access to education and training.
• To identify the skills and competencies that will be required for individuals to thrive in an AI-driven workplace, and to explore the role of education and training in developing these skills.
• To evaluate the ethical and legal considerations surrounding the use of AI for employment, including issues such as bias, privacy, and the responsibility of employers and policymakers to protect workers’ rights.

When to Write Research Objectives

• At the beginning of a research project : Research objectives should be identified and written down before starting a research project. This helps to ensure that the project is focused and that data collection and analysis efforts are aligned with the intended purpose of the research.
• When refining research questions: Writing research objectives can help to clarify and refine research questions. Objectives provide a more concrete and specific framework for addressing research questions, which can improve the overall quality and direction of a research project.
• After conducting a literature review : Conducting a literature review can help to identify gaps in knowledge and areas that require further research. Writing research objectives can help to define and focus the research effort in these areas.
• When developing a research proposal: Research objectives are an important component of a research proposal. They help to articulate the purpose and scope of the research, and provide a clear and concise summary of the expected outcomes and contributions of the research.
• When seeking funding for research: Funding agencies often require a detailed description of research objectives as part of a funding proposal. Writing clear and specific research objectives can help to demonstrate the significance and potential impact of a research project, and increase the chances of securing funding.
• When designing a research study : Research objectives guide the design and implementation of a research study. They help to identify the appropriate research methods, sampling strategies, data collection and analysis techniques, and other relevant aspects of the study design.
• When communicating research findings: Research objectives provide a clear and concise summary of the main research questions and outcomes. They are often included in research reports and publications, and can help to ensure that the research findings are communicated effectively and accurately to a wide range of audiences.
• When evaluating research outcomes : Research objectives provide a basis for evaluating the success of a research project. They help to measure the degree to which research questions have been answered and the extent to which research outcomes have been achieved.
• When conducting research in a team : Writing research objectives can facilitate communication and collaboration within a research team. Objectives provide a shared understanding of the research purpose and goals, and can help to ensure that team members are working towards a common objective.

Purpose of Research Objectives

Some of the main purposes of research objectives include:

• To clarify the research question or problem : Research objectives help to define the specific aspects of the research question or problem that the study aims to address. This makes it easier to design a study that is focused and relevant.
• To guide the research design: Research objectives help to determine the research design, including the research methods, data collection techniques, and sampling strategy. This ensures that the study is structured and efficient.
• To measure progress : Research objectives provide a way to measure progress throughout the research process. They help the researcher to evaluate whether they are on track and meeting their goals.
• To communicate the research goals : Research objectives provide a clear and concise description of the research goals. This helps to communicate the purpose of the study to other researchers, stakeholders, and the general public.

Advantages of Research Objectives

Here are some advantages of having well-defined research objectives:

• Focus : Research objectives help to focus the research effort on specific areas of inquiry. By identifying clear research questions, the researcher can narrow down the scope of the study and avoid getting sidetracked by irrelevant information.
• Clarity : Clearly stated research objectives provide a roadmap for the research study. They provide a clear direction for the research, making it easier for the researcher to stay on track and achieve their goals.
• Measurability : Well-defined research objectives provide measurable outcomes that can be used to evaluate the success of the research project. This helps to ensure that the research is effective and that the research goals are achieved.
• Feasibility : Research objectives help to ensure that the research project is feasible. By clearly defining the research goals, the researcher can identify the resources required to achieve those goals and determine whether those resources are available.
• Relevance : Research objectives help to ensure that the research study is relevant and meaningful. By identifying specific research questions, the researcher can ensure that the study addresses important issues and contributes to the existing body of knowledge.

About the author

Muhammad Hassan

Researcher, Academic Writer, Web developer

You may also like

How to Cite Research Paper – All Formats and...

Data Collection – Methods Types and Examples

Delimitations in Research – Types, Examples and...

Research Paper Format – Types, Examples and...

Research Process – Steps, Examples and Tips

Research Design – Types, Methods and Examples

Library Guides

Dissertations 4: methodology: methods.

• Introduction & Philosophy
• Methodology

Primary & Secondary Sources, Primary & Secondary Data

When describing your research methods, you can start by stating what kind of secondary and, if applicable, primary sources you used in your research. Explain why you chose such sources, how well they served your research, and identify possible issues encountered using these sources.  

Definitions  

There is some confusion on the use of the terms primary and secondary sources, and primary and secondary data. The confusion is also due to disciplinary differences (Lombard 2010). Whilst you are advised to consult the research methods literature in your field, we can generalise as follows:  

Secondary sources 

Secondary sources normally include the literature (books and articles) with the experts' findings, analysis and discussions on a certain topic (Cottrell, 2014, p123). Secondary sources often interpret primary sources.  

Primary sources 

Primary sources are "first-hand" information such as raw data, statistics, interviews, surveys, law statutes and law cases. Even literary texts, pictures and films can be primary sources if they are the object of research (rather than, for example, documentaries reporting on something else, in which case they would be secondary sources). The distinction between primary and secondary sources sometimes lies on the use you make of them (Cottrell, 2014, p123). 

Primary data 

Primary data are data (primary sources) you directly obtained through your empirical work (Saunders, Lewis and Thornhill 2015, p316). 

Secondary data 

Secondary data are data (primary sources) that were originally collected by someone else (Saunders, Lewis and Thornhill 2015, p316).   

Comparison between primary and secondary data   

Use  

Virtually all research will use secondary sources, at least as background information. 

Often, especially at the postgraduate level, it will also use primary sources - secondary and/or primary data. The engagement with primary sources is generally appreciated, as less reliant on others' interpretations, and closer to 'facts'. 

The use of primary data, as opposed to secondary data, demonstrates the researcher's effort to do empirical work and find evidence to answer her specific research question and fulfill her specific research objectives. Thus, primary data contribute to the originality of the research.    

Ultimately, you should state in this section of the methodology: 

What sources and data you are using and why (how are they going to help you answer the research question and/or test the hypothesis. 

If using primary data, why you employed certain strategies to collect them. 

What the advantages and disadvantages of your strategies to collect the data (also refer to the research in you field and research methods literature). 

Quantitative, Qualitative & Mixed Methods

The methodology chapter should reference your use of quantitative research, qualitative research and/or mixed methods. The following is a description of each along with their advantages and disadvantages. 

Quantitative research 

Quantitative research uses numerical data (quantities) deriving, for example, from experiments, closed questions in surveys, questionnaires, structured interviews or published data sets (Cottrell, 2014, p93). It normally processes and analyses this data using quantitative analysis techniques like tables, graphs and statistics to explore, present and examine relationships and trends within the data (Saunders, Lewis and Thornhill, 2015, p496). 

Qualitative research  

Qualitative research is generally undertaken to study human behaviour and psyche. It uses methods like in-depth case studies, open-ended survey questions, unstructured interviews, focus groups, or unstructured observations (Cottrell, 2014, p93). The nature of the data is subjective, and also the analysis of the researcher involves a degree of subjective interpretation. Subjectivity can be controlled for in the research design, or has to be acknowledged as a feature of the research. Subject-specific books on (qualitative) research methods offer guidance on such research designs.  

Mixed methods 

Mixed-method approaches combine both qualitative and quantitative methods, and therefore combine the strengths of both types of research. Mixed methods have gained popularity in recent years.  

When undertaking mixed-methods research you can collect the qualitative and quantitative data either concurrently or sequentially. If sequentially, you can for example, start with a few semi-structured interviews, providing qualitative insights, and then design a questionnaire to obtain quantitative evidence that your qualitative findings can also apply to a wider population (Specht, 2019, p138). 

Ultimately, your methodology chapter should state: 

Whether you used quantitative research, qualitative research or mixed methods. 

Why you chose such methods (and refer to research method sources). 

Why you rejected other methods. 

How well the method served your research. 

The problems or limitations you encountered. 

Doug Specht, Senior Lecturer at the Westminster School of Media and Communication, explains mixed methods research in the following video:

LinkedIn Learning Video on Academic Research Foundations: Quantitative

The video covers the characteristics of quantitative research, and explains how to approach different parts of the research process, such as creating a solid research question and developing a literature review. He goes over the elements of a study, explains how to collect and analyze data, and shows how to present your data in written and numeric form.

Link to quantitative research video

Some Types of Methods

There are several methods you can use to get primary data. To reiterate, the choice of the methods should depend on your research question/hypothesis. 

Whatever methods you will use, you will need to consider: 

why did you choose one technique over another? What were the advantages and disadvantages of the technique you chose? 

what was the size of your sample? Who made up your sample? How did you select your sample population? Why did you choose that particular sampling strategy?) 

ethical considerations (see also tab...)  

safety considerations  

validity  

feasibility  

recording  

procedure of the research (see box procedural method...).  

Check Stella Cottrell's book  Dissertations and Project Reports: A Step by Step Guide  for some succinct yet comprehensive information on most methods (the following account draws mostly on her work). Check a research methods book in your discipline for more specific guidance.  

Experiments 

Experiments are useful to investigate cause and effect, when the variables can be tightly controlled. They can test a theory or hypothesis in controlled conditions. Experiments do not prove or disprove an hypothesis, instead they support or not support an hypothesis. When using the empirical and inductive method it is not possible to achieve conclusive results. The results may only be valid until falsified by other experiments and observations. 

For more information on Scientific Method, click here . 

Observations 

Observational methods are useful for in-depth analyses of behaviours in people, animals, organisations, events or phenomena. They can test a theory or products in real life or simulated settings. They generally a qualitative research method.  

Questionnaires and surveys 

Questionnaires and surveys are useful to gain opinions, attitudes, preferences, understandings on certain matters. They can provide quantitative data that can be collated systematically; qualitative data, if they include opportunities for open-ended responses; or both qualitative and quantitative elements. 

Interviews  

Interviews are useful to gain rich, qualitative information about individuals' experiences, attitudes or perspectives. With interviews you can follow up immediately on responses for clarification or further details. There are three main types of interviews: structured (following a strict pattern of questions, which expect short answers), semi-structured (following a list of questions, with the opportunity to follow up the answers with improvised questions), and unstructured (following a short list of broad questions, where the respondent can lead more the conversation) (Specht, 2019, p142). 

This short video on qualitative interviews discusses best practices and covers qualitative interview design, preparation and data collection methods. 

Focus groups   

In this case, a group of people (normally, 4-12) is gathered for an interview where the interviewer asks questions to such group of participants. Group interactions and discussions can be highly productive, but the researcher has to beware of the group effect, whereby certain participants and views dominate the interview (Saunders, Lewis and Thornhill 2015, p419). The researcher can try to minimise this by encouraging involvement of all participants and promoting a multiplicity of views. 

This video focuses on strategies for conducting research using focus groups.  

Check out the guidance on online focus groups by Aliaksandr Herasimenka, which is attached at the bottom of this text box. 

Case study 

Case studies are often a convenient way to narrow the focus of your research by studying how a theory or literature fares with regard to a specific person, group, organisation, event or other type of entity or phenomenon you identify. Case studies can be researched using other methods, including those described in this section. Case studies give in-depth insights on the particular reality that has been examined, but may not be representative of what happens in general, they may not be generalisable, and may not be relevant to other contexts. These limitations have to be acknowledged by the researcher.     

Content analysis 

Content analysis consists in the study of words or images within a text. In its broad definition, texts include books, articles, essays, historical documents, speeches, conversations, advertising, interviews, social media posts, films, theatre, paintings or other visuals. Content analysis can be quantitative (e.g. word frequency) or qualitative (e.g. analysing intention and implications of the communication). It can detect propaganda, identify intentions of writers, and can see differences in types of communication (Specht, 2019, p146). Check this page on collecting, cleaning and visualising Twitter data.

Extra links and resources:  

Research Methods  

A clear and comprehensive overview of research methods by Emerald Publishing. It includes: crowdsourcing as a research tool; mixed methods research; case study; discourse analysis; ground theory; repertory grid; ethnographic method and participant observation; interviews; focus group; action research; analysis of qualitative data; survey design; questionnaires; statistics; experiments; empirical research; literature review; secondary data and archival materials; data collection. 

Doing your dissertation during the COVID-19 pandemic  

Resources providing guidance on doing dissertation research during the pandemic: Online research methods; Secondary data sources; Webinars, conferences and podcasts; 

• Virtual Focus Groups Guidance on managing virtual focus groups

5 Minute Methods Videos

The following are a series of useful videos that introduce research methods in five minutes. These resources have been produced by lecturers and students with the University of Westminster's School of Media and Communication. 

Case Study Research

Research Ethics

Quantitative Content Analysis 

Sequential Analysis 

Qualitative Content Analysis 

Thematic Analysis 

Social Media Research 

Mixed Method Research 

Procedural Method

In this part, provide an accurate, detailed account of the methods and procedures that were used in the study or the experiment (if applicable!). 

Include specifics about participants, sample, materials, design and methods. 

If the research involves human subjects, then include a detailed description of who and how many participated along with how the participants were selected.  

Describe all materials used for the study, including equipment, written materials and testing instruments. 

Identify the study's design and any variables or controls employed. 

Write out the steps in the order that they were completed. 

Indicate what participants were asked to do, how measurements were taken and any calculations made to raw data collected. 

Specify statistical techniques applied to the data to reach your conclusions. 

Provide evidence that you incorporated rigor into your research. This is the quality of being thorough and accurate and considers the logic behind your research design. 

Highlight any drawbacks that may have limited your ability to conduct your research thoroughly. 

You have to provide details to allow others to replicate the experiment and/or verify the data, to test the validity of the research. 

Bibliography

Cottrell, S. (2014). Dissertations and project reports: a step by step guide. Hampshire, England: Palgrave Macmillan.

Lombard, E. (2010). Primary and secondary sources.  The Journal of Academic Librarianship , 36(3), 250-253

Saunders, M.N.K., Lewis, P. and Thornhill, A. (2015).  Research Methods for Business Students.  New York: Pearson Education. 

Specht, D. (2019).  The Media And Communications Study Skills Student Guide . London: University of Westminster Press.  

• << Previous: Introduction & Philosophy
• Next: Ethics >>
• Last Updated: Sep 14, 2022 12:58 PM
• URL: https://libguides.westminster.ac.uk/methodology-for-dissertations

CONNECT WITH US

Primary Research: Methods and Best Practices

Introduction

What is the definition of primary research, what are examples of primary research, primary vs. secondary research, types of primary research, when to use primary research.

Conducting research involves two types of data: primary data and secondary data . While secondary research deals with existing data, primary research collects new data . Ultimately, the most appropriate type of research depends on which method is best suited to your research question .

While this article discusses the difference between primary and secondary research, the main focus is on primary research, the types of data collected through primary research, and considerations for researchers who conduct primary research.

Simply put, researchers conduct primary research to gather new information. When existing data cannot address the research inquiry at hand, the researcher usually needs to collect new data to meet their research objectives.

How do you identify primary research?

Primary research uses collected data that hasn't been previously documented. Primary research typically means collecting data straight from the source (e.g., interviewing a research participant, observing a cultural practice or phenomenon firsthand).

Note that other divides that you should also consider include that of collecting quantitative or qualitative data , and of conducting basic or applied research . Each of these dimensions informs and is informed by your research inquiry.

What are the advantages of primary research?

New data, particularly that which addresses a research gap, can contribute to a novel inquiry and prove compelling to the research audience. When a researcher conducts a literature review and generates a problem statement for their research, they can identify what new data needs to be collected and what primary research method can be used to collect it.

Primary research studies ultimately contribute to theoretical developments and novel insights that an analysis of existing data might not have identified. Research publications in some fields may place a premium on primary research for its potential to generate new scientific knowledge as a result.

What are the disadvantages of primary research?

Primary research is time-consuming and potentially expensive to conduct, considering the equipment and resources needed to collect new data as well as the time required to engage with the field and collect data.

Moreover, primary research relies on new data that has yet to be documented elsewhere, meaning that the research audience is less familiar with the primary data being presented. This might raise issues of transparency and research rigor (e.g., how does the audience know that the data they are shown is trustworthy?).

Primary research is common in various fields of research. Let's look at some typical examples of primary research in three different areas.

Education research

Teaching and learning is a field that relies on evidence-based data to make policy recommendations affecting teachers, learning materials, and even classroom requirements. As a result, there are countless methods for collecting relevant data on the various aspects of education.

Observations , interviews , and assessments are just some of the primary research methods that are employed when studying education contexts. Education research acknowledges the full variety of situated differences found in the diversity of learners and their schooling contexts. This makes collecting data that is relevant to the given context and research inquiry crucial to understanding teaching and learning.

Market research

Businesses often rely on primary research to understand the target market for their products and services. Since competing businesses tend not to share research on customer insights with each other, primary research collecting original data can be a necessity.

Focus groups , surveys , and user research are typical research tools employed by businesses. Within market research, the goal is typically to understand customers' preferences and use cases for specific products and services.

Cultural studies

Fields such as anthropology and sociology count on primary research for understanding cultures and communities. Ethnographic research acknowledges that thick description of cultures and phenomena is more meaningful than only generating universal theories, making the collection of primary data essential to understanding the full diversity of the social world.

Researchers examining culture often collect data through interviews, observations, and photovoice, among other research methods. These methods look at the social world through the eyes of the research participants to generate an immersive view of cultures and groups with which audiences may not be familiar.

Insights from data are at your fingertips with ATLAS.ti

See how our powerful data analysis interface can help you make sense of data. Start with a free trial.

Primary research data stands in contrast to secondary research data, which is any data that has been previously collected and documented. In some situations, existing data may be abundant and available, making secondary research a more feasible approach to generating theory and identifying key insights.

Secondary research methods are employed in all fields of research. Market researchers conduct secondary research when there is already existing data about a target market. In particular, secondary market research might look at previous trends in the popularity of products to make predictions about the demand for new products.

Scholarly researchers can use secondary sources such as corpora, news articles, and online videos to make assertions about language and culture. Analytical approaches such as discourse analysis and content analysis can be well suited to analyzing data collected through secondary research methods.

Ultimately, primary and secondary research go hand in hand. The main function of research in building knowledge does not necessarily depend on the use of primary data collection . Rather, it is a matter of whether data needs to be collected in order to address your research inquiry, or relevant data already exists and you can access it.

There are many research methods used to collect data for primary research. The research method that works best for you depends on what you are looking to do with your research project.

This section lists some of the common primary data collection methods that researchers rely on.

One-on-one interviews are useful for capturing perspectives from research participants. Direct interactions can tell researchers what perspectives their research participants have and the thinking behind those perspectives.

Interview research is a complex and detailed methodology that includes several types of interviews to suit various research inquiries. Researchers can choose between structured interviews , semi-structured interviews , and unstructured interviews , depending on the nature of interaction they are looking to establish.

Focus groups

Focus groups are discussions that involve multiple research participants and are led by a moderator. Similar to interviews, the primary goal is to gather information about people's perspectives. Yet focus groups are distinct, because they can capture how people interact and build meaning when discussing a particular topic.

