how to write a data analysis for a research paper

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Data Analysis in Research: Types & Methods

Content Index

Why analyze data in research?

Types of data in research, finding patterns in the qualitative data, methods used for data analysis in qualitative research, preparing data for analysis, methods used for data analysis in quantitative research, considerations in research data analysis, what is data analysis in research.

Definition of research in data analysis: According to LeCompte and Schensul, research data analysis is a process used by researchers to reduce data to a story and interpret it to derive insights. The data analysis process helps reduce a large chunk of data into smaller fragments, which makes sense. 

Three essential things occur during the data analysis process — the first is data organization . Summarization and categorization together contribute to becoming the second known method used for data reduction. It helps find patterns and themes in the data for easy identification and linking. The third and last way is data analysis – researchers do it in both top-down and bottom-up fashion.

LEARN ABOUT: Research Process Steps

On the other hand, Marshall and Rossman describe data analysis as a messy, ambiguous, and time-consuming but creative and fascinating process through which a mass of collected data is brought to order, structure and meaning.

We can say that “the data analysis and data interpretation is a process representing the application of deductive and inductive logic to the research and data analysis.”

Researchers rely heavily on data as they have a story to tell or research problems to solve. It starts with a question, and data is nothing but an answer to that question. But, what if there is no question to ask? Well! It is possible to explore data even without a problem – we call it ‘Data Mining’, which often reveals some interesting patterns within the data that are worth exploring.

Irrelevant to the type of data researchers explore, their mission and audiences’ vision guide them to find the patterns to shape the story they want to tell. One of the essential things expected from researchers while analyzing data is to stay open and remain unbiased toward unexpected patterns, expressions, and results. Remember, sometimes, data analysis tells the most unforeseen yet exciting stories that were not expected when initiating data analysis. Therefore, rely on the data you have at hand and enjoy the journey of exploratory research. 

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Every kind of data has a rare quality of describing things after assigning a specific value to it. For analysis, you need to organize these values, processed and presented in a given context, to make it useful. Data can be in different forms; here are the primary data types.

• Qualitative data: When the data presented has words and descriptions, then we call it qualitative data . Although you can observe this data, it is subjective and harder to analyze data in research, especially for comparison. Example: Quality data represents everything describing taste, experience, texture, or an opinion that is considered quality data. This type of data is usually collected through focus groups, personal qualitative interviews , qualitative observation or using open-ended questions in surveys.
• Quantitative data: Any data expressed in numbers of numerical figures are called quantitative data . This type of data can be distinguished into categories, grouped, measured, calculated, or ranked. Example: questions such as age, rank, cost, length, weight, scores, etc. everything comes under this type of data. You can present such data in graphical format, charts, or apply statistical analysis methods to this data. The (Outcomes Measurement Systems) OMS questionnaires in surveys are a significant source of collecting numeric data.
• Categorical data: It is data presented in groups. However, an item included in the categorical data cannot belong to more than one group. Example: A person responding to a survey by telling his living style, marital status, smoking habit, or drinking habit comes under the categorical data. A chi-square test is a standard method used to analyze this data.

Learn More : Examples of Qualitative Data in Education

Data analysis in qualitative research

Data analysis and qualitative data research work a little differently from the numerical data as the quality data is made up of words, descriptions, images, objects, and sometimes symbols. Getting insight from such complicated information is a complicated process. Hence it is typically used for exploratory research and data analysis .

Although there are several ways to find patterns in the textual information, a word-based method is the most relied and widely used global technique for research and data analysis. Notably, the data analysis process in qualitative research is manual. Here the researchers usually read the available data and find repetitive or commonly used words. 

For example, while studying data collected from African countries to understand the most pressing issues people face, researchers might find  “food”  and  “hunger” are the most commonly used words and will highlight them for further analysis.

LEARN ABOUT: Level of Analysis

The keyword context is another widely used word-based technique. In this method, the researcher tries to understand the concept by analyzing the context in which the participants use a particular keyword.  

For example , researchers conducting research and data analysis for studying the concept of ‘diabetes’ amongst respondents might analyze the context of when and how the respondent has used or referred to the word ‘diabetes.’

The scrutiny-based technique is also one of the highly recommended  text analysis  methods used to identify a quality data pattern. Compare and contrast is the widely used method under this technique to differentiate how a specific text is similar or different from each other. 

For example: To find out the “importance of resident doctor in a company,” the collected data is divided into people who think it is necessary to hire a resident doctor and those who think it is unnecessary. Compare and contrast is the best method that can be used to analyze the polls having single-answer questions types .

Metaphors can be used to reduce the data pile and find patterns in it so that it becomes easier to connect data with theory.

Variable Partitioning is another technique used to split variables so that researchers can find more coherent descriptions and explanations from the enormous data.

LEARN ABOUT: Qualitative Research Questions and Questionnaires

There are several techniques to analyze the data in qualitative research, but here are some commonly used methods,

• Content Analysis:  It is widely accepted and the most frequently employed technique for data analysis in research methodology. It can be used to analyze the documented information from text, images, and sometimes from the physical items. It depends on the research questions to predict when and where to use this method.
• Narrative Analysis: This method is used to analyze content gathered from various sources such as personal interviews, field observation, and  surveys . The majority of times, stories, or opinions shared by people are focused on finding answers to the research questions.
• Discourse Analysis:  Similar to narrative analysis, discourse analysis is used to analyze the interactions with people. Nevertheless, this particular method considers the social context under which or within which the communication between the researcher and respondent takes place. In addition to that, discourse analysis also focuses on the lifestyle and day-to-day environment while deriving any conclusion.
• Grounded Theory:  When you want to explain why a particular phenomenon happened, then using grounded theory for analyzing quality data is the best resort. Grounded theory is applied to study data about the host of similar cases occurring in different settings. When researchers are using this method, they might alter explanations or produce new ones until they arrive at some conclusion.

LEARN ABOUT: 12 Best Tools for Researchers

Data analysis in quantitative research

The first stage in research and data analysis is to make it for the analysis so that the nominal data can be converted into something meaningful. Data preparation consists of the below phases.

Phase I: Data Validation

Data validation is done to understand if the collected data sample is per the pre-set standards, or it is a biased data sample again divided into four different stages

• Fraud: To ensure an actual human being records each response to the survey or the questionnaire
• Screening: To make sure each participant or respondent is selected or chosen in compliance with the research criteria
• Procedure: To ensure ethical standards were maintained while collecting the data sample
• Completeness: To ensure that the respondent has answered all the questions in an online survey. Else, the interviewer had asked all the questions devised in the questionnaire.

Phase II: Data Editing

More often, an extensive research data sample comes loaded with errors. Respondents sometimes fill in some fields incorrectly or sometimes skip them accidentally. Data editing is a process wherein the researchers have to confirm that the provided data is free of such errors. They need to conduct necessary checks and outlier checks to edit the raw edit and make it ready for analysis.

Phase III: Data Coding

Out of all three, this is the most critical phase of data preparation associated with grouping and assigning values to the survey responses . If a survey is completed with a 1000 sample size, the researcher will create an age bracket to distinguish the respondents based on their age. Thus, it becomes easier to analyze small data buckets rather than deal with the massive data pile.