Market researchers may consider conducting a focus group discussion when they want to know more about how a particular group feels about a product or service. Researchers in linguistics and anthropology might be interested in observing how a group of people construct meaning with each other.

Observations

In research involving naturalistic inquiry and the social world, the researcher can gather information directly from the field through observational research methods . Primary data takes the form of field notes , audio and video recordings , their resulting transcripts , and even images of objects of interest.

For quantitative research inquiries, observation entails measuring the amount of activity or the frequency of particular phenomena. Qualitative observations look for patterns in cultural or social practices and document significant events in the field.

When the objective is to capture perspectives from large numbers of people, surveys are a good research method for collecting novel data. In-person questionnaires and online surveys can be used to quickly collect data at scale.

Surveys are used for conducting primary research in both quantitative and qualitative research . The structure of survey questions provide data that can be measured quantitatively, while open-ended survey responses require qualitative data analysis .

Experiments

While the above methods emphasize or are involved with naturalistic inquiry, experiments are a different form of primary research that is far more controlled. When you want to understand the relationship between various elements in a certain context (e.g., the effect of water and fertilizer on plant growth), a controlled experiment is a typical research approach to empirically establish scientific knowledge.

Experiments focus on a specific set of factors from the research phenomenon to understand causal relationships between variables. Experiments are a common primary research method in physical sciences, but they are also extensively used in psychology, education, and political science, among other areas.

The decision to conduct a primary or secondary study is a question of whether existing data is sufficient to satisfy the research inquiry at hand. Where data does not exist, primary research should be conducted.

Consider an example research study regarding ideal teaching methods in elementary school contexts in a developing country in Asia. Just because there is abundant data on the same topic in elementary schools in Western countries does not preclude the possibility of novel theoretical developments in schools in Asia. This becomes particularly important if insights based on existing data from other contexts may not be applicable to the present context.

Note that this does not mean that a secondary research study is any less novel than a primary study. Indeed, many fields and methodologies rely extensively on analyzing existing data. For example, studies that employ discourse analysis and content analysis typically (though not always) rely on existing sources of data to facilitate understanding of language use in real-world situations.

As a result, the choice between primary and secondary research can be seen as more of a practical consideration than a matter of a study's potential contribution to scientific knowledge. Novelty in research is as much about the data collection as it is about the resulting analysis. If you require data for your study where none exists, then data from primary research is your best option.

Powerful data analysis at your fingertips with ATLAS.ti

Download a free trial to start making the most of your qualitative data.

Defining the Primary Outcomes and Justifying Secondary Outcomes of a Study: Usually, the Fewer, the Better

Affiliation.

• 1 From the *Department of Surgery and Perioperative Care, Dell Medical School, University of Texas at Austin, Austin, Texas; and †Departments of Quantitative Health Sciences and Outcomes Research, Cleveland Clinic, Cleveland, Ohio.
• PMID: 28682958
• DOI: 10.1213/ANE.0000000000002224

One of the first steps in designing and conducting a research study is identifying the primary and any secondary study outcomes. In an experimental, quasi-experimental, or analytic observational research study, the primary study outcomes arise from and align directly with the primary study aim or objective. Likewise, any secondary study outcomes arise from and directly align with any secondary study aim or objective. One designated primary study outcome then forms the basis for and is incorporated literally into the stated hypothesis. In a Methods section, authors clearly state and define each primary and any secondary study outcome variable. In the same Methods section, authors clearly describe how all primary and any secondary study outcome variables were measured. Enough detail is provided so that a clinician, statistician, or informatician can know exactly what is being measured and that other investigators could duplicate the measurements in their research venue. The authors provide published substantiation (preferably) or other documented evidence of the validity and reliability of any applied measurement instrument, tool, or scale. A common pitfall-and often fatal study design flaw-is the application of a newly created ("home-grown") or ad hoc modification of an existing measurement instrument, tool, or scale-without any supporting evidence of its validity and reliability. An optimal primary outcome is the one for which there is the most existing or plausible evidence of being associated with the exposure of interest or intervention. Including too many primary outcomes can (a) lead to an unfocused research question and study and (b) present problems with interpretation if the treatment effect differed across the outcomes. Inclusion of secondary variables in the study design and the resulting manuscript needs to be justified. Secondary outcomes are particularly helpful if they lend supporting evidence for the primary endpoint. A composite endpoint is an endpoint consisting of several outcome variables that are typically correlated with each. In designing a study, researchers limit components of a composite endpoint to variables on which the intervention of interest would most plausibly have an effect, and optimally with preliminary evidence of an effect. Ideally, components of a strong composite endpoint have similar treatment effect, frequency, and severity-with the most important being similar severity.

• Clinical Trials as Topic
• Outcome Assessment, Health Care / organization & administration*
• Reproducibility of Results
• Research Design*
• Statistics as Topic*
• Treatment Outcome

Secondary Research Guide: Definition, Methods, Examples

Apr 3, 2024

8 min. read

The internet has vastly expanded our access to information, allowing us to learn almost anything about everything. But not all market research is created equal , and this secondary research guide explains why.

There are two key ways to do research. One is to test your own ideas, make your own observations, and collect your own data to derive conclusions. The other is to use secondary research — where someone else has done most of the heavy lifting for you. 

Here’s an overview of secondary research and the value it brings to data-driven businesses.

Secondary Research Definition: What Is Secondary Research?

Primary vs Secondary Market Research

What Are Secondary Research Methods?

Advantages of secondary research, disadvantages of secondary research, best practices for secondary research, how to conduct secondary research with meltwater.

Secondary research definition: The process of collecting information from existing sources and data that have already been analyzed by others.

Secondary research (aka desk research ) provides a foundation to help you understand a topic, with the goal of building on existing knowledge. They often cover the same information as primary sources, but they add a layer of analysis and explanation to them.

Users can choose from several secondary research types and sources, including:

• Journal articles
• Research papers

With secondary sources, users can draw insights, detect trends , and validate findings to jumpstart their research efforts.

Primary vs. Secondary Market Research

We’ve touched a little on primary research , but it’s essential to understand exactly how primary and secondary research are unique.

Think of primary research as the “thing” itself, and secondary research as the analysis of the “thing,” like these primary and secondary research examples:

• An expert gives an interview (primary research) and a marketer uses that interview to write an article (secondary research).
• A company conducts a consumer satisfaction survey (primary research) and a business analyst uses the survey data to write a market trend report (secondary research).
• A marketing team launches a new advertising campaign across various platforms (primary research) and a marketing research firm, like Meltwater for market research , compiles the campaign performance data to benchmark against industry standards (secondary research).

In other words, primary sources make original contributions to a topic or issue, while secondary sources analyze, synthesize, or interpret primary sources.

Both are necessary when optimizing a business, gaining a competitive edge , improving marketing, or understanding consumer trends that may impact your business.

Secondary research methods focus on analyzing existing data rather than collecting primary data . Common examples of secondary research methods include:

• Literature review . Researchers analyze and synthesize existing literature (e.g., white papers, research papers, articles) to find knowledge gaps and build on current findings.
• Content analysis . Researchers review media sources and published content to find meaningful patterns and trends.
• AI-powered secondary research . Platforms like Meltwater for market research analyze vast amounts of complex data and use AI technologies like natural language processing and machine learning to turn data into contextual insights.

Researchers today have access to more market research tools and technology than ever before, allowing them to streamline their efforts and improve their findings.

Want to see how Meltwater can complement your secondary market research efforts? Simply fill out the form at the bottom of this post, and we'll be in touch.

Conducting secondary research offers benefits in every job function and use case, from marketing to the C-suite. Here are a few advantages you can expect.

Cost and time efficiency

Using existing research saves you time and money compared to conducting primary research. Secondary data is readily available and easily accessible via libraries, free publications, or the Internet. This is particularly advantageous when you face time constraints or when a project requires a large amount of data and research.

Access to large datasets

Secondary data gives you access to larger data sets and sample sizes compared to what primary methods may produce. Larger sample sizes can improve the statistical power of the study and add more credibility to your findings.

Ability to analyze trends and patterns

Using larger sample sizes, researchers have more opportunities to find and analyze trends and patterns. The more data that supports a trend or pattern, the more trustworthy the trend becomes and the more useful for making decisions. 

Historical context

Using a combination of older and recent data allows researchers to gain historical context about patterns and trends. Learning what’s happened before can help decision-makers gain a better current understanding and improve how they approach a problem or project.

Basis for further research

Ideally, you’ll use secondary research to further other efforts . Secondary sources help to identify knowledge gaps, highlight areas for improvement, or conduct deeper investigations.

Tip: Learn how to use Meltwater as a research tool and how Meltwater uses AI.

Secondary research comes with a few drawbacks, though these aren’t necessarily deal breakers when deciding to use secondary sources.

Reliability concerns

Researchers don’t always know where the data comes from or how it’s collected, which can lead to reliability concerns. They don’t control the initial process, nor do they always know the original purpose for collecting the data, both of which can lead to skewed results.

Potential bias

The original data collectors may have a specific agenda when doing their primary research, which may lead to biased findings. Evaluating the credibility and integrity of secondary data sources can prove difficult.

Outdated information

Secondary sources may contain outdated information, especially when dealing with rapidly evolving trends or fields. Using outdated information can lead to inaccurate conclusions and widen knowledge gaps.

Limitations in customization

Relying on secondary data means being at the mercy of what’s already published. It doesn’t consider your specific use cases, which limits you as to how you can customize and use the data.

A lack of relevance

Secondary research rarely holds all the answers you need, at least from a single source. You typically need multiple secondary sources to piece together a narrative, and even then you might not find the specific information you need.

To make secondary market research your new best friend, you’ll need to think critically about its strengths and find ways to overcome its weaknesses. Let’s review some best practices to use secondary research to its fullest potential.

Identify credible sources for secondary research

To overcome the challenges of bias, accuracy, and reliability, choose secondary sources that have a demonstrated history of excellence . For example, an article published in a medical journal naturally has more credibility than a blog post on a little-known website.

Assess credibility based on peer reviews, author expertise, sampling techniques, publication reputation, and data collection methodologies. Cross-reference the data with other sources to gain a general consensus of truth.

The more credibility “factors” a source has, the more confidently you can rely on it. 

Evaluate the quality and relevance of secondary data

You can gauge the quality of the data by asking simple questions:

• How complete is the data? 
• How old is the data? 
• Is this data relevant to my needs?
• Does the data come from a known, trustworthy source?

It’s best to focus on data that aligns with your research objectives. Knowing the questions you want to answer and the outcomes you want to achieve ahead of time helps you focus only on data that offers meaningful insights.

Document your sources 

If you’re sharing secondary data with others, it’s essential to document your sources to gain others’ trust. They don’t have the benefit of being “in the trenches” with you during your research, and sharing your sources can add credibility to your findings and gain instant buy-in.

Secondary market research offers an efficient, cost-effective way to learn more about a topic or trend, providing a comprehensive understanding of the customer journey . Compared to primary research, users can gain broader insights, analyze trends and patterns, and gain a solid foundation for further exploration by using secondary sources.

Meltwater for market research speeds up the time to value in using secondary research with AI-powered insights, enhancing your understanding of the customer journey. Using natural language processing, machine learning, and trusted data science processes, Meltwater helps you find relevant data and automatically surfaces insights to help you understand its significance. Our solution identifies hidden connections between data points you might not know to look for and spells out what the data means, allowing you to make better decisions based on accurate conclusions. Learn more about Meltwater's power as a secondary research solution when you request a demo by filling out the form below:

Continue Reading

How To Do Market Research: Definition, Types, Methods

What Are Consumer Insights? Meaning, Examples, Strategy

Market Intelligence 101: What It Is & How To Use It

The 13 Best Market Research Tools

Consumer Intelligence: Definition & Examples

9 Top Consumer Insights Tools & Companies

What Is Desk Research? Meaning, Methodology, Examples

An official website of the United States government

The .gov means it’s official. Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

The site is secure. The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

• Publications
• Account settings

Preview improvements coming to the PMC website in October 2024. Learn More or Try it out now .

• Advanced Search
• Journal List
• v.14(9); 2022 Sep

Research Question, Objectives, and Endpoints in Clinical and Oncological Research: A Comprehensive Review

Addanki purna singh.

1 Physiology, Saint James School of Medicine, The Quarter, AIA

Praveen R Shahapur

2 Microbiology, Bijapur Lingayat District Educational Association (BLDE, Deemed to be University) Shri B.M. Patil Medical College, Vijayapur, IND

Sabitha Vadakedath

3 Biochemistry, Prathima Institute of Medical Sciences, Karimnagar, IND

Vallab Ganesh Bharadwaj

4 Microbiology, Trichy Sri Ramasamy Memorial (SRM) Medical College Hospital & Research Centre, Tiruchirapalli, IND

Dr Pranay Kumar

5 Anatomy, Prathima Institute of Medical Sciences, Karimnagar , IND

Venkata BharatKumar Pinnelli

6 Biochemistry, Vydehi Institute of Medical Science & Research Center, Bangalore, IND

Vikram Godishala

7 Biotechnology, Ganapathy Degree College, Parkal, IND

Venkataramana Kandi

8 Clinical Microbiology, Prathima Institute of Medical Sciences, Karimnagar, IND

Clinical research is a systematic process of conducting research work to find solutions for human health-related problems. It is applied to understand the disease process and assist in the diagnosis, treatment, and prevention. Currently, we are experiencing global unrest caused by the coronavirus disease (COVID-19) pandemic. The novel severe acute respiratory syndrome coronavirus (SARS-CoV-2) has been responsible for the deaths of more than 50 million people worldwide. Also, it has resulted in severe morbidity among the affected population. The cause of such a huge amount of influence on human health by the pandemic was the unavailability of drugs and therapeutic interventions to treat and manage the disease. Cancer is a disease condition wherein the normal cell function is deranged, and the cells multiply in an uncontrolled manner. Based on recent reports by the World Health Organization (WHO), cancer is the second leading cause of death globally. Moreover, the rates of cancers have shown an increasing trend in the past decade. Therefore, it is essential to improve the understanding concerning clinical research to address the health concerns of humans. In this review, we comprehensively discuss critical aspects of clinical research that include the research question, research objectives, patient-reported outcome measures (PROMs), intention-to-treat and per-protocol analysis, and endpoints in clinical and oncological research.

Introduction and background

Successful clinical research can be conducted by well-trained researchers. Other essential factors of clinical research include framing a research question and relevant objectives, documenting, and recording research outcomes, and outcome measures, sample size, and research methodology including the type of randomization, among others [ 1 , 2 ].

Clinicians/physicians and surgeons are increasingly dependent on the clinical research results for improved management of patients. Therefore, researchers need to work upon a relevant research question/hypothesis and specific objectives that may potentially deliver results that can be translated into practice in the form of evidence-based medicine [ 3 ].

Essential elements that facilitate a researcher to frame a research question are in-depth knowledge of the subject and identifying possible gaps. Moreover, the feasible, interesting, novel, ethical, relevant (FINER) approach and the population of interest/target group, intervention, comparison group, outcome of interest, and time of study (PICOT) approach were previously suggested for researchers to be able to frame appropriate research questions [ 4 ].

Moreover, the research objectives should be framed by the researcher before the initiation of the study: a specific, measurable, achievable, realistic, and time-defined (SMART) approach is utilized to devise the objectives based on the research question. It is preferable to have a single primary objective whereas the secondary objectives can be multiple and may be dependent on the amount of data collected. The objectives must be simple and specific and must reflect the research question [ 5 , 6 ].

Given the evolution and the increasing requirement for emergency care, clinical researchers are advised to adopt a population, exposure, comparator, outcome (PECO)/ population, exposure, comparator, outcome (PICO) approach to construct the study objectives and carry out quantitative research. In contrast, qualitative research which is carried out to understand, explore, and examine requires the researcher to understand what and why the research is undertaken along with the roles of the researcher, research process/steps, and participants [ 7 , 8 ].

Since clinical research is envisaged in finding a solution to a health problem, choosing the appropriate endpoint requires special focus. The endpoints are the specific measures of the outcomes of an intervention and therefore they must be chosen judiciously [ 9 ]. The endpoints in a clinical trial can be single or multiple in numbers. The primary endpoints assess the major research question, and the secondary endpoints may assess alternative research questions. Moreover, there are other endpoints like surrogate endpoints, intermediate clinical endpoints, clinical outcomes, clinical outcome assessments, clinician-reported outcomes, observer-reported outcomes, patient-reported outcomes, and performance outcomes [ 10 ] (Figure ​ (Figure1 1 ).

This figure has been created by the authors

The clinical trial endpoints are essentially the indicators of the power of the interventions either to cure or control the disease progression. Due to the cost and the tedious nature of the clinical trials, it is suggested that multiple-arm trials that include more than one primary endpoint be used [ 11 ]. Integration of the primary endpoints with the patient prioritized endpoints was recently suggested especially among cardiovascular disease patients [ 12 ]. Cancer research is an increasingly evolving area because of the unavailability of therapeutic interventions for some malignancies like breast and lung cancer, among others [ 13 , 14 ]. Moreover, the drugs available for treating cancers are plagued by adverse reactions. However, since most cancer clinical trials apply overall survival as the preferred and gold standard clinical endpoint, it is difficult for the trial operators to sustain the costs associated with the long lengths of the study that follow-up patients for years to assess the clinical outcomes after interventions. In this study, we comprehensively review the essential elements of clinical research that include the research question, hypothesis, and clinical and oncological endpoints.

Research question

A research question can alternatively be called the aim of the researcher. It describes the problem that the researcher intends to solve vis-à-vis finding an answer to a question. A research question is the first step toward any kind of research process that includes both qualitative as well as quantitative research. Since the research question predicts the core of any project, it must be carefully framed. The essential elements to consider while determining a research question are feasibility, preciseness, and relevance to the real world.

A person interested in a broad subject area must first complete extensive reading of the available literature. This enables the researcher to find out the strengths, loopholes, deficiencies, and missing links that can form the basis for framing a research question. The problem to which a solution needs to be found and the potential causes/reasons for the problems help a researcher frame the research question.

The research questions should be framed in such a way that the researcher will find several possibilities as solutions to the research question rather than a simple yes or no. Among the various factors that determine the power of a research question, the most essential ones include the ability of research questions to find complex answers, focussed, and the specific nature of the question. The research questions must be answerable, debatable, and researchable [ 15 , 16 ]. Research questions differ from the type of research method selected by the researcher as shown in Figure ​ Figure2 2 .

FINER: Feasible, interesting, novel, ethical, relevant; PICOT: Population of interest/target group, intervention, comparison group, outcome of interest, time of study; PECO: Population, exposure, comparator, outcome; PICO: Population, intervention, comparator, outcome; SMART: Specific, measurable, achievable, realistic, and time defined; COVID-19: Coronavirus disease-19

Hypothesis testing

A hypothesis is an assumption by the researcher that the answers drawn with reference to the research question are either true or false. The researcher performs hypothesis testing by using appropriate statistical methods on the data collected from the research.

The hypothesis is an assumption/observation of the researcher regarding the outcome of potential research that is being conducted. There are two types of hypotheses, the null hypothesis (H0), wherein, the researcher assumes that there is no relation/causality/effect. The alternate hypothesis (HA) is when the researcher believes/assumes that there is a relationship/effect [ 17 ]. Basically, there are two types of errors while testing a hypothesis. Type I error (α) (false positive) is when the researcher rejects the null hypothesis although it is true. Type II error (β) (false negative) is when the researcher accepts the null hypothesis although it is false.

The errors in hypothesis testing occur because of bias among many other reasons in the study. Many studies set the power of the studies to essentially rule out errors. Researchers consider 5% chance (α=0.05; range: 0.01-0.10) of error in case of a type I error and up to 20% chance (β =0.20; range: 0.05-0.20) in case of type II errors [ 18 ]. The characteristics of a good hypothesis are simple and specific. Moreover, it must be decided by the researcher prior to initiating the study and while writing the study proposal/protocol [ 18 ]. 

Hypothesis testing means sample testing, wherein the information gathered after sample testing is inferred after applying statistical methods. A hypothesis may be generated in several ways that include observations, anatomical features, and other physiological facts observed by physicians [ 19 ]. Hypothesis testing also can be performed by using appropriate statistical methods. The testing of the hypothesis is done to prove the null hypothesis or otherwise use the sample data.

As a researcher, one must assume a null hypothesis, or believe that the alternate hypothesis holds good in the sample selected. After the collection of data, analysis, and interpretation, the researcher either accepts or rejects the hypothesis. Therefore, it must be noted that while a study is initiated, there is only a 50% chance of either the null hypothesis or the alternative hypothesis coming true [ 20 ].

The step-by-step process of hypothesis testing starts with an assumption, criteria for interpretation of results, analysis, and conclusion. The level of significance (95% free of type I error and 80% free of type II error) also is decided initially to ensure that the study results are replicated by the other researchers [ 21 ].

Objectives in clinical research

The most significant objective in clinical research is to find out whether the intervention attempted was successful in curing the disease/medical condition. It is important to understand the fact that research planning greatly influences the research results, and no statistical method can improve the results but a well-designed and conducted clinical research [ 22 ].

The primary objective of clinical research studies includes improvement in patient management. Most clinical research studies are aimed at discovering a new/novel drug to treat a medical condition that presently has no specific treatment, or the available drugs are not particularly effective in curing the disease.

The objectives are formed to address the five 'W's, namely who (children, women, etc.); what (the medical condition/disease/infection); why (causes of the medical condition/disease/infection); when (conditions responsible for the medical condition/disease/infection); and where (geographical aspects of the medical condition/disease/infection) as shown in Figure ​ Figure3 3 .

Clinical research can be of several types including primary research and secondary research. Also, the research can be observational (no intervention) and experimental/interventional. Clearly demarcated/framed research objectives are essential to improve the clarity, specificity, and focus of the clinical trial [ 23 ].

Patient-reported outcome measures (PROMs)

While conducting clinical research the investigators collect trial data through clinical observations, laboratory monitoring, and caregiver feedback. There are some aspects of the data like the patient-reported outcomes (PROs) that can be reported by the subject/patient him/herself either in the form of a questionnaire or through interviews. Such data collected from the patients in their words is termed PROMs. The PROMs include a global impression of the trial, the functional status and well-being, symptoms, health-related quality of life (HRQL), treatment compliance, and satisfaction. 

The questionnaire used to collect the PROs is called a PRO instrument. The data collected through this instrument is used to establish the benefit-to-risk ratio of the clinical trial drug. The PRO instruments can be designed as generic (contains a wide variety of health-related aspects and therefore can be used among different patient types), disease-specific (rheumatoid arthritis, psoriasis, etc.), dimension-specific (physical activity, cognitive levels, etc.), region/site-specific, individualized, utility measures, and summary items [ 24 ]. The patient-reported experience measure (PREMs), and the patient and public involvement programs are used to collect the patient’s feedback that invariably helps in improving the quality of healthcare facilities [ 25 ].

It is important to develop PROM tools/instruments for various diseases, especially among children as noted by a recent research report. This study suggested that a suitable PROM instrument is required to measure the status of disease (wheezing/asthma/respiratory diseases) and its control among preschool children [ 26 ].

Intention to treat and per-protocol analysis

The intention to treat (ITT) analysis is used when all the study subject’s data is analyzed including all those participants who were enrolled in the study, and those who have deviated (not signed the informed consent, discontinued from the study, not taken the trial drug as suggested). The ITT studies minimize the bias and ensure both the study and the control groups are compared.

The per-protocol (PP) analysis usually includes the data from only those subjects who have remained till the study period ended, have taken the drugs as suggested by the protocol, and was available for regular follow-up. The disadvantages of PP are potential disturbances in the balance between the study groups (randomized/placebo/control), a lower number of study participants due to the exclusion of dropouts, and non-compliant subjects. Therefore, the results from the PPA studies could be biased [ 27 ].

The randomized clinical trial (RCT) studies of superiority type use ITT analyses as against the non-inferiority and equivalence studies wherein an ITT approach may favor the study hypothesis.

According to the Committee for Proprietary Medicinal Products (CPMP), and the Consolidated Standards for reporting trials (CONSORT), both the ITT and PP analyses must be assessed to effectively interpret the results of the clinical trial including the safety and efficacy [ 28 ]. 

Endpoints in clinical research

The endpoints in clinical research determine whether the clinical trial has been successful in finding out if an intervention/drug has proven beneficial in improving the survival and quality of life of the patient. The endpoints determine the validity of the clinical trial results. There are different types of endpoints like primary endpoints, secondary endpoints, tertiary endpoints, surrogate endpoints (laboratory measurements, physical signs), and others [ 9 ].

The clinical trial endpoints could be subjective and objective in nature. The objective endpoints are survival, disease progression/remission, and the development of disease/condition. The subjective endpoints include symptoms, quality of life, and other patient-reported outcomes [ 29 , 30 ]. The significance of endpoints in clinical trials and the importance of choosing appropriate endpoints were previously reported. This study suggested that the primary endpoints help in deriving the sample size and confirm the generalizability of the results. The secondary and surrogate endpoints could be used while conducting the clinical research without ignoring the aspects of the quality of life of the subjects [ 31 ]. 

Endpoints in oncology clinical trials

Cancer clinical trials assume increased significance because the drugs developed against the cancer are intended to increase the survival of the patients. Also, anti-cancer agents are associated with several side effects. Therefore, oncology trials include several endpoints, the primary being the overall survival, and the secondary endpoints include the assessment of other outcomes that indicate the quality of life (QoL), tumor-related endpoints, and others. The disadvantages of oncology clinical trials are the cost associated with the recruitment of a greater number of subjects and long-time follow-up of the patients [ 32 ].

Although primary endpoints are considered as most significant in oncological trials, a recent report stressed the importance of surrogate markers in assessing the efficacy of anti-cancer drugs [ 33 ]. 

The endpoints in oncology clinical trials, their applications, functions, and drawbacks are summarized in Table ​ Table1 1 .

Survival endpoints in oncology clinical trials

The survival endpoint considers the time from randomization to death. This type of follow-up (daily), although difficult to do, will remove bias associated with the investigator’s interpretation. The survival studies require large sample sizes and cross-over therapies act as confounding factors for survival. The survival studies consider patient benefit over drug toxicity. 

Apart from the overall survival, oncology clinical trials use alternative ways to assess the efficacy of the drugs by using other endpoints like progression-free survival [ 34 ]. Other endpoints suggested are biomarkers, disease-free survival, objective response rate, time to progression, complete response, partial response, minor response, time to treatment failure, time to next treatment, duration of clinical benefit, objective response rate, complete response, pathological complete response, disease control rate, clinical benefit rate, milestone survival, event-free survival, and QoL [ 35 ].

Endpoints in immunological diseases and infections

Autoimmune diseases are usually chronic conditions that arise due to the immunologic responses against the self. They are usually associated with hyper-reactivity of immune cells towards the host's own cells/tissue and can cause significant morbidity and mortality among affected people.

Autoimmune diseases are generally genetic in origin, but many such diseases are attributed to other factors such as infection, food, drugs, and other substances. Frequently occurring autoimmune diseases are rheumatoid arthritis, psoriasis, systemic lupus erythematosus (SLE), ulcerative colitis, Crohn’s disease, and multiple sclerosis, among others [ 36 ].

Clinical trials with respect to the development of drugs/medicine to treat autoimmune diseases take into consideration the therapeutic efficacy of the trial drug, no risk, and only benefit to the patients. Therefore, the selection of endpoints for clinical trials in immune diseases must consider all these factors to effectively assess the pharmacological value of the trial drugs. The endpoints for autoimmune hepatitis include remission, incomplete response, treatment failure, and drug toxicity [ 37 ].

The endpoint related to the infections includes the direct measurement of the number of microorganisms. Other endpoints include the measurement of physiological aspects impaired by the infecting microbe and the measurement of immune responses against the infectious agent. The endpoints of human immunodeficiency virus (HIV) infection includes HIV-ribonucleic acid (RNA) viral load, maintenance, improvement, and decline of the cluster of differentiation 4 (CD4)+ T lymphocyte cell counts, and others.

Composite endpoints

Clinical trial for drugs is assessed based on several endpoints that establish efficacy and allow regulatory authorities to decide on approving the drugs for human use. In most instances, the clinical trials apply primary endpoints whereas in recent times the surge in candidate drugs and the necessity for life-saving drugs had ushered in the use of alternative endpoints like the composite endpoints. The composite endpoints are instrumental in reducing the trial costs, minimizing the long follow-ups, and lower subject recruitments. Since the composite endpoints combine more than one outcome during the drug trial, it enables the investigators to understand the efficacy of the drug in a short period of time [ 38 ].

While using multiple endpoints, it is important to understand that each one is as important as the other and the statistical methods must be used to confirm the overall efficacy of the trial drug. The drawbacks of applying composite endpoints in clinical trials are the complexity of the methods, low transparency, including invalid indicators, and the possibility of misleading results and conclusions [ 39 ].

Because there is no specific recommendation as to how the composite endpoints need to be selected, evaluated, and analyzed, there exists a possibility of bias. In a recent report, an index to evaluate the bias attributable to composite outcomes (BACO) was applied and suggested. The BACO index <0, 0 to <1, and >1 indicated that the composite endpoints were inverted, underestimated, and overestimated, respectively. A BACO index of 1 indicates that the composite endpoint usage resulted in unbiased results [ 40 ]. The composite endpoint in a clinical trial means the use of multiple endpoints. For the drug trials for migraine, the composite endpoints include no pain for two hours, nausea after two hours, and photosensitivity after two hours. The clinical trials with vaccines could have more than 20 endpoints. Other trials with multiple endpoints include rheumatoid arthritis (4), acne (4), sleep disorders (6), primary biliary cirrhosis (4), and glaucoma (9).

Conclusions

The clinical research design must be carefully accomplished keeping in mind the financial and time constraints. The trial must be initiated to address a specific research question, which essentially requires the research group to carry out an extensive literature search and identify the knowledge gaps. The research hypothesis needs to be carefully designed to avoid errors. The researchers should ensure the inclusion of appropriate objectives that guarantee quality outcomes and clinical benefits. It is essential to include the PROMs along with the clinician, researcher, and observer reported outcomes to assess the benefit-to-risk ratio of the investigational drug and improve the quality of healthcare facilities, among others. The safety and efficacy of a clinical trial drug should be carefully interpreted based on the results obtained from the ITT and PP analyses. Moreover, a clinical trial should incorporate specific and relevant endpoints that ensure the efficacy or otherwise of the intervention. Cancer clinical trials are even more complex because the interventions could potentially be life-saving and therefore, the selection of endpoints becomes critical as discussed briefly in this review.

The content published in Cureus is the result of clinical experience and/or research by independent individuals or organizations. Cureus is not responsible for the scientific accuracy or reliability of data or conclusions published herein. All content published within Cureus is intended only for educational, research and reference purposes. Additionally, articles published within Cureus should not be deemed a suitable substitute for the advice of a qualified health care professional. Do not disregard or avoid professional medical advice due to content published within Cureus.

The authors have declared that no competing interests exist.

Want to create or adapt books like this? Learn more about how Pressbooks supports open publishing practices.

55 Primary and Secondary Research

Learning Objectives

After reading this chapter, the student will be able to:

• Explain the difference between primary and secondary sources.
• Distinguish between reliable and unreliable information on the Internet.
• Access and find reliable information on the Internet.
• Explain basic terminology needed for Internet research.
• Construct a short survey usable for analyzing an audience.
• Conduct short interviews for information for speeches.
• Recognize information that should be cited.

Primary and Secondary Research

As noted in Chapters 1 and 3, credibility as a speaker is one of your main concerns. Among many voices, you must prove that yours is worth attention. You can do this by

• using engaging narratives,
• having energetic delivery, and
• meeting the needs of your audience.

However, a foundational way is to offer support for the points you make in your speech, which you can do by providing evidence from other sources. You will find these resources by doing research.

You have access to many sources of information: books in print or electronic format, Internet webpages, scholarly journal articles in databases, and information from direct, primary sources through surveys and interviews. With so many sources, information literacy is a vital skill for researchers.

Information Literacy

the ability to recognize when information is needed and have the ability to locate, evaluate, and effectively use the needed information

(American Library Association, 1989)

The term “research” is a broad one, for which the Merriam-Webster Dictionary (2018) offers three basic definitions. The second one it lists is:

studious inquiry or examination; especially: investigation or experimentation aimed at the discovery and interpretation of facts, revision of accepted theories or laws in the light of new facts, or practical application of such new or revised theories or laws.

The third definition in Merriam-Webster is more applicable for this chapter: “the collecting of information about a particular subject.”

The first definition above refers to primary research , which depends on primary sources . The term “primary source” means “those sources that provide first-hand accounts of the events, practices or conditions” being researched (“What are Primary Sources,” 2006). It is research that goes directly to the source (Futteross, 2018).

Primary Research

new research, carried out to acquire data first-hand rather from previously published sources to answer specific questions or issues and discover knowledge

Primary Sources

information that is first-hand or straight from the source; information that is unfiltered by interpretation or editing

For example, if a psychology researcher wanted to understand the stressors on military personnel in Afghanistan, he or she could interview them personally, read blog posts or other writings of the service personnel, or give them a survey with clear questions about their experiences and concerns. The information gathered in each of these examples would come straight from the “source.”

Another example would be an education professor who wants to understand if texting in class affects student learning. She sets up an experiment with similar students in two classes taught exactly the same way. One class has to follow a strict policy of no texting and the other has no policy about texting. At the end of the semester she compares test scores. Going into the experiment, she formulates a hypothesis, or prediction, of which class will do better on tests. Her results will support the hypothesis or not.

Journalists, historians, biologists, chemists, psychologists, sociologists, and others conduct primary research, which is part of achieving a doctorate in one’s field and adding to what is called “the knowledge base.” For your speeches, you might use primary sources as well. Let’s say you want to do a persuasive speech to convince your classmates to wear their seatbelts. Some of the basic information you might need to do this is:

• how many people in the class don’t wear seatbelts regularly, and
• why they choose not to.

You could conduct primary research and directly ask your classmates if they wear their seatbelts and, if not, why not. This way, you are getting information directly from a primary source. (Later in this chapter we will look at some ways you could do this efficiently.)

It is possible that you will access published primary sources in your research for this speech class (and you will definitely do so as you progress in your discipline). Additionally, and more commonly, you will use secondary sources , which are articles, books, and websites that are compilations or interpretations of the primary sources. It may sound from this description that secondary sources are inferior to primary sources. That is not the case. Poorly done primary research is not better than quality secondary sources. Which one you use depends on our purpose, topic, audience, and context. If you engage in undergraduate research in your junior or senior year and present at a conference, you will be expected have some primary research. However, for most of your college work, you will be looking for reliable secondary sources.

Secondary Sources

information that is not directly from the first-hand source; information that has been compiled, filtered, edited, or interpreted in some way

One way to assess the quality of a secondary source is to look at its references or bibliography. A reliable source will cite other sources to support its claims. Likewise, a well-researched speech will provide support for its argument by using evidence obtained from reliable sources. In this section we will examine research on the Internet and how you can conduct your own primary research. The last section will show how to use the Roberts Library resources at Dalton State College. If you are not a student at Dalton State, your college’s library is probably very similar to Dalton State’s, so you should read that section for information on different types of sources, etc. Your instructor will probably provide instruction to your college’s library system.

Exploring Communication in the Real World Copyright © 2020 by Chris Miller is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License , except where otherwise noted.

Share This Book

Have a language expert improve your writing

Run a free plagiarism check in 10 minutes, generate accurate citations for free.

• Knowledge Base

Methodology

Research Methods | Definitions, Types, Examples

Research methods are specific procedures for collecting and analyzing data. Developing your research methods is an integral part of your research design . When planning your methods, there are two key decisions you will make.

First, decide how you will collect data . Your methods depend on what type of data you need to answer your research question :

• Qualitative vs. quantitative : Will your data take the form of words or numbers?
• Primary vs. secondary : Will you collect original data yourself, or will you use data that has already been collected by someone else?
• Descriptive vs. experimental : Will you take measurements of something as it is, or will you perform an experiment?

Second, decide how you will analyze the data .

• For quantitative data, you can use statistical analysis methods to test relationships between variables.
• For qualitative data, you can use methods such as thematic analysis to interpret patterns and meanings in the data.

Table of contents

Methods for collecting data, examples of data collection methods, methods for analyzing data, examples of data analysis methods, other interesting articles, frequently asked questions about research methods.

Data is the information that you collect for the purposes of answering your research question . The type of data you need depends on the aims of your research.

Qualitative vs. quantitative data

Your choice of qualitative or quantitative data collection depends on the type of knowledge you want to develop.

For questions about ideas, experiences and meanings, or to study something that can’t be described numerically, collect qualitative data .

If you want to develop a more mechanistic understanding of a topic, or your research involves hypothesis testing , collect quantitative data .

You can also take a mixed methods approach , where you use both qualitative and quantitative research methods.

Primary vs. secondary research

Primary research is any original data that you collect yourself for the purposes of answering your research question (e.g. through surveys , observations and experiments ). Secondary research is data that has already been collected by other researchers (e.g. in a government census or previous scientific studies).

If you are exploring a novel research question, you’ll probably need to collect primary data . But if you want to synthesize existing knowledge, analyze historical trends, or identify patterns on a large scale, secondary data might be a better choice.

Descriptive vs. experimental data

In descriptive research , you collect data about your study subject without intervening. The validity of your research will depend on your sampling method .