LEARN ABOUT: Steps in Qualitative Research

After the data is prepared for analysis, researchers are open to using different research and data analysis methods to derive meaningful insights. For sure, statistical analysis plans are the most favored to analyze numerical data. In statistical analysis, distinguishing between categorical data and numerical data is essential, as categorical data involves distinct categories or labels, while numerical data consists of measurable quantities. The method is again classified into two groups. First, ‘Descriptive Statistics’ used to describe data. Second, ‘Inferential statistics’ that helps in comparing the data .

Descriptive statistics

This method is used to describe the basic features of versatile types of data in research. It presents the data in such a meaningful way that pattern in the data starts making sense. Nevertheless, the descriptive analysis does not go beyond making conclusions. The conclusions are again based on the hypothesis researchers have formulated so far. Here are a few major types of descriptive analysis methods.

Measures of Frequency

• Count, Percent, Frequency
• It is used to denote home often a particular event occurs.
• Researchers use it when they want to showcase how often a response is given.

Measures of Central Tendency

• Mean, Median, Mode
• The method is widely used to demonstrate distribution by various points.
• Researchers use this method when they want to showcase the most commonly or averagely indicated response.

Measures of Dispersion or Variation

• Range, Variance, Standard deviation
• Here the field equals high/low points.
• Variance standard deviation = difference between the observed score and mean
• It is used to identify the spread of scores by stating intervals.
• Researchers use this method to showcase data spread out. It helps them identify the depth until which the data is spread out that it directly affects the mean.

Measures of Position

• Percentile ranks, Quartile ranks
• It relies on standardized scores helping researchers to identify the relationship between different scores.
• It is often used when researchers want to compare scores with the average count.

For quantitative research use of descriptive analysis often give absolute numbers, but the in-depth analysis is never sufficient to demonstrate the rationale behind those numbers. Nevertheless, it is necessary to think of the best method for research and data analysis suiting your survey questionnaire and what story researchers want to tell. For example, the mean is the best way to demonstrate the students’ average scores in schools. It is better to rely on the descriptive statistics when the researchers intend to keep the research or outcome limited to the provided  sample  without generalizing it. For example, when you want to compare average voting done in two different cities, differential statistics are enough.

Descriptive analysis is also called a ‘univariate analysis’ since it is commonly used to analyze a single variable.

Inferential statistics

Inferential statistics are used to make predictions about a larger population after research and data analysis of the representing population’s collected sample. For example, you can ask some odd 100 audiences at a movie theater if they like the movie they are watching. Researchers then use inferential statistics on the collected  sample  to reason that about 80-90% of people like the movie. 

Here are two significant areas of inferential statistics.

• Estimating parameters: It takes statistics from the sample research data and demonstrates something about the population parameter.
• Hypothesis test: I t’s about sampling research data to answer the survey research questions. For example, researchers might be interested to understand if the new shade of lipstick recently launched is good or not, or if the multivitamin capsules help children to perform better at games.

These are sophisticated analysis methods used to showcase the relationship between different variables instead of describing a single variable. It is often used when researchers want something beyond absolute numbers to understand the relationship between variables.

Here are some of the commonly used methods for data analysis in research.

• Correlation: When researchers are not conducting experimental research or quasi-experimental research wherein the researchers are interested to understand the relationship between two or more variables, they opt for correlational research methods.
• Cross-tabulation: Also called contingency tables,  cross-tabulation  is used to analyze the relationship between multiple variables.  Suppose provided data has age and gender categories presented in rows and columns. A two-dimensional cross-tabulation helps for seamless data analysis and research by showing the number of males and females in each age category.
• Regression analysis: For understanding the strong relationship between two variables, researchers do not look beyond the primary and commonly used regression analysis method, which is also a type of predictive analysis used. In this method, you have an essential factor called the dependent variable. You also have multiple independent variables in regression analysis. You undertake efforts to find out the impact of independent variables on the dependent variable. The values of both independent and dependent variables are assumed as being ascertained in an error-free random manner.
• Frequency tables: The statistical procedure is used for testing the degree to which two or more vary or differ in an experiment. A considerable degree of variation means research findings were significant. In many contexts, ANOVA testing and variance analysis are similar.
• Analysis of variance: The statistical procedure is used for testing the degree to which two or more vary or differ in an experiment. A considerable degree of variation means research findings were significant. In many contexts, ANOVA testing and variance analysis are similar.
• Researchers must have the necessary research skills to analyze and manipulation the data , Getting trained to demonstrate a high standard of research practice. Ideally, researchers must possess more than a basic understanding of the rationale of selecting one statistical method over the other to obtain better data insights.
• Usually, research and data analytics projects differ by scientific discipline; therefore, getting statistical advice at the beginning of analysis helps design a survey questionnaire, select data collection methods , and choose samples.

LEARN ABOUT: Best Data Collection Tools

• The primary aim of data research and analysis is to derive ultimate insights that are unbiased. Any mistake in or keeping a biased mind to collect data, selecting an analysis method, or choosing  audience  sample il to draw a biased inference.
• Irrelevant to the sophistication used in research data and analysis is enough to rectify the poorly defined objective outcome measurements. It does not matter if the design is at fault or intentions are not clear, but lack of clarity might mislead readers, so avoid the practice.
• The motive behind data analysis in research is to present accurate and reliable data. As far as possible, avoid statistical errors, and find a way to deal with everyday challenges like outliers, missing data, data altering, data mining , or developing graphical representation.

LEARN MORE: Descriptive Research vs Correlational Research The sheer amount of data generated daily is frightening. Especially when data analysis has taken center stage. in 2018. In last year, the total data supply amounted to 2.8 trillion gigabytes. Hence, it is clear that the enterprises willing to survive in the hypercompetitive world must possess an excellent capability to analyze complex research data, derive actionable insights, and adapt to the new market needs.

LEARN ABOUT: Average Order Value

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Writing a Good Data Analysis Report: 7 Steps

As a data analyst, you feel most comfortable when you’re alone with all the numbers and data. You’re able to analyze them with confidence and reach the results you were asked to find. But, this is not the end of the road for you. You still need to write a data analysis report explaining your findings to the laymen - your clients or coworkers.

That means you need to think about your target audience, that is the people who’ll be reading your report.

They don’t have nearly as much knowledge about data analysis as you do. So, your report needs to be straightforward and informative. The article below will help you learn how to do it. Let’s take a look at some practical tips you can apply to your data analysis report writing and the benefits of doing so.

source: Pexels  

Data Analysis Report Writing: 7 Steps

The process of writing a data analysis report is far from simple, but you can master it quickly, with the right guidance and examples of similar reports .

This is why we've prepared a step-by-step guide that will cover everything you need to know about this process, as simply as possible. Let’s get to it.

Consider Your Audience

You are writing your report for a certain target audience, and you need to keep them in mind while writing. Depending on their level of expertise, you’ll need to adjust your report and ensure it speaks to them. So, before you go any further, ask yourself:

Who will be reading this report? How well do they understand the subject?

Let’s say you’re explaining the methodology you used to reach your conclusions and find the data in question. If the reader isn’t familiar with these tools and software, you’ll have to simplify it for them and provide additional explanations.

So, you won't be writing the same type of report for a coworker who's been on your team for years or a client who's seeing data analysis for the first time. Based on this determining factor, you'll think about:

the language and vocabulary you’re using

abbreviations and level of technicality

the depth you’ll go into to explain something

the type of visuals you’ll add

Your readers’ expertise dictates the tone of your report and you need to consider it before writing even a single word.