In experimental research , you systematically intervene in a process and measure the outcome. The validity of your research will depend on your experimental design .

To conduct an experiment, you need to be able to vary your independent variable , precisely measure your dependent variable, and control for confounding variables . If it’s practically and ethically possible, this method is the best choice for answering questions about cause and effect.

Receive feedback on language, structure, and formatting

Professional editors proofread and edit your paper by focusing on:

• Academic style
• Vague sentences
• Style consistency

See an example

Your data analysis methods will depend on the type of data you collect and how you prepare it for analysis.

Data can often be analyzed both quantitatively and qualitatively. For example, survey responses could be analyzed qualitatively by studying the meanings of responses or quantitatively by studying the frequencies of responses.

Qualitative analysis methods

Qualitative analysis is used to understand words, ideas, and experiences. You can use it to interpret data that was collected:

• From open-ended surveys and interviews , literature reviews , case studies , ethnographies , and other sources that use text rather than numbers.
• Using non-probability sampling methods .

Qualitative analysis tends to be quite flexible and relies on the researcher’s judgement, so you have to reflect carefully on your choices and assumptions and be careful to avoid research bias .

Quantitative analysis methods

Quantitative analysis uses numbers and statistics to understand frequencies, averages and correlations (in descriptive studies) or cause-and-effect relationships (in experiments).

You can use quantitative analysis to interpret data that was collected either:

• During an experiment .
• Using probability sampling methods .

Because the data is collected and analyzed in a statistically valid way, the results of quantitative analysis can be easily standardized and shared among researchers.

Here's why students love Scribbr's proofreading services

Discover proofreading & editing

If you want to know more about statistics , methodology , or research bias , make sure to check out some of our other articles with explanations and examples.

• Chi square test of independence
• Statistical power
• Descriptive statistics
• Degrees of freedom
• Pearson correlation
• Null hypothesis
• Double-blind study
• Case-control study
• Research ethics
• Data collection
• Hypothesis testing
• Structured interviews

Research bias

• Hawthorne effect
• Unconscious bias
• Recall bias
• Halo effect
• Self-serving bias
• Information bias

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

Quantitative methods allow you to systematically measure variables and test hypotheses . Qualitative methods allow you to explore concepts and experiences in more detail.

In mixed methods research , you use both qualitative and quantitative data collection and analysis methods to answer your research question .

A sample is a subset of individuals from a larger population . Sampling means selecting the group that you will actually collect data from in your research. For example, if you are researching the opinions of students in your university, you could survey a sample of 100 students.

In statistics, sampling allows you to test a hypothesis about the characteristics of a population.

The research methods you use depend on the type of data you need to answer your research question .

• If you want to measure something or test a hypothesis , use quantitative methods . If you want to explore ideas, thoughts and meanings, use qualitative methods .
• If you want to analyze a large amount of readily-available data, use secondary data. If you want data specific to your purposes with control over how it is generated, collect primary data.
• If you want to establish cause-and-effect relationships between variables , use experimental methods. If you want to understand the characteristics of a research subject, use descriptive methods.

Methodology refers to the overarching strategy and rationale of your research project . It involves studying the methods used in your field and the theories or principles behind them, in order to develop an approach that matches your objectives.

Methods are the specific tools and procedures you use to collect and analyze data (for example, experiments, surveys , and statistical tests ).

In shorter scientific papers, where the aim is to report the findings of a specific study, you might simply describe what you did in a methods section .

In a longer or more complex research project, such as a thesis or dissertation , you will probably include a methodology section , where you explain your approach to answering the research questions and cite relevant sources to support your choice of methods.

Is this article helpful?

Other students also liked, writing strong research questions | criteria & examples.

• What Is a Research Design | Types, Guide & Examples
• Data Collection | Definition, Methods & Examples

More interesting articles

• Between-Subjects Design | Examples, Pros, & Cons
• Cluster Sampling | A Simple Step-by-Step Guide with Examples
• Confounding Variables | Definition, Examples & Controls
• Construct Validity | Definition, Types, & Examples
• Content Analysis | Guide, Methods & Examples
• Control Groups and Treatment Groups | Uses & Examples
• Control Variables | What Are They & Why Do They Matter?
• Correlation vs. Causation | Difference, Designs & Examples
• Correlational Research | When & How to Use
• Critical Discourse Analysis | Definition, Guide & Examples
• Cross-Sectional Study | Definition, Uses & Examples
• Descriptive Research | Definition, Types, Methods & Examples
• Ethical Considerations in Research | Types & Examples
• Explanatory and Response Variables | Definitions & Examples
• Explanatory Research | Definition, Guide, & Examples
• Exploratory Research | Definition, Guide, & Examples
• External Validity | Definition, Types, Threats & Examples
• Extraneous Variables | Examples, Types & Controls
• Guide to Experimental Design | Overview, Steps, & Examples
• How Do You Incorporate an Interview into a Dissertation? | Tips
• How to Do Thematic Analysis | Step-by-Step Guide & Examples
• How to Write a Literature Review | Guide, Examples, & Templates
• How to Write a Strong Hypothesis | Steps & Examples
• Inclusion and Exclusion Criteria | Examples & Definition
• Independent vs. Dependent Variables | Definition & Examples
• Inductive Reasoning | Types, Examples, Explanation
• Inductive vs. Deductive Research Approach | Steps & Examples
• Internal Validity in Research | Definition, Threats, & Examples
• Internal vs. External Validity | Understanding Differences & Threats
• Longitudinal Study | Definition, Approaches & Examples
• Mediator vs. Moderator Variables | Differences & Examples
• Mixed Methods Research | Definition, Guide & Examples
• Multistage Sampling | Introductory Guide & Examples
• Naturalistic Observation | Definition, Guide & Examples
• Operationalization | A Guide with Examples, Pros & Cons
• Population vs. Sample | Definitions, Differences & Examples
• Primary Research | Definition, Types, & Examples
• Qualitative vs. Quantitative Research | Differences, Examples & Methods
• Quasi-Experimental Design | Definition, Types & Examples
• Questionnaire Design | Methods, Question Types & Examples
• Random Assignment in Experiments | Introduction & Examples
• Random vs. Systematic Error | Definition & Examples
• Reliability vs. Validity in Research | Difference, Types and Examples
• Reproducibility vs Replicability | Difference & Examples
• Reproducibility vs. Replicability | Difference & Examples
• Sampling Methods | Types, Techniques & Examples
• Semi-Structured Interview | Definition, Guide & Examples
• Simple Random Sampling | Definition, Steps & Examples
• Single, Double, & Triple Blind Study | Definition & Examples
• Stratified Sampling | Definition, Guide & Examples
• Structured Interview | Definition, Guide & Examples
• Survey Research | Definition, Examples & Methods
• Systematic Review | Definition, Example, & Guide
• Systematic Sampling | A Step-by-Step Guide with Examples
• Textual Analysis | Guide, 3 Approaches & Examples
• The 4 Types of Reliability in Research | Definitions & Examples
• The 4 Types of Validity in Research | Definitions & Examples
• Transcribing an Interview | 5 Steps & Transcription Software
• Triangulation in Research | Guide, Types, Examples
• Types of Interviews in Research | Guide & Examples
• Types of Research Designs Compared | Guide & Examples
• Types of Variables in Research & Statistics | Examples
• Unstructured Interview | Definition, Guide & Examples
• What Is a Case Study? | Definition, Examples & Methods
• What Is a Case-Control Study? | Definition & Examples
• What Is a Cohort Study? | Definition & Examples
• What Is a Conceptual Framework? | Tips & Examples
• What Is a Controlled Experiment? | Definitions & Examples
• What Is a Double-Barreled Question?
• What Is a Focus Group? | Step-by-Step Guide & Examples
• What Is a Likert Scale? | Guide & Examples
• What Is a Prospective Cohort Study? | Definition & Examples
• What Is a Retrospective Cohort Study? | Definition & Examples
• What Is Action Research? | Definition & Examples
• What Is an Observational Study? | Guide & Examples
• What Is Concurrent Validity? | Definition & Examples
• What Is Content Validity? | Definition & Examples
• What Is Convenience Sampling? | Definition & Examples
• What Is Convergent Validity? | Definition & Examples
• What Is Criterion Validity? | Definition & Examples
• What Is Data Cleansing? | Definition, Guide & Examples
• What Is Deductive Reasoning? | Explanation & Examples
• What Is Discriminant Validity? | Definition & Example
• What Is Ecological Validity? | Definition & Examples
• What Is Ethnography? | Definition, Guide & Examples
• What Is Face Validity? | Guide, Definition & Examples
• What Is Non-Probability Sampling? | Types & Examples
• What Is Participant Observation? | Definition & Examples
• What Is Peer Review? | Types & Examples
• What Is Predictive Validity? | Examples & Definition
• What Is Probability Sampling? | Types & Examples
• What Is Purposive Sampling? | Definition & Examples
• What Is Qualitative Observation? | Definition & Examples
• What Is Qualitative Research? | Methods & Examples
• What Is Quantitative Observation? | Definition & Examples
• What Is Quantitative Research? | Definition, Uses & Methods

Unlimited Academic AI-Proofreading

✔ Document error-free in 5minutes ✔ Unlimited document corrections ✔ Specialized in correcting academic texts

• Open access
• Published: 11 April 2024

Can family doctor system improve health service utilization for patients with hypertension and diabetes in China? A difference-in-differences study

• Luying Zhang 1   na1 ,
• Peng Zhang 2   na1 &
• Wen Chen 1  

BMC Health Services Research volume  24 , Article number:  454 ( 2024 ) Cite this article

Metrics details

Family doctors, serving as gatekeepers, are the core of primary health care to meet basic health needs, provide accessible care, and improve attainable health. The study objective was to evaluate the impact of the family doctor system on health service utilization among patients with hypertension and diabetes in China.

Difference-in-Differences (DID) models are constructed to estimate the net effect of the family doctor system, based on the official health management records and medical insurance claim data of patients with hypertension and diabetes in an eastern city of China.

The family doctor system significantly increases follow-up visits (hypertension patients coef. = 0.13, diabetes patients coef. = 0.08, both p  < 0.001) and outpatient visits (hypertension patients coef. = 0.08, diabetes patients coef. = 0.05, both p  < 0.001) among the contracted compared to the non-contracted. The proportion of outpatient visits in community health centers among the contracted significantly rose (hypertension patients coef. = 0.02, diabetes patients coef. = 0.04, both p  < 0.001) due to significantly more outpatient visits in community health centers and fewer in secondary and tertiary hospitals. It also significantly mitigates the increase in inpatient admissions among hypertension patients but not among diabetes patients.

Conclusions

The examined family doctor system strengthens primary care, both by increasing follow-up visits and outpatient visits and promoting a rationalized structure of outpatient utilization in China.

Peer Review reports

Primary health care (PHC) addresses meeting the basic health needs of individuals throughout their lives by providing people-centered care in the community, to guarantee the right to the most accessible health care, the utmost equity, and the highest level of attainable health [ 1 ]. As the core of primary care, gatekeepers, globally known as family physicians (FP), or general practitioners (GP), and in China family doctors (FD), provide standardized services including preventive and basic medical services to advocate a healthy lifestyle, manage common diseases, and treat patients in primary care setting [ 2 , 3 , 4 ].

The effectiveness of gatekeepers on health service utilization has been proven that having gatekeepers promotes more preventive and outpatient service utilization, and decreases unnecessary emergency visits or avoidable inpatient utilization [ 5 , 6 , 7 , 8 , 9 , 10 , 11 , 12 ]. In the United States, the pilot practices found that GP collaborative practice in 27 sites has statistically significantly higher rates of diabetes care, breast cancer screening, and ambulatory primary care visits; lower rates of all-cause hospitalization, emergency department visits, and ambulatory visits to specialists [ 5 ]. In Australia, GP care utilization is associated with reduced risk for any emergency department presentations [ 7 ]; and using GP services lowers the rate of potentially preventable hospitalizations in people with diabetes [ 6 ]. In Portuguese, a study shows that having an assigned GP increases the appropriate use of emergency departments by 1% [ 8 ]. In Hungary, GPs motivate participation in cervical cancer screening by 27% of women who initially refused [ 9 ]. In Iran, the GP program reduces the number of not only hospitalizations but also specialist visits [ 10 ]. In China, Family Physician Integrated Care Program in Taiwan indicates that it might reduce hospital admissions in the long term [ 11 ] and a survey of 3148 residents in Hongkong demonstrates people with regular GPs are 2.3% less likely to use emergency services than people without FDs [ 12 ].

China has been striving to establish referral systems since major healthcare reform nationwide in 2009, and family doctors serve as gatekeepers to strengthen primary care [ 13 ]. Several pilot programs of family doctor system reform were launched in multiple regions, and in 2016, seven departments, led by the State Council’s Medical Reform Office and the National Health Commission, officially launched family doctor system reform nationwide, which provided explicit guidelines on contracted services, content, fees, incentive mechanisms, performance assessment, and technical support [ 14 ]. Residents are encouraged to voluntarily contract with family doctors and then contracted residents would be provided with family doctor contract services [ 15 ]. The main objectives of the family doctor system were as follows: (1) to expand the family doctor services by enhancing service capacity, improving the quality of basic public health and health management services, ensuring rational drug use, and providing home-based services. (2) to cover patients with chronic diseases as well as vulnerable populations (the elderly, the pregnant, the children, and the disabled), and to provide accessible health management in primary care, delay disease progression, and prevent unnecessary hospitalization for chronic disease patients. (3) to establish a well-functioning referral system with more health service utilization in community health centers rather than in secondary and tertiary hospitals. In 2018, the coverage rate among the key population reached over 71.3% [ 16 ], which initially forms the function of gatekeeping in several pilot cities [ 17 ].

A few studies in China focused on the impact of family doctors on health service utilization but the net effect was unclear [ 18 , 19 , 20 , 21 ]. After controlling factors such as age, occupation, income level, and medical insurance type, contracted Chinese residents are proven to utilize more primary medical consultations, as well as chronic disease follow-up services, rehabilitation, and nursing services [ 19 ]. A similar result is concluded from research that contracted patients with chronic diseases have higher utilization rates of chronic disease follow-up services than non-contracted patients [ 18 ]. Besides that, surveys conducted in Shanghai [ 20 , 22 ], Hangzhou [ 23 ], and Shenzhen [ 21 ] found more contracted residents first go to and contact community health centers than non-contracted residents. A higher proportion (51.9%) in the first-visit to primary care was presented among the contracted residents [ 24 ]. However, these studies were based on cross-sectional data with a relatively small sample size, and mainly on self-reported services utilization. The net effect of the family doctor system on health service utilization cannot be conducted and more solid data are required. There is also a lack of research to shed light on the change in health service utilization among different institutions, to reflect the role of FD in re-allocating resources and optimizing the referral system.

To evaluate the impact of the family doctor system on health service utilization among patients with hypertension and diabetes, this study presents the change in follow-up service utilization, outpatient service utilization, and inpatient service utilization. The hypotheses of this study were:

The family doctor system would promote the follow-up service and the outpatient service utilization as a result reduce or mitigate the increase in the inpatient service utilization among patients with hypertension and diabetes.

The family doctor system would increase the health service utilization in community health centers and decrease it in secondary and tertiary hospitals among patients with hypertension and diabetes.

Aim and setting

This study aims to evaluate the impact of the family doctor system on health service utilization among patients with hypertension and diabetes in China.

An eastern city in China was selected as the sample city, since it was one of the earliest pilot cities and implemented the family doctor system on January 1st, 2015. In this city, the coverage of social insurance was 98% in 2015, which was slightly higher than the 95% coverage of social insurance nationwide. According to the family doctor system, the insured residents could voluntarily contract with a family doctor, who cooperates with nurses, and public health practitioners as a team. For the contracted, the uniform service contents of the family doctor system included four aspects: (1) improving the accessibility of timely counseling services in community health centers (2) offering high-quality diagnosis and treatment services, and a green channel for accurate referral services (3) providing integrated healthcare and home-based services (4) implementing health management for chronic patients through regular follow-ups. For the non-contracted, services were provided in a traditional way, where they did not have a designated family doctor during visits and received less comprehensive services such as fewer follow-ups for chronic diseases, home care, and rehabilitation. As a result, the non-contracted may directly seek care at secondary or tertiary healthcare institutions, leading to poor continuity of health services and chronic disease management.

Regarding the family doctor system in this city as an intervention, we chose the years 2014 and 2017 as the pre-treatment period and post-treatment period, considering the effect of this reform. The national family doctor system was designed and formulated based on the practices and experiences of our pilot city, which are fundamentally consistent. Also, we focus on the chronic disease patients who are diagnosed with diabetes and hypertension in our study, because they are the key population of registration in the pilot city and also the mainland China.

Data sources

We extracted individual-level data in 2014 and 2017 from two databases and then linked the data by pseudonymous patients’ identification. To be specific, the official health management records were provided by the Community Chronic Disease Management System of the city’s Health Information Center, including patients’ individual information such as demographic information and chronic disease follow-up records. Medical insurance claim data during the study period were extracted from the municipal Medical Insurance Bureau, providing information about outpatient and inpatient service utilization, and the level of medical institutions.

This study restricted the research sample according to the following criteria (Fig.  1 ):

Insurers of social medical insurance before January 1, 2014.

Participants diagnosed with diabetes and hypertension who registered in community health centers before January 1, 2014.

Patients without missing follow-up records in both years.

Flow chart of data resource and research sample

We examined six outcome variables in three sets as follows. (1) We examined the outcome of follow-up service utilization by the average number of annual follow-up visits per capita. The follow-up visits include outpatient visits, phone calls, text messages, and home visits. (2) We examined the four measures of outpatient service utilization: annual outpatient visits per capita, annual outpatient visits in community health centers per capita, annual outpatient visits in secondary and tertiary hospitals per capita, and the proportion of outpatient visits in community health centers. We included the total number of outpatient visits and outpatient visits in different institutions respectively. (3) We examined the outcome of inpatient service utilization, as indicated by annual inpatient admissions per capita.

The key independent variables were two dummy variables “group i ” , “time t ” , and their interaction term “group i *time t ” . The dummy variable “group i ” was created, which equals “1” for the treatment group of contracted patients, who contracted with FDs in 2015 and remained so from then; and “0” for the control group of non-contracted patients, who never contracted with FDs from 2015 to 2017. The other dummy variable “time t ” was conducted to capture the tendency of change within the group. It equals “0” when the year is 2014 and “1” when the year is 2017.

Other control variables were considered to obtain more robust results. Firstly, unequal health service utilization exists by demographic characteristics [ 25 , 26 , 27 ]. To address this issue, age, gender, and insurance type were included. Two main medical insurance in China were the Urban-Rural Resident Basic Medical Insurance (URRBMI), and the Urban Employee Basic Medical Insurance (UEBMI) with further classification of UEBMI for employees and UEBMI for retirees [ 28 ]. Besides that, health status and severity of the disease were also controlled, such as comorbidity of diabetes and hypertension, body mass index (BMI), disease duration (in years), as well as respectively average scores of systolic blood pressure (SBP), diastolic blood pressure (DBP) and fasting blood glucose (FBG). The average scores of SBP, DBP, and FBG were calculated by the proportion of normal results to total tests annually and the higher scores the better disease controlled. These are detailed in Table  1 .