Draft Out the Sections

The next thing you need to do is create a draft of your data analysis report. This is just a skeleton of what your report will be once you finish. But, you need a starting point.

So, think about the sections you'll include and what each section is going to cover. Typically, your report should be divided into the following sections:

Introduction

Body (Data, Methods, Analysis, Results)

For each section, write down several short bullet points regarding the content to cover. Below, we'll discuss each section more elaborately.

Develop The Body

The body of your report is the most important section. You need to organize it into subsections and present all the information your readers will be interested in.

We suggest the following subsections.

Explain what data you used to conduct your analysis. Be specific and explain how you gathered the data, what your sample was, what tools and resources you’ve used, and how you’ve organized your data. This will give the reader a deeper understanding of your data sample and make your report more solid.

Also, explain why you choose the specific data for your sample. For instance, you may say “ The sample only includes data of the customers acquired during 2021, in the peak of the pandemic.”

Next, you need to explain what methods you’ve used to analyze the data. This simply means you need to explain why and how you choose specific methods. You also need to explain why these methods are the best fit for the goals you’ve set and the results you’re trying to reach.

Back up your methodology section with background information on each method or tool used. Explain how these resources are typically used in data analysis.

After you've explained the data and methods you've used, this next section brings those two together. The analysis section shows how you've analyzed the specific data using the specific methods. 

This means you’ll show your calculations, charts, and analyses, step by step. Add descriptions and explain each of the steps. Try making it as simple as possible so that even the most inexperienced of your readers understand every word.

This final section of the body can be considered the most important section of your report. Most of your clients will skim the rest of the report to reach this section. 

Because it’ll answer the questions you’ve all raised. It shares the results that were reached and gives the reader new findings, facts, and evidence. 

So, explain and describe the results using numbers. Then, add a written description of what each of the numbers stands for and what it means for the entire analysis. Summarize your results and finalize the report on a strong note. 

Write the Introduction

Yes, it may seem strange to write the introduction section at the end, but it’s the smartest way to do it. This section briefly explains what the report will cover. That’s why you should write it after you’ve finished writing the Body.

In your introduction, explain:

the question you’ve raised and answered with the analysis

context of the analysis and background information

short outline of the report

Simply put, you’re telling your audience what to expect.

Add a Short Conclusion

Finally, the last section of your paper is a brief conclusion. It only repeats what you described in the Body, but only points out the most important details.

It should be less than a page long and use straightforward language to deliver the most important findings. It should also include a paragraph about the implications and importance of those findings for the client, customer, business, or company that hired you.

Include Data Visualization Elements

You have all the data and numbers in your mind and find it easy to understand what the data is saying. But, to a layman or someone less experienced than yourself, it can be quite a puzzle. All the information that your data analysis has found can create a mess in the head of your reader.

So, you should simplify it by using data visualization elements.

Firstly, let’s define what are the most common and useful data visualization elements you can use in your report:

There are subcategories to each of the elements and you should explore them all to decide what will do the best job for your specific case. For instance, you'll find different types of charts including, pie charts, bar charts, area charts, or spider charts.

For each data visualization element, add a brief description to tell the readers what information it contains. You can also add a title to each element and create a table of contents for visual elements only.

Proofread & Edit Before Submission

All the hard work you’ve invested in writing a good data analysis report might go to waste if you don’t edit and proofread. Proofreading and editing will help you eliminate potential mistakes, but also take another objective look at your report.

First, do the editing part. It includes:

reading the whole report objectively, like you’re seeing it for the first time

leaving an open mind for changes

adding or removing information

rearranging sections

finding better words to say something

You should repeat the editing phase a couple of times until you're completely happy with the result. Once you're certain the content is all tidied up, you can move on to the proofreading stage. It includes:

finding and removing grammar and spelling mistakes

rethinking vocabulary choices

improving clarity 

improving readability

You can use an online proofreading tool to make things faster. If you really want professional help, Grab My Essay is a great choice. Their professional writers can edit and rewrite your entire report, to make sure it’s impeccable before submission.

Whatever you choose to do, proofread yourself or get some help with it, make sure your report is well-organized and completely error-free.

Benefits of Writing Well-Structured Data Analysis Reports

Yes, writing a good data analysis report is a lot of hard work. But, if you understand the benefits of writing it, you’ll be more motivated and willing to invest the time and effort. After knowing how it can help you in different segments of your professional journey, you’ll be more willing to learn how to do it.

Below are the main benefits a data analysis report brings to the table.

Improved Collaboration

When you’re writing a data analysis report, you need to be aware more than one end user is going to use it. Whether it’s your employer, customer, or coworker - you need to make sure they’re all on the same page. And when you write a data analysis report that is easy to understand and learn from, you’re creating a bridge between all these people.

Simply, all of them are given accurate data they can rely on and you’re thus removing the potential misunderstandings that can happen in communication. This improves the overall collaboration level and makes everyone more open and helpful.

Increased Efficiency

People who are reading your data analysis report need the information it contains for some reason. They might use it to do their part of the job, to make decisions, or report further to someone else. Either way, the better your report, the more efficient it'll be. And, if you rely on those people as well, you'll benefit from this increased productivity as well.

Data tells a story about a business, project, or venture. It's able to show how well you've performed, what turned out to be a great move, and what needs to be reimagined. This means that a data analysis report provides valuable insight and measurable KPIs (key performance indicators) that you’re able to use to grow and develop. 

Clear Communication

Information is key regardless of the industry you're in or the type of business you're doing. Data analysis finds that information and proves its accuracy and importance. But, if those findings and the information itself aren't communicated clearly, it's like you haven't even found them.

This is why a data analysis report is crucial. It will present the information less technically and bring it closer to the readers.

Final Thoughts

As you can see, it takes some skill and a bit more practice to write a good data analysis report. But, all the effort you invest in writing it will be worth it once the results kick in. You’ll improve the communication between you and your clients, employers, or coworkers. People will be able to understand, rely on, and use the analysis you’ve conducted.

So, don’t be afraid and start writing your first data analysis report. Just follow the 7 steps we’ve listed and use a tool such as ProWebScraper to help you with website data analysis. You’ll be surprised when you see the result of your hard work.

Jessica Fender is a business analyst and a blogger. She writes about business and data analysis, networking in this sector, and acquiring new skills. Her goal is to provide fresh and accurate information that readers can apply instantly.

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How To Write A Research Paper

Step-By-Step Tutorial With Examples + FREE Template

By: Derek Jansen (MBA) | Expert Reviewer: Dr Eunice Rautenbach | March 2024

For many students, crafting a strong research paper from scratch can feel like a daunting task – and rightly so! In this post, we’ll unpack what a research paper is, what it needs to do , and how to write one – in three easy steps. 🙂 

Overview: Writing A Research Paper

What (exactly) is a research paper.

• How to write a research paper
• Stage 1 : Topic & literature search
• Stage 2 : Structure & outline
• Stage 3 : Iterative writing
• Key takeaways

Let’s start by asking the most important question, “ What is a research paper? ”.

Simply put, a research paper is a scholarly written work where the writer (that’s you!) answers a specific question (this is called a research question ) through evidence-based arguments . Evidence-based is the keyword here. In other words, a research paper is different from an essay or other writing assignments that draw from the writer’s personal opinions or experiences. With a research paper, it’s all about building your arguments based on evidence (we’ll talk more about that evidence a little later).