Statistical analysis

A descriptive analysis was conducted and the demographic characteristics at baseline are reported. The t-test was used to compare the difference between contracted and non-contracted patients, with the threshold of statistical significance at P  < 0.05 (two-tailed).

We assumed that determinants of outcomes remained stable within the same city in two groups over time. Differences-in-differences (DID) approach is adopted to measure the net effect of the family doctor system among patients with hypertension and diabetes using the following model:

Considering that healthcare service utilization follows a positively skewed distribution with a right tail, and the values are discrete and non-negative, we used generalized linear models (GLM) with the Poisson distribution and a log link function to regress Eq. (1) respectively for the hypertension models and the diabetes models. The dummy variable “ group i ” and “ time t ” indicated groups and time periods. For six outcomes ( Y i ), the average treatment effect (ATT) is estimated by comparing the change within the treatment group with the change within the control group during the study period. The parameter of interest is the coefficient of the interaction term β 3 , which would capture the different changes in Y it among patients in two groups if the family doctor system had an impact. X ij consists of the control variables as mentioned, and ε it is the error term [ 29 , 30 , 31 ].

As for propensity score matching (PSM), the study included age, gender, comorbidities, insurance type, BMI for all patients, hypertension duration, SBP, and DBP additionally for hypertensive patients, and FBS additionally for diabetes patients as matching variables at the baseline in 2014. Propensity score matching was attempted using 1:1 nearest neighbor matching, 1:3 nearest neighbor matching, kernel matching, and radius matching. It was found that 1:1 nearest neighbor matching yielded the best results but only 31.63% of hypertension patients and 26.46% of diabetes patients were matched. Therefore, the propensity score matching DID (PSM-DID) analysis with 1:1 nearest neighbor matching was chosen for robustness analysis rather than the main analysis.

Baseline characteristics

154,755 patients were finally included as the research sample, consisting of 123,607 patients with hypertension and 31,148 patients with diabetes. 130,697 patients contracted with family doctors and 24,058 were non-contracted. As seen in Table  1 , contracted patients were older than non-contracted patients, who were more likely to be female and enrolled in the Urban-Rural Resident Basic Medical Insurance. The contracted had higher odds of comorbidity of hypertension and diabetes and tended to be overweight (BMI≥25). For the contracted hypertension patients, the course of hypertension was shorter than the non-contracted with an average of 10.39 years. The average scores and absolute value of SBP and DBP implied that at least 93% of hypertension patients controlled their blood pressure and there was a small but significant difference. As for the diabetes patients, the average score and value of FBG also demonstrated the contracted patients were in worse health status than the non-contracted.

Difference-in-differences regression

The study presented the annual follow-up visits, outpatient visits (both in community health centers and in secondary and tertiary hospitals), and inpatient admissions, as well as the proportion of outpatient visits in community health centers for the contracted and non-contracted groups in 2014 and 2017, as shown in Table  2 . We compared different outcome variables between and within groups and all the differences were significant ( p  < 0.001), and a similar pattern of health service utilization among hypertension and diabetes patients was observed.

Compared with the non-contracted patients, the contracted significantly utilized more follow-up services and outpatient services both in 2014 and 2017, with a higher proportion of outpatient visits to community health centers, and the difference between groups became greater in 2017. Fewer outpatient visits in secondary and tertiary hospitals and inpatient admissions remained among the contracted from 2014 to 2017.

From 2014 to 2017, for contracted patients, there was a tendency that follow-up visits, outpatient visits, and outpatient visits in community health centers and its proportion, and inpatient admissions steadily increased while outpatient visits in secondary and tertiary hospitals decreased. The contracted group exhibited a greater increase in outpatient visits in community health centers compared to the overall increase in outpatient visits. During the same period, a slight but significant increase in outpatient visits, outpatient visits in community health centers, and its proportion were observed among non-contracted hypertension and diabetes patients, while follow-up visits and outpatient visits in secondary and tertiary hospitals among them declined. Furthermore, the non-contracted group experienced an increase in inpatient admissions from 2014 to 2017, and the magnitude of this increase was greater than that observed in the contracted group.

We identified a statistically significant impact of the family doctor system on health service utilization in patients with both diseases with the main DID results shown in Table  2 . After controlling other factors, the family doctor system increased the follow-up service and outpatient service utilization. The results of DID models indicate that the contracted patients increased the number of annual follow-up visits (hypertension patients coef. = 0.13, diabetes patients coef. = 0.08, both p  < 0.001), and outpatient visits (hypertension patients coef. = 0.08, diabetes patients coef. = 0.05, both p  < 0.001).

In addition, the structure of outpatient service utilization has changed. More contracted patients went to community health centers for outpatient visits (hypertension patients coef. = 0.09, diabetes patients coef. = 0.07, both p  < 0.001). The proportion of outpatient visits in community health centers among hypertension and diabetes patients also significantly rose (hypertension patients coef. = 0.02, diabetes patients coef. = 0.04, both p  < 0.001). At the same time, outpatient visits in secondary and tertiary hospitals decreased with a greater magnitude in both disease groups (hypertension patients coef. = -0.14, diabetes patients coef. = -0.13, both p  < 0.001).

As for inpatient admissions, the family doctor system may have a lower increase in annual inpatient admissions for contracted patients, but the results are mixed. The results of DID models demonstrate that among hypertensive patients, the family doctor system had a lower increase in annual inpatient admissions of contracted patients compared to non-contracted patients (coef. = -0.02, p  = 0.042). Among diabetes patients, the contracted patients also had less annual inpatient admissions compared to non-contracted patients, but the lower increase is not statistically significant (coef. = -0.09, p  = 0.124).

Robustness analysis

PSM-DID was conducted as the robustness test due to a large number of unmatched samples. After comparing the matching results of four different methods by the reduced bias and t-test of each variable before and after matching, 1:1 nearest matching with logit regression was selected as the best method and the k-density plots before and after matching are shown in Fig.  2 . After 1:1 nearest matching, a total of 47,336 patients (39,094 hypertension patients and 8242 diabetes patients) were propensity score matched and two groups were balanced (Table  1 ).

The k-density plots before and after matching for hypertension and diabetes patients

The description of outcome variables for the matched contracted and non-contracted groups in 2014 and 2017 and the full results of PSM-DID are presented in Table  3 . Consistent with the main analysis results, the differences observed in the comparison of outcome variables between and within the matched groups remained statistically significant ( p  < 0.001). The magnitude of differences between and within groups was similar to that observed in the main analysis. The PSM-DID results showed slightly small estimates for some outcome measures (follow-up visits among hypertension and diabetes patients, and the proportion of outpatient visits in community health centers among diabetes patients). However, all the estimates for the outcome measures were still significant, so the main DID results withstood the robustness test.

Main findings

The study adds to the evidence of the impact of FD on health service utilization, which reveals that the family doctor system increases follow-up visits among contracted patients as well as outpatient visits, while it mitigates the increase in inpatient admissions. It also increases the health service utilization in community health centers and decreases it in secondary and tertiary hospitals among patients with hypertension and diabetes, implying a better structure of outpatient utilization. This paper not only extends previous study findings on the impact of the family doctor system but also enriches practical evidence for developing countries with similar contexts.

The results show that the family doctor system increased the follow-up service and outpatient service utilization and mitigated the increase in inpatient admissions, suggesting that contracted patients may shift their utilization from the inpatient care to the outpatient setting. Consistent with other studies, our study found that the family doctor system increases follow-up visits and outpatient visits among contracted patients. Compared to residents without FDs, contracted residents are proven to utilize more follow-up services, ambulatory primary care visits, and less hospitalization in developed countries [ 5 , 6 , 9 , 10 , 11 , 18 , 19 ]. Though surveys found the first-visit to primary care might increase [ 20 , 21 , 22 , 23 , 24 ], we made a marginal contribution by using the indicator, the proportion of outpatient visits in community health centers, and directly revealing that the family doctor system changes the structure of outpatient care by attracting patients with hypertension and diabetes to community health centers and decreasing the health service utilization in secondary and tertiary hospitals. At the same time, the results revealed an increase in outpatient visits, exceeding the reduction in hospital admissions. This may indicate that in developing countries like China, after systematic optimization of service content within the family doctor system, the accessibility of healthcare services for patients has improved. This has led to the release of unmet medical needs, resulting in an increase in the utilization of related services in the short term [ 2 , 32 ].

Under the family doctor system reform, the substitution effect between outpatient and inpatient care may exist [ 33 , 34 , 35 ]. A possible explanation is that chronic disease management services, such as regular examinations, follow-up services, and health education or counseling, prevent or delay further progress and diminish avoidable inpatient admissions. For example, controlling blood glucose can prevent renal failure in diabetic patients, thus avoiding the utilization of inpatient services due to renal failure [ 33 ]. From the societal perspective, the family doctor system may be beneficial to harnessing health expenditure growth and promoting population health.

Among patients with hypertension and diabetes, in our study, the family doctor system promotes the utilization of follow-up services and outpatient services but mitigates the increase in inpatient services. On the one hand, this result is consistent with former results from developed countries which verified more outpatient service utilization [ 5 , 6 , 10 , 11 ]. On the other hand, unlike previous studies, our research findings do not support the hypothesis that the family doctor system reduces hospital service utilization [ 5 ]. We only observed that in the period of 2014–2017, while both the signed and non-signed groups experienced an increase in hospitalization rates, the family doctor system helped mitigate the growth rate of hospitalizations within the signed group. This may be attributed to the following reasons: Firstly, with the progress of universal health coverage in China, residents’ healthcare demands have been released and met, leading to an overall increase in various types of services including inpatient care. Secondly, the content of China’s family doctor system primarily focuses on strengthening chronic disease management and providing organized and continuous services, with implementation effects mainly concentrated on increasing follow-up and outpatient visits and optimizing the structure of outpatient care. Lastly, as the family doctor system has only recently been implemented in China, its impact on hospital service utilization requires more time, as the effect transmission exhibits a certain lag.

The family doctor system promotes a better structure of outpatient utilization among different institutions. Corresponding to our hypothesis, DID results show that after controlling other factors, compared with non-contracted patients, FD increases the health service utilization in community health centers, but decreases it in secondary and tertiary hospitals among contracted patients with hypertension and diabetes. In other words, FD effectively guides the reasonable diversion of patients to different medical institutions and attracts more patients to community health service centers. In China, the national policy requires “Patients firstly utilize primary care to distinguish patients with urgent or chronic diseases for referrals; establishing a two-way referral system linking the primary health centers with secondary or tertiary hospitals”, and family doctors and their services are identified as the cornerstone of primary health care and the entry point to the referral systems [ 17 , 36 ]. At this stage, we proved that the family doctor system partly realizes the policy goal of referral systems and strengthens the function of community health centers by attracting more outpatient visits from secondary and tertiary hospitals [ 18 , 19 , 20 ]. However, we did not observe the change in emergency visits due to data limitations, while other studies found fewer emergency services were utilized among people with FDs than people without FDs [ 5 , 7 , 8 , 12 ].

Under the backdrop of the family doctor system reform, the chronic disease management implemented in China has been shifting its focus from treatment to prevention and management. In China, before the implementation of the family doctor system, traditional management of chronic diseases was primarily carried out by secondary and tertiary hospitals, neglecting the role of community health centers in follow-up and daily management, which was characterized by fragmentation, lack of systematicity, and disorderliness. The family doctor system introduced a contract-based model for managing chronic diseases, which emphasized the role of community health centers and multidisciplinary team collaboration, to provide comprehensive services such as health assessments, regular follow-ups, health education, and medication management. As a result, by emphasizing prevention, early intervention, and continuous services, the family doctor system would facilitate rational management of chronic diseases, promote the development of primary care, and elevate patients’ health.

Strengths and weaknesses of the study

This study fills the gap in China to quantitively evaluate the effect of the family doctor system for adequate observation time and adds to the evidence of the impact of FD on health service utilization with real-world data and appropriate methods. The study uses panel data to compare changes among contracted and non-contracted patients with hypertension and diabetes in health service utilization before and after the implementation of the family doctor system in real-world settings. The study combined two objective records with detailed information into the big sample size of 154,755 patients and then conducted the DID models and PSM method to rigorously evaluate this intervention with great validity.

Some limitations should be mentioned. Firstly, according to the comparison of the characteristics between contracted and non-contracted patients at baseline, there is a propensity whether a patient chooses to register with family doctors. Patients with hypertension and diabetes, who are female, elderly, with higher BMI and commodity of hypertension and diabetes, and enrolled in URRBMI intended to register with FD. It is consistent with previous Chinese research [ 37 , 38 , 39 ]. A possible reason is that the older the patients are, the worse their health condition, and the greater their motivation exists, and the more likely they register [ 32 ]. Our following study will also pay attention to the long-term impact of this selective behavior on health outcomes and disparity as a consensus mentioned in a local study [ 40 ]. Secondly, we cannot obtain more detailed data on emergency department utilization, medication usage, specific outpatient departments visited, diagnostic and treatment processes, and inpatient treatment, so we were unable to provide a comprehensive analysis of the overall impact of the family doctor system on healthcare utilization. The study can only draw conclusions regarding the optimization of outpatient utilization patterns but cannot infer policy implications regarding resource reallocation and the optimization of the referral system. Thirdly, due to a lack of relevant data, other variables that could reflect the severity of diseases among patients with hypertension and diabetes, were not collected. The severity of diseases was not included in the analysis and matching algorithms, and there may be other unobserved variables that could influence the outcome variables of this study. However, during the matching process, we included comorbidity as an indicator, primarily to capture the coexistence of diabetes and hypertension, attempting to partially reflect the complexity and severity of patients’ conditions. Nevertheless, it would be more accurate if we could conclude more related diseases such as heart disease, stroke, etc. Lastly, due to unavailable data in any other pre-treatment period earlier than 2014 as the baseline, the preliminary analysis cannot be conducted. To solve these two issues, the PSM method was conducted to balance the two groups as well as possible, which also showed robust results [ 41 ]. We will track the dynamic change of the family doctor system and evaluate its impact on health services in future studies.

Implications for policy and practice

Three significant implications are instructive internationally as China and several developing countries consider strengthening gatekeepers in primary care reform. Firstly, drawing upon the experiences from pilot cities and the positive impact of the family doctor system, it is recommended to tailor and improve local regulations based on specific circumstances and implement the family doctor system nationwide. Secondly, considering the insufficient signing rate and selective signing, there is still much room for enhancing publicity across multiple channels so residents can improve awareness of the importance and necessity of contracting with family doctors [ 4 , 42 , 43 ]. Lastly, the key to enhancing the effectiveness of the family doctor system primarily lies in focusing on services related to standard, personalized chronic disease management. For the younger and healthier residents, providing health education, counseling, and individualized services is necessary to attract them to register with FDs [ 42 ]. For other higher-demand groups besides chronic disease patients, such as the poor and the disabled, identifying these populations and fully understanding their requirements are important to promote universal coverage and reduce health disparity [ 44 , 45 ].

In conclusion, this paper constructed Difference-in-Differences models based on the official health management records and medical insurance claim data of the patients with hypertension and diabetes to evaluate the family doctor system on health service utilization. The family doctor system increases follow-up visits and outpatient visits, and mitigates the increase inpatient admissions, and it also promotes a better structure of outpatient utilization with more outpatient visits in community health centers and fewer in secondary and tertiary hospitals. The examined family doctor system strengthens primary care, both by increasing follow-up visits and outpatient visits and promoting a rationalized structure of outpatient utilization in China.

Data availability

The data that support the findings of this study are available from the H city’s Health Information Center and the municipal Medical Insurance Bureau but restrictions apply to the availability of these data, which were used under license for the current study, and so are not publicly available.

Abbreviations

Primary Health Care

Family Physicians

General Practitioner

Family Doctors

Differences-In-Differences

Propensity Score Matching

World Health Organization. Primary health care.|.*2020* 2020 .; 2020.

Yip WN, Fu HQ, Chen AT, Zhai TM, Jian WY, Xu RM, Pan J, Hu M, Zhou ZL, Chen QL et al. 10 years of health-care reform in China: progress and gaps in Universal Health Coverage. LANCET 2019, 394(10204):1192–1204.

World Health Organization. Operational Framework for Primary Health Care.|.*2020* 2020 .; 2020.

Yuan SS, Wang F, Li X, Jia M, Tian MM. Facilitators and barriers to implement the family doctor contracting services in China: findings from a qualitative study. BMJ OPEN 2019, 9(10).

Friedberg MW, Rosenthal MB, Werner RM, Volpp KG, Schneider EC. Effects of a Medical Home and Shared Savings Intervention on Quality and Utilization of Carevol 175, pg 1362, (2015). JAMA INTERN MED 2015, 175(8):1426.

Ha NT, Harris M, Preen D, Robinson S, Moorin R. Identifying patterns of general practitioner service utilisation and their relationship with potentially preventable hospitalisations in people with diabetes: the utility of a cluster analysis approach. DIABETES RES CLIN PR. 2018;138:201–10.

Article   Google Scholar  

Yang BH, Messom R. Association between potential primary care emergency service and general practitioner care utilisation inNew South Wales. EMERG MED AUSTRALAS. 2021;33(1):52–7.

Article   PubMed   Google Scholar  

Almeida A, Vales J. The impact of primary health care reform on hospital emergency department overcrowding: evidence from the Portuguese reform. INT J HEALTH PLAN M. 2020;35(1):368–77.

Gyulai A, Nagy A, Pataki V, Tonte D, Adany R, Voko Z. General practitioners can increase participation in cervical cancer screening - a model program in Hungary. BMC FAM PRACT 2018, 19.

Bayati M, Keshavarz K, Lotfi F, KebriaeeZadeh A, Barati O, Zareian S, Amiri A, Delavari S. Effect of two major health reforms on health care cost and utilization in Fars Province of Iran: family physician program and health transformation plan. BMC HEALTH SERV RES 2020, 20(1).

Huang PT, Kung PT, Kuo WY, Tsai WC. Impact of family physician integrated care program on decreasing utilization of emergency department visit and hospital admission: a population-based retrospective cohort study. BMC HEALTH SERV RES 2020, 20(1).

Fung C, Wong C, Fong D, Lee A, Lam C. Having a family doctor was associated with lower utilization of hospital-based health services. BMC HEALTH SERV RES 2015, 15.

The Central People’s Government of the People’s Republic of China. Opinions on Deepening the Reform of the Medical and Health System.|.*2020* 2020 .; 2009.

National Medical Reform Office. Notice on Promoting Guidance for Family Doctor Contract Services.; 2016.