Now, it’s worth noting that there are many different types of research papers , including analytical papers (the type I just described), argumentative papers, and interpretative papers. Here, we’ll focus on analytical papers , as these are some of the most common – but if you’re keen to learn about other types of research papers, be sure to check out the rest of the blog .

With that basic foundation laid, let’s get down to business and look at how to write a research paper .

Overview: The 3-Stage Process

While there are, of course, many potential approaches you can take to write a research paper, there are typically three stages to the writing process. So, in this tutorial, we’ll present a straightforward three-step process that we use when working with students at Grad Coach.

These three steps are:

• Finding a research topic and reviewing the existing literature
• Developing a provisional structure and outline for your paper, and
• Writing up your initial draft and then refining it iteratively

Let’s dig into each of these.

Need a helping hand?

Step 1: Find a topic and review the literature

As we mentioned earlier, in a research paper, you, as the researcher, will try to answer a question . More specifically, that’s called a research question , and it sets the direction of your entire paper. What’s important to understand though is that you’ll need to answer that research question with the help of high-quality sources – for example, journal articles, government reports, case studies, and so on. We’ll circle back to this in a minute.

The first stage of the research process is deciding on what your research question will be and then reviewing the existing literature (in other words, past studies and papers) to see what they say about that specific research question. In some cases, your professor may provide you with a predetermined research question (or set of questions). However, in many cases, you’ll need to find your own research question within a certain topic area.

Finding a strong research question hinges on identifying a meaningful research gap – in other words, an area that’s lacking in existing research. There’s a lot to unpack here, so if you wanna learn more, check out the plain-language explainer video below.

Once you’ve figured out which question (or questions) you’ll attempt to answer in your research paper, you’ll need to do a deep dive into the existing literature – this is called a “ literature search ”. Again, there are many ways to go about this, but your most likely starting point will be Google Scholar .

If you’re new to Google Scholar, think of it as Google for the academic world. You can start by simply entering a few different keywords that are relevant to your research question and it will then present a host of articles for you to review. What you want to pay close attention to here is the number of citations for each paper – the more citations a paper has, the more credible it is (generally speaking – there are some exceptions, of course).

Ideally, what you’re looking for are well-cited papers that are highly relevant to your topic. That said, keep in mind that citations are a cumulative metric , so older papers will often have more citations than newer papers – just because they’ve been around for longer. So, don’t fixate on this metric in isolation – relevance and recency are also very important.

Beyond Google Scholar, you’ll also definitely want to check out academic databases and aggregators such as Science Direct, PubMed, JStor and so on. These will often overlap with the results that you find in Google Scholar, but they can also reveal some hidden gems – so, be sure to check them out.

Once you’ve worked your way through all the literature, you’ll want to catalogue all this information in some sort of spreadsheet so that you can easily recall who said what, when and within what context. If you’d like, we’ve got a free literature spreadsheet that helps you do exactly that.

Step 2: Develop a structure and outline

With your research question pinned down and your literature digested and catalogued, it’s time to move on to planning your actual research paper .

It might sound obvious, but it’s really important to have some sort of rough outline in place before you start writing your paper. So often, we see students eagerly rushing into the writing phase, only to land up with a disjointed research paper that rambles on in multiple

Now, the secret here is to not get caught up in the fine details . Realistically, all you need at this stage is a bullet-point list that describes (in broad strokes) what you’ll discuss and in what order. It’s also useful to remember that you’re not glued to this outline – in all likelihood, you’ll chop and change some sections once you start writing, and that’s perfectly okay. What’s important is that you have some sort of roadmap in place from the start.

At this stage you might be wondering, “ But how should I structure my research paper? ”. Well, there’s no one-size-fits-all solution here, but in general, a research paper will consist of a few relatively standardised components:

• Introduction
• Literature review
• Methodology

Let’s take a look at each of these.

First up is the introduction section . As the name suggests, the purpose of the introduction is to set the scene for your research paper. There are usually (at least) four ingredients that go into this section – these are the background to the topic, the research problem and resultant research question , and the justification or rationale. If you’re interested, the video below unpacks the introduction section in more detail. 

The next section of your research paper will typically be your literature review . Remember all that literature you worked through earlier? Well, this is where you’ll present your interpretation of all that content . You’ll do this by writing about recent trends, developments, and arguments within the literature – but more specifically, those that are relevant to your research question . The literature review can oftentimes seem a little daunting, even to seasoned researchers, so be sure to check out our extensive collection of literature review content here .

With the introduction and lit review out of the way, the next section of your paper is the research methodology . In a nutshell, the methodology section should describe to your reader what you did (beyond just reviewing the existing literature) to answer your research question. For example, what data did you collect, how did you collect that data, how did you analyse that data and so on? For each choice, you’ll also need to justify why you chose to do it that way, and what the strengths and weaknesses of your approach were.

Now, it’s worth mentioning that for some research papers, this aspect of the project may be a lot simpler . For example, you may only need to draw on secondary sources (in other words, existing data sets). In some cases, you may just be asked to draw your conclusions from the literature search itself (in other words, there may be no data analysis at all). But, if you are required to collect and analyse data, you’ll need to pay a lot of attention to the methodology section. The video below provides an example of what the methodology section might look like.

By this stage of your paper, you will have explained what your research question is, what the existing literature has to say about that question, and how you analysed additional data to try to answer your question. So, the natural next step is to present your analysis of that data . This section is usually called the “results” or “analysis” section and this is where you’ll showcase your findings.

Depending on your school’s requirements, you may need to present and interpret the data in one section – or you might split the presentation and the interpretation into two sections. In the latter case, your “results” section will just describe the data, and the “discussion” is where you’ll interpret that data and explicitly link your analysis back to your research question. If you’re not sure which approach to take, check in with your professor or take a look at past papers to see what the norms are for your programme.

Alright – once you’ve presented and discussed your results, it’s time to wrap it up . This usually takes the form of the “ conclusion ” section. In the conclusion, you’ll need to highlight the key takeaways from your study and close the loop by explicitly answering your research question. Again, the exact requirements here will vary depending on your programme (and you may not even need a conclusion section at all) – so be sure to check with your professor if you’re unsure.

Step 3: Write and refine

Finally, it’s time to get writing. All too often though, students hit a brick wall right about here… So, how do you avoid this happening to you?

Well, there’s a lot to be said when it comes to writing a research paper (or any sort of academic piece), but we’ll share three practical tips to help you get started.

First and foremost , it’s essential to approach your writing as an iterative process. In other words, you need to start with a really messy first draft and then polish it over multiple rounds of editing. Don’t waste your time trying to write a perfect research paper in one go. Instead, take the pressure off yourself by adopting an iterative approach.

Secondly , it’s important to always lean towards critical writing , rather than descriptive writing. What does this mean? Well, at the simplest level, descriptive writing focuses on the “ what ”, while critical writing digs into the “ so what ” – in other words, the implications. If you’re not familiar with these two types of writing, don’t worry! You can find a plain-language explanation here.

Last but not least, you’ll need to get your referencing right. Specifically, you’ll need to provide credible, correctly formatted citations for the statements you make. We see students making referencing mistakes all the time and it costs them dearly. The good news is that you can easily avoid this by using a simple reference manager . If you don’t have one, check out our video about Mendeley, an easy (and free) reference management tool that you can start using today.

Recap: Key Takeaways

We’ve covered a lot of ground here. To recap, the three steps to writing a high-quality research paper are:

• To choose a research question and review the literature
• To plan your paper structure and draft an outline
• To take an iterative approach to writing, focusing on critical writing and strong referencing

Remember, this is just a b ig-picture overview of the research paper development process and there’s a lot more nuance to unpack. So, be sure to grab a copy of our free research paper template to learn more about how to write a research paper.

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How to Write Data Analysis Reports in 9 Easy Steps

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Enjoy reading this blog post written by our experts or partners.

If you want to see what Databox can do for you, click here .

Imagine a bunch of bricks. They don’t have a purpose until you put them together into a house, do they?

In business intelligence, data is your building material, and a quality data analysis report is what you want to see as the result.

But if you’ve ever tried to use the collected data and assemble it into an insightful report, you know it’s not an easy job to do. Data is supposed to tell a story about your performance, but there’s a long way from unprocessed, raw data to a meaningful narrative that you can use to create an actionable plan for making steady progress towards your goals.

This article will help you improve the quality of your data analysis reports and build them effortlessly and fast. Let’s jump right in.

What Is a Data Analysis Report?

Why is data analysis reporting important, how to write a data analysis report 9 simple steps, data analysis report examples.

A data analysis report is a type of business report in which you present quantitative and qualitative data to evaluate your strategies and performance. Based on this data, you give recommendations for further steps and business decisions while using the data as evidence that backs up your evaluation.

Today, data analysis is one of the most important elements of business intelligence strategies as companies have realized the potential of having data-driven insights at hand to help them make data-driven decisions.

Just like you’ll look at your car’s dashboard if something’s wrong, you’ll pull your data to see what’s causing drops in website traffic, conversions, or sales – or any other business metric you may be following. This unprocessed data still doesn’t give you a diagnosis – it’s the first step towards a quality analysis. Once you’ve extracted and organized your data, it’s important to use graphs and charts to visualize it and make it easier to draw conclusions.

Once you add meaning to your data and create suggestions based on it, you have a data analysis report.

A vital detail everyone should know about data analysis reports is their accessibility for everyone in your team, and the ability to innovate. Your analysis report will contain your vital KPIs, so you can see where you’re reaching your targets and achieving goals, and where you need to speed up your activities or optimize your strategy. If you can uncover trends or patterns in your data, you can use it to innovate and stand out by offering even more valuable content, services, or products to your audience.

Data analysis is vital for companies for several reasons.

A reliable source of information

Trusting your intuition is fine, but relying on data is safer. When you can base your action plan on data that clearly shows that something is working or failing, you won’t only justify your decisions in front of the management, clients, or investors, but you’ll also be sure that you’ve taken appropriate steps to fix an issue or seize an important opportunity.

A better understanding of your business

According to Databox’s State of Business Reporting , most companies stated that regular monitoring and reporting improved progress monitoring, increased team effectiveness, allowed them to identify trends more easily, and improved financial performance. Data analysis makes it easier to understand your business as a whole, and each aspect individually. You can see how different departments analyze their workflow and how each step impacts their results in the end, by following their KPIs over time. Then, you can easily conclude what your business needs to grow – to boost your sales strategy, optimize your finances, or up your SEO game, for example.

An additional way to understand your business better is to compare your most important metrics and KPIs against companies that are just like yours. With Databox Benchmarks , you will need only one spot to see how all of your teams stack up against your peers and competitors.

Instantly and Anonymously Benchmark Your Company’s Performance Against Others Just Like You

If you ever asked yourself:

• How does our marketing stack up against our competitors?
• Are our salespeople as productive as reps from similar companies?
• Are our profit margins as high as our peers?

Databox Benchmark Groups can finally help you answer these questions and discover how your company measures up against similar companies based on your KPIs.

When you join Benchmark Groups, you will:

• Get instant, up-to-date data on how your company stacks up against similar companies based on the metrics most important to you. Explore benchmarks for dozens of metrics, built on anonymized data from thousands of companies and get a full 360° view of your company’s KPIs across sales, marketing, finance, and more.
• Understand where your business excels and where you may be falling behind so you can shift to what will make the biggest impact. Leverage industry insights to set more effective, competitive business strategies. Explore where exactly you have room for growth within your business based on objective market data.
• Keep your clients happy by using data to back up your expertise. Show your clients where you’re helping them overperform against similar companies. Use the data to show prospects where they really are… and the potential of where they could be.
• Get a valuable asset for improving yearly and quarterly planning . Get valuable insights into areas that need more work. Gain more context for strategic planning.

The best part?

• Benchmark Groups are free to access.
• The data is 100% anonymized. No other company will be able to see your performance, and you won’t be able to see the performance of individual companies either.

When it comes to showing you how your performance compares to others, here is what it might look like for the metric Average Session Duration:

And here is an example of an open group you could join:

And this is just a fraction of what you’ll get. With Databox Benchmarks, you will need only one spot to see how all of your teams stack up — marketing, sales, customer service, product development, finance, and more. 

• Choose criteria so that the Benchmark is calculated using only companies like yours
• Narrow the benchmark sample using criteria that describe your company
• Display benchmarks right on your Databox dashboards

Sounds like something you want to try out? Join a Databox Benchmark Group today!

It makes data accessible to everyone

Data doesn’t represent a magical creature reserved for data scientists only anymore. Now that you have streamlined and easy-to-follow data visualizations and tools that automatically show the latest figures, you can include everyone in the decision-making process as they’ll understand what means what in the charts and tables. The data may be complex, but it becomes easy to read when combined with proper illustrations. And when your teams gain such useful and accessible insight, they will feel motivated to act on it immediately.

Better collaboration

Data analysis reports help teams collaborate better, as well. You can apply the SMART technique to your KPIs and goals, because your KPIs become assignable. When they’re easy to interpret for your whole team, you can assign each person with one or multiple KPIs that they’ll be in charge of. That means taking a lot off a team leader’s plate so they can focus more on making other improvements in the business. At the same time, removing inaccurate data from your day-to-day operations will improve friction between different departments, like marketing and sales, for instance.

More productivity

You can also expect increased productivity, since you’ll be saving time you’d otherwise spend on waiting for specialists to translate data for other departments, etc. This means your internal procedures will also be on a top level.

Want to give value with your data analysis report? It’s critical to master the skill of writing a quality data analytics report. Want to know how to report on data efficiently? We’ll share our secret in the following section.

• Start with an Outline
• Make a Selection of Vital KPIs
• Pick the Right Charts for Appealing Design
• Use a Narrative
• Organize the Information
• Include a Summary
• Careful with Your Recommendations
• Double-Check Everything
• Use Interactive Dashboards

1. Start with an Outline

If you start writing without having a clear idea of what your data analysis report is going to include, it may get messy. Important insights may slip through your fingers, and you may stray away too far from the main topic. To avoid this, start the report by writing an outline first. Plan the structure and contents of each section first to make sure you’ve covered everything, and only then start crafting the report.

2. Make a Selection of Vital KPIs

Don’t overwhelm the audience by including every single metric there is. You can discuss your whole dashboard in a meeting with your team, but if you’re creating data analytics reports or marketing reports for other departments or the executives, it’s best to focus on the most relevant KPIs that demonstrate the data important for the overall business performance.

PRO TIP: How Well Are Your Marketing KPIs Performing?