Shang XP, Huang YM, Li BE, Yang Q, Zhao YR, Wang W, Liu Y, Lin JF, Hu CG, Qiu YW. Residents’ Awareness of Family Doctor Contract Services, Status of Contract with a Family Doctor, and Contract Service Needs in Zhejiang Province, China: A Cross-Sectional Study. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2019, 16(18).

Liang WWCWP. Development Report on Health Reform in China 2020. Beijing: Social Sciences Academic Press (China); 2020.

Google Scholar  

World Health Organization. Healthy China: Deepening Health Reform in China. Washington, DC: World Bank and World Health Organization; 2019.

Li M, Gao Y, Mao Q. Study on the Impact of Family Doctor Contracting Service on Health Service utilization of chronic patients. Health Econ Res. 2019;2(36):38–42.

CAS   Google Scholar  

Qiu B, Huang J, Liang H. A comparative study on the health services utilization and satisfaction of the family doctor service. Chin J Health Policy. 2016;1(9):31–6.

Wang Y, Wang J, Maitland E. Growing old before growing rich: inequality in health service utilization among the mid-aged and elderly in Gansu and Zhejiang Provinces, China. Bmc Health Serv Res. 2012;12(12):302–13.

Article   CAS   PubMed   PubMed Central   Google Scholar  

Zheng QM, Shi L, Pang TT, Leung W. Utilization of community health care centers and family doctor contracts services among community residents: a community-based analysis in Shenzhen, China. BMC FAM PRACT 2021, 22(1).

Huang JL, Liu Y, Zhang T, Wang L, Liu SS, Liang H, Zhang YM, Chen G, Liu CJ. Can family doctor contracted services facilitate orderly visits in the referral system? A frontier policy study from Shanghai, China. INT J HEALTH PLAN M. 2022;37(1):403–16.

Yu Y, Ye AZ, Chen C, Dai WD, Liu X. The impact of family doctor system on patients’ utilisation of general practitioner in primary care facilities-Evidence from Hangzhou, China. INT J HEALTH PLAN M 2022.

Huang JL, Lu W, Wang L, Zhang T, Liu CJ, Liu SS, Liang H, Zhang YM, Guo DF. A preliminary effect analysis of family doctor and medical insurance payment coordination reform in Changning District of Shanghai, China. BMC FAM PRACT 2019, 20.

Wu R. The effects of Contract Service Status from General Practitioner on the Utilization of Health Services among Community residents in Pukou District, Nanjing City. Med Soc. 2019;2(32):9–15.

Klein J, von Dem Knesebeck O. Social disparities in outpatient and inpatient care. An overview of current findings in Germany. BUNDESGESUNDHEITSBLA. 2016;59(2):238–44.

Qian Y, Zhou Z, Yan J, Gao J, Wang Y, Yang X, Xu Y, Li Y. An economy-ralated equity analysis of health service utilization by women in economically underdeveloped regions of western China. INT J EQUITY HEALTH. 2017;16(1):186.

Article   PubMed   PubMed Central   Google Scholar  

Li C, Tang C, Wang H. Effects of health insurance integration on health care utilization and its equity among the mid-aged and elderly: evidence from China. INT J EQUITY HEALTH. 2019;18(1):166.

Rao M, Katyal A, Singh PV, Samarth A, Bergkvist S, Kancharla M, Wagstaff A, Netuveli G, Renton A. Changes in addressing inequalities in access to hospital care in Andhra Pradesh and Maharashtra states of India: a difference-in-differences study using repeated cross-sectional surveys. BMJ OPEN 2014, 4(6).

Stokes J, Kristensen SR, Checkland K, Bower P. Effectiveness of multidisciplinary team case management: difference-in-differences analysis. BMJ OPEN 2016, 6(4).

Keeble E, Bardsley M, Durand MA, Hoomans T, Mays N. Area level impacts on emergency hospital admissions of the integrated care and support pioneer programme in England: difference-in-differences analysis. BMJ OPEN 2019, 9(8).

Yip W, Hsiao W. Harnessing the privatisation of China’s fragmented health-care delivery. Lancet. 2014;384(9945):805–18.

Elek P, Molnar T, Varadi B. The closer the better: does better access to outpatient care prevent hospitalization? EUR J HEALTH ECON. 2019;20(6):801–17.

Adhikari SR. An assessment of a substitute or complement for inpatient and outpatient care of visceral leishmaniasis in Nepal. J VECTOR DIS. 2012;49(4):242–8.

WICKIZER TM, WHEELER J, FELDSTEIN PJ. Have hospital inpatient cost containment programs contributed to the growth in outpatient expenditures - analysis of the substitution effect associated with hospital utilization review. MED CARE. 1991;29(5):442–51.

Yip W, Hsiao W, Meng QY, Chen W, Sun XM. Realignment of incentives for health-care providers in China. Lancet. 2010;375(9720):1120–30.

Huang Y, Feng L, Li Z. Studying on the Status of General practitioners’ services and its influencing factors sampled with Longhua District of Shenzhen. Chin Health Service Manage. 2018;2(31):49–51.

Liu Q, Zhong W, Wang Z. Demands for Family Physician Service among Community Elderly in Wuhan City and Influcing factors. 2018, 2(31):49–51.

Li Q, Huang W, Miao C. Study on urban residents’ willingness of family doctor contract and its influencing factors in Xuzhou. 2018, 3(21):262–6.

Huang J, Zhu Q, Guo J. Can Health Disparity be eliminated? The role of Family Doctor played in Shanghai, China. INT J ENV RES PUB HE 2020, 17(15).

Tang B, Wang Y, Gao Y, Wu S, Li H, Chen Y, Shi Y. The Effect of Boarding on the Mental Health of Primary School students in Western Rural China. INT J ENV RES PUB HE 2020, 17(21).

Mercer SW, Siu JY, Hillier SM, Lam CL, Lo YY, Lam TP, Griffiths SM. A qualitative study of the views of patients with long-term conditions on family doctors in Hong Kong. BMC FAM PRACT. 2010;11:46.

Fu P, Wang Y, Liu S, Li J, Gao Q, Zhou C, Meng Q, Sylvia S. Analysing the preferences for family doctor contract services in rural China: a study using a discrete choice experiment. BMC FAM PRACT. 2020;21(1):148.

World Health Organization. WHO global strategy on people-centred and integrated health services interim report. Geneva, Switzerland: WHO Document Production Services; 2015.

Huang SS, Yin AT, Liu QQ, Sun XH. Can the implementation of family doctor contracted service enable the elderly to utilize primary health care services more equally? Empirical evidence from Shandong, China. BMC Prim CARE 2022, 23(1).

Download references

Acknowledgements

We would like to thank Qiuyue Zhu for her supporting work on data cleaning and analysis.

This work was supported by the National Social Science Foundation of China, 20ZDA072; the Shanghai Pujiang Program, 21PJC024; the Shanghai Philosophy and Social Science Research Program, 2020BGL003.

Author information

Luying Zhang and Peng Zhang are joint first authors.

Authors and Affiliations

School of Public Health, Fudan University, Shanghai, China

Luying Zhang & Wen Chen

School of Humanities, Shanghai Institute of Technology, 100 Haiquan Road, Fengxian District, Shanghai, China

You can also search for this author in PubMed   Google Scholar

Contributions

LZ and WC conceived and designed the study. LZ and ZP analyzed the data. ZP wrote the manuscript. LZ and WC revised the manuscript. The first two authors LZ and PZ contributed equally to this paper and should be regarded as co-first authors. All authors read and approved this manuscript before submission to this journal.

Corresponding author

Correspondence to Wen Chen .

Ethics declarations

Ethics approval and consent to participate.

Since the data we used came from anonymized and secondary databases, human participants were not directly involved in the study, and the informed consent was exempted. The Ethics Review Committee of the School of Public Health, Fudan University approved our study (IRB# 2016-03-19) and waived the requirement of informed consent. All methods were carried out in accordance with relevant guidelines and regulations (declarations of helsinki).

Consent for publication

Not applicable.

Competing interests

The authors declare no competing interests.

Additional information

Publisher’s note.

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ . The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/ ) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

Reprints and permissions

About this article

Cite this article.

Zhang, L., Zhang, P. & Chen, W. Can family doctor system improve health service utilization for patients with hypertension and diabetes in China? A difference-in-differences study. BMC Health Serv Res 24 , 454 (2024). https://doi.org/10.1186/s12913-024-10903-6

Download citation

Received : 21 August 2023

Accepted : 26 March 2024

Published : 11 April 2024

DOI : https://doi.org/10.1186/s12913-024-10903-6

Share this article

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

• Family doctor system
• Gatekeepers
• Health service utilization
• Chronic disease
• Difference-in-differences

BMC Health Services Research

ISSN: 1472-6963

• General enquiries: [email protected]

This paper is in the following e-collection/theme issue:

Published on 11.4.2024 in Vol 26 (2024)

Evaluating the Digital Health Experience for Patients in Primary Care: Mixed Methods Study

Authors of this article:

Original Paper

• Melinda Ada Choy 1, 2 , BMed, MMed, DCH, MD   ; 
• Kathleen O'Brien 1 , BSc, GDipStats, MBBS, DCH   ; 
• Katelyn Barnes 1, 2 , BAPSC, MND, PhD   ; 
• Elizabeth Ann Sturgiss 3 , BMed, MPH, MForensMed, PhD   ; 
• Elizabeth Rieger 1 , BA, MClinPsych, PhD   ; 
• Kirsty Douglas 1, 2 , MBBS, DipRACOG, Grad Cert HE, MD  

1 School of Medicine and Psychology, College of Health and Medicine, The Australian National University, Canberra, Australia

2 Academic Unit of General Practice, Office of Professional Leadership and Education, ACT Health Directorate, Canberra, Australia

3 School of Primary and Allied Health Care, Monash University, Melbourne, Australia

Corresponding Author:

Melinda Ada Choy, BMed, MMed, DCH, MD

School of Medicine and Psychology

College of Health and Medicine

The Australian National University

Phone: 61 51244947

Email: [email protected]

Background: The digital health divide for socioeconomic disadvantage describes a pattern in which patients considered socioeconomically disadvantaged, who are already marginalized through reduced access to face-to-face health care, are additionally hindered through less access to patient-initiated digital health. A comprehensive understanding of how patients with socioeconomic disadvantage access and experience digital health is essential for improving the digital health divide. Primary care patients, especially those with chronic disease, have experience of the stages of initial help seeking and self-management of their health, which renders them a key demographic for research on patient-initiated digital health access.

Objective: This study aims to provide comprehensive primary mixed methods data on the patient experience of barriers to digital health access, with a focus on the digital health divide.

Methods: We applied an exploratory mixed methods design to ensure that our survey was primarily shaped by the experiences of our interviewees. First, we qualitatively explored the experience of digital health for 19 patients with socioeconomic disadvantage and chronic disease and second, we quantitatively measured some of these findings by designing and administering a survey to 487 Australian general practice patients from 24 general practices.

Results: In our qualitative first phase, the key barriers found to accessing digital health included (1) strong patient preference for human-based health services; (2) low trust in digital health services; (3) high financial costs of necessary tools, maintenance, and repairs; (4) poor publicly available internet access options; (5) reduced capacity to engage due to increased life pressures; and (6) low self-efficacy and confidence in using digital health. In our quantitative second phase, 31% (151/487) of the survey participants were found to have never used a form of digital health, while 10.7% (52/487) were low- to medium-frequency users and 48.5% (236/487) were high-frequency users. High-frequency users were more likely to be interested in digital health and had higher self-efficacy. Low-frequency users were more likely to report difficulty affording the financial costs needed for digital access.

Conclusions: While general digital interest, financial cost, and digital health literacy and empowerment are clear factors in digital health access in a broad primary care population, the digital health divide is also facilitated in part by a stepped series of complex and cumulative barriers. Genuinely improving digital health access for 1 cohort or even 1 person requires a series of multiple different interventions tailored to specific sequential barriers. Within primary care, patient-centered care that continues to recognize the complex individual needs of, and barriers facing, each patient should be part of addressing the digital health divide.

Introduction

The promise of ehealth.

The rapid growth of digital health, sped up by the COVID-19 pandemic and associated lockdowns, brings the promise of improved health care efficiency, empowerment of consumers, and health care equity [ 1 ]. Digital health is the use of information and communication technology to improve health [ 2 ]. eHealth, which is a type of digital health, refers to the use of internet-based technology for health care and can be used by systems, providers, and patients [ 2 ]. At the time of this study (before COVID-19), examples of eHealth used by patients in Australia included searching for web-based health information, booking appointments on the web, participating in online peer-support health forums, using mobile phone health apps (mobile health), emailing health care providers, and patient portals for electronic health records.

Digital health is expected to improve chronic disease management and has already shown great potential in improving chronic disease health outcomes [ 3 , 4 ]. Just under half of the Australian population (47.3%) has at least 1 chronic disease [ 5 ]. Rates of chronic disease and complications from chronic disease are overrepresented among those with socioeconomic disadvantage [ 6 ]. Therefore, patients with chronic disease and socioeconomic disadvantage have a greater need for the potential benefits of digital health, such as an improvement in their health outcomes. However, there is a risk that those who could benefit most from digital health services are the least likely to receive them, exemplifying the inverse care law in the digital age by Hart [ 7 ].

Our Current Understanding of the Digital Health Divide

While the rapid growth of digital health brings the promise of health care equity, it may also intensify existing inequities [ 8 ]. The digital health divide for socioeconomic disadvantage describes a pattern in which patients considered socioeconomically disadvantaged who are already marginalized through poor access to traditional health care are additionally hindered through poor access to digital health [ 9 ]. In Australia, only 67.4% of households in the lowest household income quintile have home internet access, compared to 86% of the general population and 96.9% of households in the highest household income quintile [ 10 ]. Survey-based studies have also shown that even with internet access, effective eHealth use is lower in populations considered disadvantaged, which speaks to broader barriers to digital health access [ 11 ].

The ongoing COVID-19 global pandemic has sped up digital health transitions with the rapid uptake of telephone and video consultations, e-prescription, and the ongoing rollout of e-mental health in Australia. These have supported the continuation of health care delivery while limiting physical contact and the pandemic spread; however, the early evidence shows that the digital health divide remains problematic. A rapid review identified challenges with reduced digital access and digital literacy among the older adults and racial and ethnic minority groups, which are both groups at greater health risk from COVID-19 infections [ 12 ]. An Australian population study showed that the rapid uptake of telehealth during peak pandemic was not uniform, with the older adults, very young, and those with limited English language proficiency having a lower uptake of general practitioner (GP) telehealth services [ 13 ].

To ensure that digital health improves health care outcome gaps, it is essential to better understand the nature and nuance of the digital health divide for socioeconomic disadvantage. The nature of the digital health divide for socioeconomic disadvantage has been explored primarily through quantitative survey data, some qualitative papers, a few mixed methods papers, and systematic reviews [ 11 , 14 - 16 ]. Identified barriers include a lack of physical hardware and adequate internet bandwidth, a reduced inclination to seek out digital health, and a low ability and confidence to use digital health effectively [ 16 ]. The few mixed methods studies that exist on the digital health divide generally triangulate quantitative and qualitative data on a specific disease type or population subgroup to draw a combined conclusion [ 17 , 18 ]. These studies have found digital health access to be associated with education, ethnicity, and gender as well as trust, complementary face-to-face services, and the desire for alternative sources of information [ 17 , 19 ].

What This Work Adds

This project sought to extend previous research by using an exploratory mixed methods design to ensure that the first step and driver of our survey of a larger population was primarily shaped by the experiences of our interviewees within primary care. This differs from the triangulation method, which places the qualitative and quantitative data as equal first contributors to the findings and does not allow one type of data to determine the direction of the other [ 18 ]. We qualitatively explored the experience of digital health for patients with socioeconomic disadvantage and chronic disease and then quantitatively measured some of the qualitative findings via a survey of the Australian general practice patient population. Our key objective was to provide comprehensive primary mixed methods data, describing the experience and extent of barriers to accessing digital health and its benefits, with a focus on the digital health divide. We completed this research in a primary care context to investigate a diverse community-based population with conceivable reasons to seek digital help in managing their health. Findings from this mixed methods study were intended to provide health care providers and policy makers with a more detailed understanding of how specific barriers affect different aspects or steps of accessing digital health. Ultimately, understanding digital health access can influence the future design and implementation of digital health services by more effectively avoiding certain barriers or building in enablers to achieve improved digital health access not only for everyone but also especially for those in need.

Study Design

We conducted a sequential exploratory mixed methods study to explore a complex phenomenon in depth and then measure its prevalence. We qualitatively explored the experience of digital health for patients with chronic disease and socioeconomic disadvantage in the first phase. Data from the first phase informed a quantitative survey of the phenomenon across a wider population in the second phase [ 18 ]. Both stages of research were conducted before the COVID-19 pandemic in Australia.

Recruitment

Qualitative phase participants.

The eligibility criteria for the qualitative phase were as follows: English-speaking adults aged ≥18 years with at least 1 self-reported chronic disease and 1 marker of socioeconomic disadvantage (indicated by ownership of a Health Care Card or receiving a disability pension, unemployment, or a user of public housing). A chronic disease was defined to potential participants as a diagnosed long-term health condition that had lasted at least 6 months (or is expected to last for at least 6 months; examples are listed in Multimedia Appendix 1 ). The markers of socioeconomic disadvantage we used to identify potential participants were based on criteria typically used by local general practices to determine which patients can have lower or no out-of-pocket expenses. Apart from unemployment, the 3 other criteria to identify socioeconomic disadvantage are means-tested government-allocated public social services [ 20 ]. Qualitative phase participants were recruited from May to July 2019 through 3 general practices and 1 service organization that serve populations considered socioeconomically disadvantaged across urban, regional, and rural regions in the Australian Capital Territory and South Eastern New South Wales. A total of 2 recruitment methods were used in consultation with and as per the choice of the participating organizations. Potential participants were either provided with an opportunity to engage with researchers (KB and MAC) in the general practice waiting room or identified by the practice or organization as suitable for an interview. Interested participants were given a detailed verbal and written description of the project in a private space before providing written consent to be interviewed. All interview participants received an Aus $50 (US $32.68) grocery shopping voucher in acknowledgment of their time.

Quantitative Phase Participants

Eligibility for the quantitative phase was English-speaking adults aged ≥18 years. The eligibility criteria for the quantitative phase were deliberately broader than those for the qualitative phase to achieve a larger sample size within the limitations of recruitment and with the intention that the factors of socioeconomic disadvantage and having a chronic disease could be compared to the digital health access of a more general population. The quantitative phase participants were recruited from November 2019 to February 2020. Study information and paper-based surveys were distributed and collected through 24 general practices across the Australian Capital Territory and South Eastern New South Wales regions, with an option for web-based completion.