Like most marketers and marketing managers, you want to know how well your efforts are translating into results each month. How much traffic and new contact conversions do you get? How many new contacts do you get from organic sessions? How are your email campaigns performing? How well are your landing pages converting? You might have to scramble to put all of this together in a single report, but now you can have it all at your fingertips in a single Databox dashboard.

Our Marketing Overview Dashboard includes data from Google Analytics 4 and HubSpot Marketing with key performance metrics like:

• Sessions . The number of sessions can tell you how many times people are returning to your website. Obviously, the higher the better.
• New Contacts from Sessions . How well is your campaign driving new contacts and customers?
• Marketing Performance KPIs . Tracking the number of MQLs, SQLs, New Contacts and similar will help you identify how your marketing efforts contribute to sales.
• Email Performance . Measure the success of your email campaigns from HubSpot. Keep an eye on your most important email marketing metrics such as number of sent emails, number of opened emails, open rate, email click-through rate, and more.
• Blog Posts and Landing Pages . How many people have viewed your blog recently? How well are your landing pages performing?

Now you can benefit from the experience of our Google Analytics and HubSpot Marketing experts, who have put together a plug-and-play Databox template that contains all the essential metrics for monitoring your leads. It’s simple to implement and start using as a standalone dashboard or in marketing reports, and best of all, it’s free!

You can easily set it up in just a few clicks – no coding required.

To set up the dashboard, follow these 3 simple steps:

Step 1: Get the template 

Step 2: Connect your HubSpot and Google Analytics 4 accounts with Databox. 

Step 3: Watch your dashboard populate in seconds.

3. Pick the Right Charts for Appealing Design

If you’re showing historical data – for instance, how you’ve performed now compared to last month – it’s best to use timelines or graphs. For other data, pie charts or tables may be more suitable. Make sure you use the right data visualization to display your data accurately and in an easy-to-understand manner.

4. Use a Narrative

Do you work on analytics and reporting ? Just exporting your data into a spreadsheet doesn’t qualify as either of them. The fact that you’re dealing with data may sound too technical, but actually, your report should tell a story about your performance. What happened on a specific day? Did your organic traffic increase or suddenly drop? Why? And more. There are a lot of questions to answer and you can put all the responses together in a coherent, understandable narrative.

5. Organize the Information

Before you start writing or building your dashboard, choose how you’re going to organize your data. Are you going to talk about the most relevant and general ones first? It may be the best way to start the report – the best practices typically involve starting with more general information and then diving into details if necessary.

6. Include a Summary

Some people in your audience won’t have the time to read the whole report, but they’ll want to know about your findings. Besides, a summary at the beginning of your data analytics report will help the reader get familiar with the topic and the goal of the report. And a quick note: although the summary should be placed at the beginning, you usually write it when you’re done with the report. When you have the whole picture, it’s easier to extract the key points that you’ll include in the summary.

7. Careful with Your Recommendations

Your communication skills may be critical in data analytics reports. Know that some of the results probably won’t be satisfactory, which means that someone’s strategy failed. Make sure you’re objective in your recommendations and that you’re not looking for someone to blame. Don’t criticize, but give suggestions on how things can be improved. Being solution-oriented is much more important and helpful for the business.

8. Double-Check Everything

The whole point of using data analytics tools and data, in general, is to achieve as much accuracy as possible. Avoid manual mistakes by proofreading your report when you finish, and if possible, give it to another person so they can confirm everything’s in place.

9. Use Interactive Dashboards

Using the right tools is just as important as the contents of your data analysis. The way you present it can make or break a good report, regardless of how valuable the data is. That said, choose a great reporting tool that can automatically update your data and display it in a visually appealing manner. Make sure it offers streamlined interactive dashboards that you can also customize depending on the purpose of the report.

To wrap up the guide, we decided to share nine excellent examples of what awesome data analysis reports can look like. You’ll learn what metrics you should include and how to organize them in logical sections to make your report beautiful and effective.

• Marketing Data Analysis Report Example

SEO Data Analysis Report Example

Sales data analysis report example.

• Customer Support Data Analysis Report Example

Help Desk Data Analysis Report Example

Ecommerce data analysis report example, project management data analysis report example, social media data analysis report example, financial kpi data analysis report example, marketing data report example.

If you need an intuitive dashboard that allows you to track your website performance effortlessly and monitor all the relevant metrics such as website sessions, pageviews, or CTA engagement, you’ll love this free HubSpot Marketing Website Overview dashboard template .

Tracking the performance of your SEO efforts is important. You can easily monitor relevant SEO KPIs like clicks by page, engaged sessions, or views by session medium by downloading this Google Organic SEO Dashboard .

How successful is your sales team? It’s easy to analyze their performance and predict future growth if you choose this HubSpot CRM Sales Analytics Overview dashboard template and track metrics such as average time to close the deal, new deals amount, or average revenue per new client.

Customer Support Analysis Data Report Example

Customer support is one of the essential factors that impact your business growth. You can use this streamlined, customizable Customer Success dashboard template . In a single dashboard, you can monitor metrics such as customer satisfaction score, new MRR, or time to first response time.

Other than being free and intuitive, this HelpScout for Customer Support dashboard template is also customizable and enables you to track the most vital metrics that indicate your customer support agents’ performance: handle time, happiness score, interactions per resolution, and more.

Is your online store improving or failing? You can easily collect relevant data about your store and monitor the most important metrics like total sales, orders placed, and new customers by downloading this WooCommerce Shop Overview dashboard template .

Does your IT department need feedback on their project management performance? Download this Jira dashboard template to track vital metrics such as issues created or resolved, issues by status, etc. Jira enables you to gain valuable insights into your teams’ productivity.

Need to know if your social media strategy is successful? You can find that out by using this easy-to-understand Social Media Awareness & Engagement dashboard template . Here you can monitor and analyze metrics like sessions by social source, track the number of likes and followers, and measure the traffic from each source.

Tracking your finances is critical for keeping your business profitable. If you want to monitor metrics such as the number of open invoices, open deals amount by stage by pipeline, or closed-won deals, use this free QuickBooks + HubSpot CRM Financial Performance dashboard template .

Rely on Accurate Data with Databox

“I don’t have time to build custom reports from scratch.”

“It takes too long and becomes daunting very soon.”

“I’m not sure how to organize the data to make it effective and prove the value of my work.”

Does this sound like you?

Well, it’s something we all said at some point – creating data analytics reports can be time-consuming and tiring. And you’re still not sure if the report is compelling and understandable enough when you’re done.

That’s why we decided to create Databox dashboards – a world-class solution for saving your money and time. We build streamlined and easy-to-follow dashboards that include all the metrics that you may need and allow you to create custom ones if necessary. That way, you can use templates and adjust them to any new project or client without having to build a report from scratch.

You can skip the setup and get your first dashboard for free in just 24 hours, with our fantastic customer support team on the line to assist you with the metrics you should track and the structure you should use.

Enjoy crafting brilliant data analysis reports that will improve your business – it’s never been faster and more effortless. Sign up today and get your free dashboard in no time.

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A Message From Our CEO

At Databox, we’re obsessed with helping companies more easily monitor, analyze, and report their results. Whether it’s the resources we put into building and maintaining integrations with 100+ popular marketing tools, enabling customizability of charts, dashboards, and reports, or building functionality to make analysis, benchmarking, and forecasting easier, we’re constantly trying to find ways to help our customers save time and deliver better results.

Stefana Zarić is a freelance writer & content marketer. Other than writing for SaaS and fintech clients, she educates future writers who want to build a career in marketing. When not working, Stefana loves to read books, play with her kid, travel, and dance.

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Analysis is a type of primary research that involves finding and interpreting patterns in data, classifying those patterns, and generalizing the results. It is useful when looking at actions, events, or occurrences in different texts, media, or publications. Analysis can usually be done without considering most of the ethical issues discussed in the overview, as you are not working with people but rather publicly accessible documents. Analysis can be done on new documents or performed on raw data that you yourself have collected.

Here are several examples of analysis:

• Recording commercials on three major television networks and analyzing race and gender within the commercials to discover some conclusion.
• Analyzing the historical trends in public laws by looking at the records at a local courthouse.
• Analyzing topics of discussion in chat rooms for patterns based on gender and age.

Analysis research involves several steps:

• Finding and collecting documents.
• Specifying criteria or patterns that you are looking for.
• Analyzing documents for patterns, noting number of occurrences or other factors.

Advanced essay writing guides for everyone

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Helpful Tips on Composing a Research Paper Data Analysis Section

If you are given a research paper assignment, you should create a list of tasks to be done and try to stick to your working schedule. It is recommended that you complete your research and then start writing your work. One of the important steps is to prepare your data analysis section. However, that step is vital as it aims to explain how the data will be described in the results section. Use the following helpful tips to complete that section without a hitch.

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How to Compose a Data Analysis Section for Your Research Paper

Usually, a data analysis section is provided right after the methods and approaches used. There, you should explain how you organized your data, what statistical tests were applied, and how you evaluated the obtained results. Follow these simple tips to compose a strong piece of writing:

• Avoid analyzing your results in the data analysis section.
• Indicate whether your research is quantitative or qualitative.
• Provide your main research questions and the analysis methods that were applied to answer them.
• Report what software you used to gather and analyze your data.
• List the data sources, including electronic archives and online reports of different institutions.
• Explain how the data were summarized and what measures of variability you have used.
• Remember to mention the data transformations if any, including data normalizing.
• Make sure that you included the full name of statistical tests used.
• Describe graphical techniques used to analyze the raw data and the results.

Where to Find the Necessary Assistance If You Get Stuck

Research paper writing is hard, so if you get stuck, do not wait for enlightenment and start searching for some assistance. It is a good idea to consult a statistics expert if you have a large amount of data and have no idea on how to summarize it. Your academic advisor may suggest you where to find a statistician to ask your questions.

Another great help option is getting a sample of a data analysis section. At the school’s library, you can find sample research papers written by your fellow students, get a few works, and study how the students analyzed data. Pay special attention to the word choices and the structure of the writing.

If you decide to follow a section template, you should be careful and keep your professor’s instructions in mind. For example, you may be asked to place all the page-long data tables in the appendices or build graphs instead of providing tables.

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What is Data Analytics? A Simple Breakdown

By Brianna Flavin on 05/02/2024

Da ta is everywhere these days. Businesses want to make data-driven decisions. And pretty much every industry wants to utilize the new capacities we have to gather information and turn it into better results, more efficiency and more profit.

But that much information, that much raw data is basically useless without data analytics. It's like dumping an ocean on someone and asking them to pull a dose of cough syrup out of it.

Data analysis allows you to navigate an ocean of information, identify trends, pull select insights, and most importantly—learn.

So, what is data analytics?

Data analytics is the action of collecting, organizing and finding ways to use huge amounts of information. It's simple to say. But data analysis is so versatile and can apply in so many different ways that data analytics isn't a job so much as an entire industry of jobs.

There's honestly so much going on in data analytics that it's overwhelming to just get a basic grip on the field. But when you do dive in, you’ll see how exciting the industry can be.

“Data is lit AF,” laughs Nathan Keysser, senior data analyst at BI Worldwide . “The ability to take large amounts of meaningless data and turn it into meaning, into something useful and interesting... it really satisfies that problem-solving itch.”

This is a really good field for people who enjoy puzzles, according to Keysser. If that’s you, keep reading. See how it works.

What does data analysis look like?

If you start from square one, there's a pretty straightforward process to data analytics. You can break all of these steps down into more steps if you want to get really detailed. But for a general understanding of how to analyze data, this is what you need to know.

Step one: Data collection

Data analysis can't happen without data collection—so that's the first step in the data analysis process. Companies, data analysts and data scientists need to figure out how to source information relevant to their needs.

This could look like your favorite shoe company sending out an automated survey after every purchase and collecting the results. Or it could be web cookies that record information about who visits a webpage, what they click on, how long they linger, etc... Data mining is a more-automated aspect of data science that's all about how to gather mass amounts of raw data for useful analysis.

Hospitals might use data from their electronic health records, law firms might analyze historical data on case studies, and environmental organizations might collect data about the soil, wind or solar conditions in hundreds of locations. Basically, you need a way to compile useful information before you can make use of it.

Step two: Data processing

Once you have lots of information, you have to organize it into a format you can easily sort or play around with. This might mean digitizing paper records, porting information from assorted files into one spreadsheet or hundreds of other methods.

This part of analyzing raw data is usually very time-consuming because you not only have to do a lot of manual, individual work—you also have to consider very carefully which aspects of information might be relevant. Otherwise, you’ll wind up doing the process all over again.

People used to ask is data analytics important enough to really splash out on expensive data analytics tools? But as people understand the power of data, it’s often more of a priority area. For a lot of companies, technology that can automate this time-consuming process and assist with data management might well pay for itself before too long.

Step three: Data analysis

Now we've reached the name of the game! After data is stored somewhere secure, accessible and sort-able, you're ready to actually analyze. How you analyze will vary a lot based on your organization, the needs of the moment and even how you prefer to work.

“Generally, I know what I want to find,” Keysser says. “But I frequently don't know how the data is structured, and I have to do a fair amount of problem solving to figure out exactly how I want to pull the data.” Keysser adds that since data analytics sounds so technical, many people don't realize how creative the work can be.

Data scientists and data analysts can approach analysis from many different angles. You could try to find the answer to a specific question. Ex: Will buyers in Illinois spend 15$ on this type of shirt?

Or you could pinpoint something and try to pull as much data related to that as possible. Ex: What is everything we know about people who've purchased a shirt from this website.

The options in data analytics are as wide as your imagination here. But there are defined types of data analytics that can give you some ideas.

Descriptive analytics

This type of analysis can be pretty simple. Descriptive analytics is all about answering the what, when, how types of questions in a given data set. For example, descriptive analytics could mean doing a simple statistical analysis to find the median number in a huge group of statistics.

Or to be more specific, a telehealth provider could keep track of when their clients with young children schedule consultations for strep throat. Descriptive analysis could help them track the months of the year with the most consultations over the last decade and isolate the peak times for strep throat consultations in children.

Descriptive analytics is a good first step to perform data analysis because it can help you identify errors like typos in your data, as well as find commonalities you may not have initially considered.

Diagnostic analytics

Diagnostic analytics goes a little deeper, into the "why" questions we can bring to data. This can range from technical questions (why does our website keep crashing) to more social/emotional questions (why are employees leaving our company).

For example, a company might wonder why they are experiencing higher turnover in their employees. Collecting information from exit interviews, they could use diagnostic analytics to pinpoint the problem areas employees most commonly cite and find ways to weigh the value of each reason.