Ethical Considerations

Qualitative and quantitative phase research protocols, including the participant information sheet, were approved by the Australian Capital Territory Health Human Research Ethics Committee (2019/ETH/00013) and the Australian National University Human Research Ethics Committee (2019/ETH00003). Qualitative phase participants were given a verbal and written explanation of the study, including how and when they could opt out, before they provided written consent. All interview participants received an Aus $50 (US $32.68) grocery shopping voucher in acknowledgment of their time. Quantitative participants were given a written explanation and their informed consent was implied by return of a completed survey. Participants in both phases of the study were told that all their data was deidentified. Consent was implied through the return of a completed survey.

Qualitative Data Collection and Analysis

Participants were purposively sampled to represent a range in age, gender, degree of socioeconomic disadvantage, and experience of digital health. The sampling and sample size were reviewed regularly by the research team as the interviews were being completed to identify potential thematic saturation.

The interview guide was developed by the research team based on a review of the literature and the patient dimensions of the framework of access by Levesque et al [ 21 ]. The framework by Levesque et al [ 21 ] is a conceptualization of health care access comprising 5 service and patient dimensions of accessibility and ability. The patient dimensions are as follows: (1) ability to perceive, (2) ability to seek, (3) ability to reach, (4) ability to pay, and (5) ability to engage [ 21 ]. The key interview topics included (1) digital health use and access, including facilitators and barriers; (2) attitudes toward digital health; and (3) self-perception of digital health skills and potential training. The interview guide was reviewed for face and content validity by the whole research team, a patient advocate, a digital inclusion charity representative, and the general practices where recruitment occurred. The questions and guide were iteratively refined by the research team to ensure relevance and support reaching data saturation. The interview guide has been provided as Multimedia Appendix 1 . The interviews, which took 45 minutes on average, were taped and transcribed. An interview summary sheet and reflective journal were completed by the interviewer after each interview to also capture nonverbal cues and tone.

Interview transcriptions were coded and processed by inductive thematic analysis. Data collection and analysis were completed in parallel to support the identification of data saturation. Data saturation was defined as no significant new information arising from new interviews and was identified by discussion with the research team [ 22 ]. The 2 interviewers (MAC and KB) independently coded the first 5 transcripts and reflected on them with another researcher (EAS) to ensure intercoder validity and reliability. The rest of the interviews were coded independently by the 2 interviewers, who regularly met to reflect on emerging themes and thematic saturation. Data saturation was initially indicated after 15 interviews and subsequently confirmed with a total of 19 interviews. Coding disagreements and theme development were discussed with at least 1 other researcher (EAS, ER, and KD). Thematic saturation and the final themes were agreed upon by the entire research team.

Quantitative Survey Development

The final themes derived in the qualitative phase of the project guided the specific quantitative phase research questions. The final themes were a list of ordered cumulative barriers experienced by participants in accessing digital health and its benefits ( Figure 1 ). The quantitative survey was designed to test the association between barriers to access and the frequency of use of digital health as a proxy measure for digital health access.

In the survey, the participants were asked about their demographic details, health and chronic diseases, knowledge, use and experience of digital health tools, internet access, perception of digital resource affordability, trust in digital health and traditional health services, perceived capability, health care empowerment, eHealth literacy, and relationship with their GP.

Existing scales and questions from the literature and standardized Australian-based surveys were used whenever possible. We used selected questions and scales from the Australian Bureau of Statistics standards, the eHealth Literacy Scale (eHEALS), the eHealth Literacy Questionnaire, and the Southgate Institute for Health Society and Equity [ 17 , 23 - 26 ]. We adapted other scales from the ICEpop Capability Measure for Adults, the Health Care Empowerment Inventory (HCEI), the Patient-Doctor Relationship Questionnaire, and the Chao continuity questionnaire [ 23 , 27 - 29 ]. Where an existing scale to measure a barrier or theme did not exist, the research team designed the questions based on the literature. Our questions around the frequency of digital health use were informed by multiple existing Australian-based surveys on general technology use [ 30 , 31 ]. Most of the questions used a Likert scale. Every choice regarding the design, adaptation, or copy of questions for the survey was influenced by the qualitative findings and decided on by full agreement among the 2 researchers who completed and coded the interviews. A complete copy of the survey is provided in Multimedia Appendix 2 .

Pilot-testing of the survey was completed with 5 patients, 2 experts on digital inclusion, and 3 local GPs for both the paper surveys and web-based surveys via Qualtrics Core XM (Qualtrics LLC). The resulting feedback on face and content validity, functionality of the survey logic, and feasibility of questionnaire completion was incorporated into the final version of the survey.

The survey was offered on paper with a participant information sheet, which gave the patients the option to complete the web-based survey. The survey was handed out to every patient on paper to avoid sampling bias through the exclusion of participants who could not complete the web-based survey [ 32 ].

Quantitative Data Treatment and Analysis

Data were exported from Qualtrics Core XM to an SPSS (version 26; IBM Corp) data set. Data cleaning and screening were undertaken (KB and KO).

Descriptive statistics (number and percentage) were used to summarize participant characteristics, preference measures, and frequency of eHealth use. Significance testing was conducted using chi-square tests, with a threshold of P <.05; effect sizes were measured by the φ coefficient for 2×2 comparisons and Cramer V statistic for all others. Where the cells sizes were too small, the categories were collapsed for the purposes of significance testing. The interpretation of effect sizes was as per the study by Cohen [ 33 ]. The analysis was conducted in SPSS and SAS (version 9.4; SAS Institute).

Participant Characteristics

Participants’ self-reported characteristics included gender, indigenous status, income category, highest level of education, marital status, and language spoken at home.

Age was derived from participant-reported year of birth and year of survey completion as of 2019 and stratified into age groups. The state or territory of residence was derived from the participant-reported postcode. The remoteness area was derived using the postcode reported by the participants and mapped to a modified concordance from the Australian Bureau of Statistics. Occupation-free text responses were coded using the Australian Bureau of Statistics Census statistics level 1 and 2 descriptors. The country of birth was mapped to Australia, other Organisation for Economic Cooperation and Development countries, and non–Organisation for Economic Cooperation and Development countries.

Frequency of eHealth Use

A summary measure of the frequency of eHealth use was derived from the questions on the use of different types of eHealth.

Specifically, respondents were asked if they had ever used any form of web-based health (“eHealth“) and, if so, to rate how often (never, at least once, every now and then, and most days) against 6 types of “eHealth” (searching for health information online, booking appointments online, emailing health care providers, using health-related mobile phone apps, accessing My Health Record, and accessing online health forums). The frequency of eHealth use was then classified as follows:

• High user: answered “most days” to at least 1 question on eHealth use OR answered “every now and then” to at least 2 questions on eHealth use
• Never user: answered “no” to having ever used any form of eHealth OR “never” to all 6 questions on eHealth use
• Low or medium user: all other respondents.

The frequency of eHealth use was reported as unweighted descriptive statistics (counts and percentages) against demographic characteristics and for the elements of each of the themes identified in phase 1.

Overview of Key Themes

Data were reported against the 6 themes from the phase 1 results of preference, trust, cost, structural access, capacity to engage, and self-efficacy. Where the components of trust, cost, capacity to engage, and self-efficacy had missing data (for less than half of the components only), mean imputation was used to minimize data loss. For each theme, the analysis excluded those for whom the frequency of eHealth use was unknown.

Preference measures (survey section D1 parts 1 to 3) asked participants to report against measures with a 4-point Likert scale (strongly disagree, disagree, agree, and strongly agree). Chi-square tests were conducted after the categories were condensed into 2 by combining strongly disagree and as well as combining strongly agree and agree.

Summary measures for trust were created in 4 domains: trust from the eHealth Literacy Questionnaire (survey section D1 parts 4 to 8), trust from Southgate—GPs, specialists, or allied health (survey section D2 parts 1 to 5), trust from Southgate—digital health (survey section D2 parts 6, 7, 9, and 10), and trust from Southgate—books or pamphlets (survey section D2 part 8). The data were grouped as low, moderate, and high trust based on the assigned scores from the component data. Chi-square tests were conducted comparing low-to-moderate trust against high trust for GP, specialists, or allied health and comparing low trust against moderate-to-high trust for book or pamphlet.

Summary measures for cost were created from survey item C10. To measure cost, participants were asked about whether they considered certain items or services to be affordable. These included cost items mentioned in the qualitative phase interviews relating to mobile phones (1 that connects to the internet, 1 with enough memory space to download apps, downloads or apps requiring payment, repairs, and maintenance costs), having an iPad or tablet with internet connectivity, a home computer or laptop (owning, repairs, and maintenance), home fixed internet access, and an adequate monthly data allowance. These 9 items were scored as “yes definitely”=1 or 0 otherwise. Chi-square tests were conducted with never and low or medium eHealth users combined.

Structural Access

Structural access included asking where the internet is used by participants (survey section C8) and factors relating to internet access (survey section C8 parts 1-3) reporting against a 4-point Likert scale (strongly disagree, disagree, agree, and strongly agree). Chi-square tests were conducted with strongly disagree, disagree, agree, or strongly agree, and never, low, or medium eHealth use combined.

Capacity to Engage

Summary measures for capacity to engage were created from survey section E1. To measure the capacity to engage, participants were asked about feeling “settled and secure,” “being independent,” and “achievement and progress” as an adaptation of the ICEpop Capability Measure for Adults [ 27 ], reporting against a 4-point Likert-like scale. Responses were scored from 1 (“I am unable to feel settled and secure in any areas of my life”) to 4 (“I am able to feel settled and secure in all areas of my life”).

The summary capacity measure was derived by the summation of responses across the 3 questions, which were classified into 4 groups, A to D, based on these scores. Where fewer than half of the responses were missing, mean imputation was used; otherwise, the record was excluded. Groups A and B were combined for significance testing.

Self-Efficacy

Summary measures for self-efficacy were adapted from the eHEALS (E3) and the HCEI (E2) [ 23 , 24 ].

Survey section E3—eHEALS—comprised 8 questions, with participants reporting against a 5-point Likert scale for each (strongly disagree, disagree, neither, agree, and strongly agree). These responses were assigned 1 to 5 points, respectively. The summary eHEALS measure was derived by the summation of responses across the 8 questions, which were classified into 5 groups, A to E, based on these scores. Where fewer than half of the responses were missing, mean imputation was used; otherwise, the record was excluded. Groups A to C and D to E were combined for significance testing.

Survey section E2—HCEI—comprised 5 questions, with participants reporting against a 5-point Likert scale for each (strongly disagree, disagree, neither, agree, and strongly agree). Strongly disagree and disagree and neither were combined, and similarly agree and strongly agree were combined for significance testing.

Qualitative Results

The demographic characteristics of the patients that we interviewed are presented in Table 1 .

The key barriers found to accessing digital health included (1) strong patient preference for human-based health services; (2) low trust in digital health services; (3) high financial costs of necessary tools, maintenance, and repairs; (4) poor publicly available internet access options; (5) reduced capacity to engage due to increased life pressures; and (6) low self-efficacy and confidence in using digital health.

Rather than being an equal list of factors, our interviewees described these barriers as a stepped series of cumulative hurdles, which is illustrated in Figure 1 . Initial issues of preference and trust were foundational to a person even when considering the option of digital health, while digital health confidence and literacy were barriers to full engagement with and optimal use of digital health. Alternatively, interviewees who did use digital health had been enabled by the same factors that were barriers to others.

a GP: general practitioner.

b Multiple answers per respondent.

Strong Patient Preference for Human-Based Health Services

Some patients expressed a strong preference for human-based health services rather than digital health services. In answer to a question about how digital health services could be improved, a patient said the following:

Well, having an option where you can actually bypass actually having to go through the app and actually talk directly to someone. [Participant #10]

For some patients, this preference for human-based health services appeared to be related to a lack of exposure to eHealth. These patients were not at all interested in or had never thought about digital health options. A participant responded the following to the interviewer’s questions:

Interviewer: So when...something feels not right, how do you find out what’s going on?

Respondent: I talk to Doctor XX.

Interviewer: Do you ever Google your symptoms or look online for information?

Respondent: No, I have never even thought of doing that actually. [Participant #11]

For other patients, their preference for human-based health care stemmed from negative experiences with technology. These patients reported actively disliking computers and technology in general and were generally frustrated with what they saw as the pitfalls of technology. A patient stated the following:

If computers and internet weren’t so frigging slow because everything is on like the slowest speed network ever and there’s ads blocking everything. Ads, (expletive) ads. [Participant #9]

A patient felt that he was pushed out of the workforce due his inability to keep up with technology-based changes and thus made a decision to never own a computer:

But, you know, in those days when I was a lot younger those sorts of things weren’t about and they’re just going ahead in leaps and bounds and that’s one of the reasons why I retired early. I retired at 63 because it was just moving too fast and it’s all computers and all those sorts of things and I just couldn’t keep up. [Participant #17]

Low Trust in Digital Health Services

Several patients described low trust levels for digital and internet-based technology in general. Their low trust was generally based on stories they had heard of other people’s negative experiences. A patient said the following:

I don’t trust the internet to be quite honest. You hear all these stories about people getting ripped off and I’ve worked too hard to get what I’ve got rather than let some clown get it on the internet for me. [Participant #11]

Some of this distrust was specific to eHealth. For example, some patients were highly suspicious of the government’s motives with regard to digital health and were concerned about the privacy of their health information, which made them hesitant about the concept of a universal electronic health record. In response to the interviewer’s question, a participant said the following:

Interviewer: Are there any other ways you think that eHealth might help you?

Respondent: I’m sorry but it just keeps coming back to me, Big Brother. [Participant #7]

Another participant said the following:

I just would run a mile from it because I just wouldn’t trust it. It wouldn’t be used to, as I said, for insurance or job information. [Participant #16]

High Financial Costs of the Necessary Tools, Maintenance, and Repairs

A wide variety of patients described affordability issues across several different aspects of the costs involved in digital health. They expressed difficulty in paying for the following items: a mobile phone that could connect to the internet, a mobile phone with enough memory space to download apps, mobile phone apps requiring extra payment without advertisements, mobile phone repair costs such as a broken screen, a computer or laptop, home internet access, and adequate monthly data allowance and speeds to functionally use the internet. Current popular payment systems, such as plans, were not feasible for some patients. A participant stated the following:

I don’t have a computer...I’m not in the income bracket to own a computer really. Like I could, if I got one on a plan kind of thing or if I saved up for x-amount of time. But then like if I was going on the plan I’d be paying interest for having it on like lay-buy kind of thing, paying it off, and if it ever got lost or stolen I would still have to repay that off, which is always a hassle. And yeah. Yeah, I’m like financially not in the state where I’m able to...own a computer right now as I’m kind of paying off a number of debts. [Participant #9]

Poor Publicly Available Internet Access Options

Some patients described struggling without home internet access. While they noted some cost-free public internet access points, such as libraries, hotel bars, and restaurants, they often found these to be inconvenient, lacking in privacy, and constituting low-quality options for digital health. A patient stated the following:

...it’s incredibly slow at the library. And I know why...a friend I went to school with used to belong to the council and the way they set it up, they just got the raw end of the stick and it is really, really slow. It’s bizarre but you can go to the X Hotel and it’s heaps quicker. [Participant #15]

In response to the interviewer's question, a participant said the following:

Interviewer: And do you feel comfortable doing private stuff on computers at the library...?

Respondent: Not really, no, but I don’t have any other choice, so, yeah. [Participant #9]

Reduced Capacity to Engage Due to Increased Life Pressures

When discussing why they were not using digital health or why they had stopped using digital health, patients often described significant competing priorities and life pressures that affected their capacity to engage. An unemployed patient mentioned that his time and energy on the internet were focused primarily on finding work and that he barely had time to focus on his health in general, let alone engage in digital health.

Other patients reported that they often felt that their ability to learn about and spend time on digital health was taken up by caring for sick family members, paying basic bills, or learning English. Some patients said that the time they would have spent learning digital skills when they were growing up had been lost to adverse life circumstances such as being in jail:

So we didn’t have computers in the house when I was growing up. And I didn’t know I’ve never...I’ve been in and out of jail for 28 odd years so it sort of takes away from learning from this cause it’s a whole different… it’s a whole different way of using a telephone from a prison. [Participant #11]

Low Self-Efficacy and Confidence in Starting the Digital Health Process

Some patients had a pervasive self-perception of being slow learners and being unable to use technology. Their stories of being unconfident learners seemed to stem from the fact that they had been told throughout their lives that they were intellectually behind. A patient said the following:

The computer people...wouldn’t take my calls because I’ve always been dumb with that sort of stuff. Like I only found out this later on in life, but I’m actually severely numerically dyslexic. Like I have to triple-check everything with numbers. [Participant #7]

Another patient stated the following:

I like went to two English classes like a normal English class with all the kids and then another English class with about seven kids in there because I just couldn’t I don’t know maybe because I spoke another language at home and they sort of like know I was a bit backward. [Participant #6]

These patients and others had multiple missing pieces of information that they felt made it harder to engage in digital health compared to “easier” human-based services. A patient said the following:

Yeah I’ve heard of booking online but I just I don’t know I find it easier just to ring up. And I’ll answer an email from a health care provider but I wouldn’t know where to start to look for their email address. [Participant #11]

In contrast, the patients who did connect with digital health described themselves as independent question askers and proactive people. Even when they did not know how to use a specific digital health tool, they were confident in attempting to and asking for help when they needed it. A patient said the following:

I’m a “I will find my way through this, no matter how long it takes me” kind of person. So maybe it’s more my personality...If I have to ask for help from somewhere, wherever it is, I will definitely do that. [Participant #3]

Quantitative Results

A total of 487 valid survey responses were received from participants across 24 general practices. The participant characteristics are presented in detail in Table S1 in Multimedia Appendix 3 .

The mean age of the participants was approximately 50 years (females 48.9, SD 19.4 years; males 52.8, SD 20.0 years), and 68.2% (332/487) of the participants identified as female. Overall, 34.3% (151/439) of respondents reported never using eHealth, and 53.8% (236/439) reported high eHealth use.

There were statistically significant ( P <.05) differences in the frequency of eHealth use in terms of age group, gender, state, remoteness, highest level of education, employment status, occupation group, marital status, and language spoken at home, with effect sizes being small to medium. Specifically, high eHealth characteristics were associated with younger age, being female, living in an urban area, and being employed.

Table 2 presents the frequency of eHealth use against 3 internet preference questions.

Preference for using the internet and technology in general and for health needs in particular were significantly related to the frequency of eHealth use ( P <.05 for each), with the effect sizes being small to medium.

a Excludes those for whom frequency of eHealth use is unknown.

b Chi-square tests conducted with strongly disagree and disagree combined, and agree and strongly agree combined.

Table 3 presents the frequency of eHealth use against 4 measures of trust.

The degree of trust was not statistically significantly different for the frequency of eHealth use for any of the domains.

b eHLQ: eHealth Literacy Questionnaire.

c Derived from survey question D1, parts 4 to 8. Mean imputation used where ≤2 responses were missing. If >2 responses were missing, the records were excluded.

d Derived from survey question D2, parts 1 to 5. Mean imputation used where ≤2 responses were missing. If >2 responses were missing, the records were excluded.

e Chi-square test conducted comparing low-to-moderate trust against high trust.

f Derived from survey question D2, parts 6, 7, 9, and 10. Mean imputation used where ≤2 responses were missing. If >2 responses were missing, the records were excluded.

g Derived from survey question D2 part 8.

h Chi-square test conducted comparing low trust against moderate-to-high trust.

Affordability of items and services was reported as No cost difficulty or Cost difficulty. eHealth frequency of use responses were available for 273 participants; among those with no cost difficulty , 1% (2/204) were never users, 14.2% (29/204) were low or medium users, and 84.8% (173/204) were high users of eHealth; among those with cost difficulty , 1% (1/69) were never users, 26% (18/69) were low or medium users, and 73% (50/69) were high users. There was a statistically significant difference in the presence of cost as a barrier between never and low or medium eHealth users compared to high users ( χ 2 1 =5.25; P =.02), although the effect size was small.

Table 4 presents the frequency of eHealth use for elements of structural access.

Quality of internet access and feeling limited in access to the internet were significantly associated with frequency of eHealth use ( P <.05), although the effect sizes were small.

b N/A: not applicable (cell sizes insufficient for chi-square test).

c Chi-square tests conducted with strongly disagree and disagree combined, agree and strongly agree combined, and never and low or medium categories combined.

Table 5 presents the frequency of eHealth use against respondents’ capacity to engage.

Capacity to engage was not significantly different for the frequency of eHealth use ( P =.54). 

b Derived from survey item E1. Where 1 response was missing, the mean imputation was used. If >1 response was missing, the record was excluded.

c Chi-square tests conducted with groups A and B combined.

Table 6 presents the frequency of eHealth use for elements of self-efficacy.

Statistically significant results were observed for the relationship between self-efficacy by eHEALS (moderate effect size) and frequency of eHealth use as well as for some of the questions from the HCEI (reliance on health professionals or others to access and explain information; small effect size; P <.05).

b eHEALS: eHealth Literacy Scale.

c eHEALS derived from item E3 (8 parts). Where ≤ 4 responses were missing, mean imputation was used. If >4 responses were missing, the records were excluded. Groups A to C as well as groups D to E were combined for the chi-square test.

d Strongly disagree, disagree, neither, and agree or strongly agree combined for significance testing.

Principal Findings

This paper reports on the findings of a sequential exploratory mixed methods study on the barriers to digital health access for a group of patients in Australian family medicine, with a particular focus on chronic disease and socioeconomic disadvantage.

In the qualitative first phase, the patients with socioeconomic disadvantage and chronic disease described 6 cumulative barriers, as demonstrated in Figure 1 . Many nonusers of digital health preferred human-based services and were not interested in technology, while others were highly suspicious of the technology in general. Some digitally interested patients could not afford quality hardware and internet connectivity, a barrier that was doubled by low quality and privacy when accessing publicly available internet connections. Furthermore, although some digitally interested patients had internet access, their urgent life circumstances left scarce opportunity to access digital health and develop digital health skills and confidence.

In our quantitative second phase, 31% (151/487) of the survey participants from Australian general practices were found to have never used a form of digital health. Survey participants were more likely to use digital health tools frequently when they also had a general digital interest and a digital health interest. Those who did not frequently access digital health were more likely to report difficulty affording the financial costs needed for digital access. The survey participants who frequently accessed digital health were more likely to have high eHealth literacy and high levels of patient empowerment.

Comparison With Prior Work

In terms of general digital health access, the finding that 31% (151/487) of the survey participants had never used one of the described forms of eHealth is in keeping with an Australian-based general digital participation study that found that approximately 9% of the participants were nonusers and 17% rarely engaged with the internet at all [ 34 ]. With regard to the digital health divide, another Australian-based digital health divide study found that increased age, living in a lower socioeconomic area, being Aboriginal or Torres Strait Islander, being male, and having no tertiary education were factors negatively associated with access to digital health services [ 17 ]. Their findings correspond to our findings that higher-frequency users of eHealth were associated with younger age, being female, living in an urban area, and being employed. Both studies reinforce the evidence of the digital health divide based on gender, age, and socioeconomic disadvantage in Australia.

With regard to digital health barriers, our findings provide expanded details on the range of digital health items and services that present a cost barrier to consumers. Affordability is a known factor in digital access and digital health access, and it is measured often by general self-report or relative expenditure on internet access to income [ 30 ]. Our study revealed the comprehensive list of relevant costs for patients. Our study also demonstrated factors of cost affordability beyond the dollar value of an item, as interviewees described the struggle of using slow public internet access without privacy features and the risks involved in buying a computer in installments. When we reflected on the complexity and detail of the cost barrier in our survey, participants demonstrated a clear association between cost and the frequency of digital health use. This suggests that a way to improve digital health access for some people is to improve the quality, security, and accessibility of public internet access options as well as to provide free or subsidized hardware, internet connection, and maintenance options for those in need, work that is being done by at least 1 digital inclusion charity in the United Kingdom [ 35 ].

Many studies recognize the factors of eHealth literacy and digital confidence for beneficial digital health access [ 36 ]. Our interviews demonstrated that some patients with socioeconomic disadvantage have low digital confidence, but that this is often underlined by a socially reinforced lifelong low self-confidence in their intellectual ability. In contrast, active users, regardless of other demographic factors, described themselves as innately proactive question askers. This was reinforced by our finding of a relationship between health care empowerment and the frequency of eHealth use. This suggests that while digital health education and eHealth literacy programs can improve access for some patients, broader and deeper long-term solutions addressing socioeconomic drivers of digital exclusion are needed to improve digital health access for some patients with socioeconomic disadvantage [ 8 ]. The deep permeation of socially enforced low self-confidence and lifelong poverty experienced by some interviewees demonstrate that the provision of free hardware and a class on digital health skills can be, for some, a superficial offering when the key underlying factor is persistent general socioeconomic inequality.

The digital health divide literature tends to identify the digital health divide, the factors and barriers that contribute to it, and the potential for it to widen if not specifically addressed [ 16 ]. Our findings have also identified the divide and the barriers, but what this study adds through our qualitative phase in particular is a description of the complex interaction of those barriers and the stepped nature of some of those barriers as part of the individual’s experience in trying to access digital health.

Strengths and Limitations

A key strength of this study is the use of a sequential exploratory mixed methods design. The initial qualitative phase guided a phenomenological exploration of digital health access experiences for patients with chronic disease and socioeconomic disadvantage. Our results in both study phases stem from the patients’ real-life experiences of digital health access. While some of our results echo the findings of other survey-based studies on general digital and digital health participation, our method revealed a greater depth and detail of some of these barriers, as demonstrated in how our findings compare to prior work.

As mentioned previously, the emphasis of this study on the qualitative first phase is a strength that helped describe the interactions between different barriers. The interviewees described their experiences as cumulative unequal stepped barriers rather than as producing a nonordered list of equal barriers. These findings expand on the known complexity of the issue of digital exclusion and add weight to the understanding that improving digital health access needs diverse, complex solutions [ 17 ]. There is no panacea for every individual’s digital health access, and thus, patient-centered digital health services, often guided by health professionals within the continuity of primary care, are also required to address the digital health divide [ 37 ].

While the sequential exploratory design is a strength of the study, it also created some limitations for the second quantitative phase. Our commitment to using the qualitative interview findings to inform the survey questions meant that we were unable to use previously validated scales for every question and that our results were less likely to lead to a normal distribution. This likely affected our ability to demonstrate significant associations for some barriers. We expect that further modeling is required to control for baseline characteristics and determine barrier patterns for different types of users.

One strength of this study is that the survey was administered to a broad population of Australian family medicine patients with diverse patterns of health via both paper-based and digital options. Many other digital health studies use solely digital surveys, which can affect the sample. However, we cannot draw conclusions from our survey about patients with chronic disease due to the limitations of the sample size for these subgroups.

Another sample-based limitation of this study was that our qualitative population did not include anyone aged from 18 to 24 years, despite multiple efforts to recruit. Future research will hopefully address this demographic more specifically.

While not strictly a limitation, we recognize that because this research was before COVID-19, it did not include questions about telehealth, which has become much more mainstream in recent years. The patients may also have changed their frequency of eHealth use because of COVID-19 and an increased reliance on digital services in general. Future work in this area or future versions of this survey should include telehealth and acknowledge the impact of COVID-19. However, the larger concept of the digital health divide exists before and after COVID-19, and in fact, our widespread increased reliance on digital services makes the digital divide an even more pressing issue [ 12 ].

Conclusions

The experience of digital health access across Australian primary care is highly variable and more difficult to access for those with socioeconomic disadvantage. While general digital interest, financial cost, and digital health literacy and empowerment are clear factors in digital health access in a broad primary care population, the digital health divide is also facilitated in part by a stepped series of complex and cumulative barriers.

Genuinely improving digital health access for 1 cohort or even 1 person requires a series of multiple different interventions tailored to specific sequential barriers. Given the rapid expansion of digital health during the global COVID-19 pandemic, attention to these issues is necessary if we are to avoid entrenching inequities in access to health care. Within primary care, patient-centered care that continues to recognize the complex individual needs of, and barriers facing, each patient should be a part of addressing the digital health divide.

Acknowledgments

The authors are thankful to the patients who shared their experiences with them via interview and survey completion. The authors are also very grateful to the general practices in the Australian Capital Territory and New South Wales who kindly gave their time and effort to help organize interviews, administer, and post surveys in the midst of the stress of day-to-day practice life and the bushfires of 2018-2019. The authors thank and acknowledge the creators of the eHealth Literacy Scale, the eHealth Literacy Questionnaire, the ICEpop Capability Measure for Adults, the Health Care Empowerment Inventory, the Patient-Doctor Relationship Questionnaire, the Chao continuity questionnaire, and the Southgate Institute for Health Society and Equity for their generosity in sharing their work with the authors [ 17 , 19 - 25 ]. This study would not have been possible without the support of the administrative team of the Academic Unit of General Practice. This project was funded by the Royal Australian College of General Practitioners (RACGP) through the RACGP Foundation IPN Medical Centres Grant, and the authors gratefully acknowledge their support.

Data Availability

The data sets generated during this study are not publicly available due to the nature of our original ethics approval but are available from the corresponding author on reasonable request.

Authors' Contributions

MAC acquired the funding, conceptualized the project, and organized interview recruitment. MAC and KB conducted interviews and analyzed the qualitative data. EAS, ER, and KD contributed to project planning, supervision and qualitative data analysis. MAC, KB and KO wrote the survey and planned quantitative data analysis. MAC and KB recruited practices for survey administration. KO and KB conducted the quantitative data analysis. MAC and KO, with KB drafted the paper. EAS, ER, and KD helped with reviewing and editing the paper.

Conflicts of Interest

None declared.

Phase 1 interview guide.

Phase 2 survey: eHealth and digital divide.

Phase 2 participant characteristics by frequency of eHealth use.

• Eysenbach G. What is e-health? J Med Internet Res. 2001;3(2):E20. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Iyawa GE, Herselman M, Botha A. Digital health innovation ecosystems: from systematic literature review to conceptual framework. Procedia Comput Sci. 2016;100:244-252. [ FREE Full text ] [ CrossRef ]
• Berrouiguet S, Baca-García E, Brandt S, Walter M, Courtet P. Fundamentals for future mobile-health (mHealth): a systematic review of mobile phone and web-based text messaging in mental health. J Med Internet Res. Jun 10, 2016;18(6):e135. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Shen H, van der Kleij RM, van der Boog PJ, Chang X, Chavannes NH. Electronic health self-management interventions for patients with chronic kidney disease: systematic review of quantitative and qualitative evidence. J Med Internet Res. Nov 05, 2019;21(11):e12384. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Australia's health 2018. Australian Institute of Health and Welfare. 2018. URL: https://www.aihw.gov.au/reports/australias-health/australias-health-2018/contents/table-of-contents [accessed 2024-04-04]
• Australian Institute of Health and Welfare. Chronic Diseases and Associated Risk Factors in Australia, 2006. Canberra, Australia. Australian Institute of Health and Welfare; 2006.
• Hart JT. The inverse care law. The Lancet. Feb 27, 1971;297(7696):405-412. [ CrossRef ]
• Davies AR, Honeyman M, Gann B. Addressing the digital inverse care law in the time of COVID-19: potential for digital technology to exacerbate or mitigate health inequalities. J Med Internet Res. Apr 07, 2021;23(4):e21726. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Choi NG, Dinitto DM. The digital divide among low-income homebound older adults: internet use patterns, eHealth literacy, and attitudes toward computer/internet use. J Med Internet Res. May 02, 2013;15(5):e93. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Household use of information technology. Australian Bureau of Statistics. 2018. URL: https://tinyurl.com/4efm6u92 [accessed 2024-03-24]
• Kontos E, Blake KD, Chou WY, Prestin A. Predictors of eHealth usage: insights on the digital divide from the health information national trends survey 2012. J Med Internet Res. Jul 16, 2014;16(7):e172. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Litchfield I, Shukla D, Greenfield S. Impact of COVID-19 on the digital divide: a rapid review. BMJ Open. Oct 12, 2021;11(10):e053440. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Butler DC, Joshy G, Douglas KA, Sayeed MS, Welsh J, Douglas A, et al. Changes in general practice use and costs with COVID-19 and telehealth initiatives: analysis of Australian whole-population linked data. Br J Gen Pract. Apr 27, 2023;73(730):e364-e373. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Arsenijevic J, Tummers L, Bosma N. Adherence to electronic health tools among vulnerable groups: systematic literature review and meta-analysis. J Med Internet Res. Feb 06, 2020;22(2):e11613. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Kontos EZ, Bennett GG, Viswanath K. Barriers and facilitators to home computer and internet use among urban novice computer users of low socioeconomic position. J Med Internet Res. Oct 22, 2007;9(4):e31. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Latulippe K, Hamel C, Giroux D. Social health inequalities and eHealth: a literature review with qualitative synthesis of theoretical and empirical studies. J Med Internet Res. Apr 27, 2017;19(4):e136. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Foley K, Freeman T, Ward P, Lawler A, Osborne R, Fisher M. Exploring access to, use of and benefits from population-oriented digital health services in Australia. Health Promot Int. Aug 30, 2021;36(4):1105-1115. [ CrossRef ] [ Medline ]
• Cresswell JW, Plano Clark VL. Designing and Conducting Mixed Methods Research. Thousand Oaks, CA. SAGE Publications; 2007.
• Tappen RM, Cooley ME, Luckmann R, Panday S. Digital health information disparities in older adults: a mixed methods study. J Racial Ethn Health Disparities. Feb 2022;9(1):82-92. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Who can get a card. Services Australia. URL: https://www.servicesaustralia.gov.au/who-can-get-health-care-card?context=21981 [accessed 2023-11-03]
• Levesque JF, Harris MF, Russell G. Patient-centred access to health care: conceptualising access at the interface of health systems and populations. Int J Equity Health. Mar 11, 2013;12:18. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Bryant A, Charmaz K. The SAGE Handbook of Grounded Theory, Paperback Edition. Thousand Oaks, CA. SAGE Publications; 2010.
• Johnson MO, Rose CD, Dilworth SE, Neilands TB. Advances in the conceptualization and measurement of health care empowerment: development and validation of the health care empowerment inventory. PLoS One. 2012;7(9):e45692. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Norman CD, Skinner HA. eHEALS: the eHealth literacy scale. J Med Internet Res. Nov 14, 2006;8(4):e27. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Kayser L, Karnoe A, Furstrand D, Batterham R, Christensen KB, Elsworth G, et al. A multidimensional tool based on the eHealth literacy framework: development and initial validity testing of the eHealth Literacy Questionnaire (eHLQ). J Med Internet Res. Feb 12, 2018;20(2):e36. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Standards. Australian Bureau of Statistics. URL: https://www.abs.gov.au/statistics/standards [accessed 2024-04-04]
• Al-Janabi H, Flynn TN, Coast J. Development of a self-report measure of capability wellbeing for adults: the ICECAP-A. Qual Life Res. Feb 2012;21(1):167-176. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Van der Feltz-Cornelis CM, Van Oppen P, Van Marwijk HW, De Beurs E, Van Dyck R. A patient-doctor relationship questionnaire (PDRQ-9) in primary care: development and psychometric evaluation. Gen Hosp Psychiatry. 2004;26(2):115-120. [ CrossRef ] [ Medline ]
• Chao J. Continuity of care: incorporating patient perceptions. Fam Med. 1988;20(5):333-337. [ Medline ]
• Wilson CK, Thomas J, Barraket J. Measuring digital inequality in Australia: the Australian digital inclusion index. JTDE. Jun 30, 2019;7(2):102-120. [ CrossRef ]
• Digital participation: a view of Australia's online behaviours. Australia Post. Jul 2017. URL: https://auspost.com.au/content/dam/auspost_corp/media/documents/white-paper-digital-inclusion.pdf [accessed 2024-04-04]
• Poli A, Kelfve S, Motel-Klingebiel A. A research tool for measuring non-participation of older people in research on digital health. BMC Public Health. Nov 08, 2019;19(1):1487. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Cohen J. Statistical Power Analysis for the Behavioral Sciences Second Edition. London, UK. Routledge; 1988.
• Borg K, Smith L. Digital inclusion and online behaviour: five typologies of Australian internet users. Behav Inf Technol. Feb 15, 2018;37(4):367-380. [ CrossRef ]
• Mathers A, Richardson J, Vincent S, Joseph C, Stone E. Good Things Foundation COVID-19 response report. Good Things Foundation. 2020. URL: https://tinyurl.com/2peu3kak [accessed 2024-04-04]
• Norman CD, Skinner HA. eHealth literacy: essential skills for consumer health in a networked world. J Med Internet Res. Jun 16, 2006;8(2):e9. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Neves AL, Burgers J. Digital technologies in primary care: implications for patient care and future research. Eur J Gen Pract. Dec 11, 2022;28(1):203-208. [ FREE Full text ] [ CrossRef ] [ Medline ]

Abbreviations

Edited by T Leung; submitted 03.07.23; peer-reviewed by T Freeman, H Shen; comments to author 16.08.23; revised version received 30.11.23; accepted 31.01.24; published 11.04.24.

©Melinda Ada Choy, Kathleen O'Brien, Katelyn Barnes, Elizabeth Ann Sturgiss, Elizabeth Rieger, Kirsty Douglas. Originally published in the Journal of Medical Internet Research (https://www.jmir.org), 11.04.2024.

This is an open-access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in the Journal of Medical Internet Research, is properly cited. The complete bibliographic information, a link to the original publication on https://www.jmir.org/, as well as this copyright and license information must be included.