Predictive analytics

Predictive analytics does exactly what it sounds like—predict things. If you analyze data on every tornado to ever hit the U.S., for example, you can use predictive analytics to help you determine when tornadoes might occur again in the future.

Since having some insight into future trends is highly-profitable for pretty much everyone, predictive analysis is an excellent way to draw valuable insights.

Prescriptive analytics

Prescriptive analytics is all about using data to choose the next course of action. It relies on predictive analytics, but then creates a recommended next step . This can be very helpful when there are lots of predictive variables involved. In the tornado example, cities could use a combination of data on when tornadoes are most likely to occur and what weather conditions will likely occur first and make a prescriptive decision about when they should run tornado warning sirens.

This type of data analytics often involves algorithms, machine learning or automation to help deal with all the raw data. Think about the way a credit card company might monitor transactions—that's way too much info for data analysts to wrangle. So, the company might create a prescriptive analytics algorithm to pay attention to spending patterns, flag anomalies and recommend a fraud or theft alert.

Then, if a customer buys something highly unusual, they will automatically receive a fraud alert and a hold on their card to limit potential damage if the card were stolen.

Step four: Data interpretation

As you can see from the types of data analytics, the whole point of all this is to draw meaningful conclusions from data.

After data is analyzed, those insights are now available. But it still takes some work to explain and translate. This is where data visualization and data modeling come in! You could have the highest quality data analysis in the world, but if people can't understand the results of your analysis, that hard work won't go anywhere.

One of the most important data analytics techniques is to create visual, clear ways to explain the data analytics process (and the results and recommendations) to people outside the field.

How do organizations use data analytics?

You're probably getting the picture already. An organization could use data analytics to answer questions about....

• What they are doing (who are our customers or clients, how much do we produce, where are we most successful)
• Why something is happening (why isn't this working, why is this working)
• What might happen in the future (when will we be busiest, how often is something likely to happen)
• What should happen next (at what point should we react, what is our best course of action when ___ happens)

It's hard to overstate the potential in all that.

It goes way beyond making more money too. Data analytics can help with healthcare, education, governing, economics, politics and all of the “this is too big to wrap my head around” elements of society.

“Knowledge is power,” Keysser says. “I know it's kind of cheesy to say that, but the more we can find truth in data, the better choices we can make about our future.”

The people who puzzle it out

Data analysts are one type of professional in the field of data analytics. If you like to solve problems, you might be curious to learn a little more about what it's actually like to work as a data analyst. All of this macro-level information is great for a general overview—but it gets way more interesting when you zoom in on the actual job.

Get those details at What Does a Data Analyst Do? Exploring the Day-to-Day of This Tech Career .

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8 Tips for Writing Dissertation Data Analysis to Elevate Your Scholarship

Dissertation data analysis is the process by which researchers interpret findings to drive useful insights. In this way, they can divide the big chunks of results into smaller fragments that make more sense to the reader. It helps to draw evidence-based conclusions and provides the basis for making claims and recommendations. 

Writing up such data analysis is not a skill that someone is born with. You need guidance or practice to meet the standards. If you want to elevate your research skills by writing an exceptional data analysis for your thesis, then consider the guidance provided in this article. Here are the top tips that can help you while you are describing your approach to draw results. If a deeper understanding of the process stresses its complexity, then asking for dissertation help online can take away this academic anxiety. The experts are well aware of the patterns, trends and relationships within the data. So, the write-up can stand out as unique due to the exceptional organizations involved. 

Keep reading to learn the top tips for writing a data analysis chapter in your dissertation. This way, you can stand out in the increasing competition by showcasing your research skills to readers. 

What Is a Data Analysis Dissertation?

Dissertation data analysis is the systematic examination, organization, or interpretation of data from your research findings. The intent is to define patterns to provide a deeper understanding of the qualitative or quantitative results. It addresses the intriguing research question in data analysis dissertations. 

How Do You Write Data Analysis Dissertation? 8 Tips

In presenting data analysis for dissertation, you need to define the statistical test conducted for the process. You will also crack the assumptions underlying these tests, the sample size and how to conclude. Here we will demystify the process of writing the data analysis of your dissertation. These are the expert tips that can assist you in addition to a basic understanding of the process. 

1. The Data Must Be Relevant 

Do not blindly follow all the data collected in the data analysis and findings dissertation. For the data analysis, you will check which part of the data has a close resemblance with your research objectives. The inclusion of irrelevant data will showcase the incoherence of the findings. Ultimately, it can raise questions about your research expertise. 

2. Use Appropriate Methods 

You must use appropriate dissertation data analysis methods to fulfill your aims. These methods need to be justified by providing proper reasoning. Remember that the reader should not feel that you chose your method haphazardly. Rather write in the data analysis chapter dissertation how you find these approaches meaningful. 

3. Make Use of Info Graphics 

It can be difficult to cover large amounts of information within a given word count limit in dissertation data analysis. So, you should use all the possible means to present the data that you collected. Consider using diagrams, graphs, charts, and formulas to provide a unique advantage to the paper. If it seems difficult for you to present your findings in a presentational way, then availing yourself of dissertation writing services UK can be one of the best possible options. These experts can better illustrate the data in a succinate manner that no longer bores the reader with long phrases. 

4. Explain Thoroughly 

Most of the students make this mistake when conducting qualitative data analysis in dissertations. They think that writing a quote is enough for the reader to understand. Remember, a qualitative data analysis dissertation never speaks for itself. You should demonstrate all the areas with a critical perspective so that no ambiguity remains for the reader.  

5. Write Some Data in the Appendix  

It is always scary for the writer to remove long information from dissertation data analysis if it seems irrelevant after proofreading. If you feel unwilling to cut down the long data that you spend hours collecting, then simply shift it to the appendix. Only the most relevant snippet of the information should be part of the dissertation itself. 

The National Academies Press presents a sample of the appendix where questionnaires are used to illustrate the data. 

6. Consider Various Interpretations 

You will cover this part in the discussion chapter of the data collection and analysis dissertation. Here demonstrate the trends, patterns and themes within the data. Highlight the strengths as well as limitations so that there must not be any bias in the findings. 

7. Relate With Previous Literature 

It is advisable to compare your dissertation data analysis with other academia to highlight the points of agreement and differences. Make sure that your findings are consistent with the previous data or does it create some sort of controversy in the literature? It is important to create this link explicitly. 

8. Seek Professional Help 

Writing a dissertation itself is not a difficult process. A researcher who can go through the complex research process can also perform well in writing those findings. However, the critical analysis of these findings may be challenging. Dissertation data analysis is more than presenting what has been found out after experimentation. 

If including a critical perspective in your writing is difficult for you, ask a dissertation writer to conduct these critiques for you. These experts offer customized assistance tailored to the specific needs of the researcher. 

How Do You Write a Statistical Analysis for A Dissertation?

To write the statistical dissertation data analysis, the below five-step guide can direct you to its effective implementation. 

• Describe your hypothesis and research design.
• Data collection. 
• Summarise your findings and give a descriptive explanation. 
• Test your hypothesis. 
• Interpret findings. 

Data Analysis Dissertation Examples

The Bridge Point Education describes the whole dissertation writing process. The dissertation data analysis chapter can be helpful for students struggling with the complexity of it. All the related details, such as what to discuss here and how to align with the research design, are mentioned in side notes as,  

Conclusion 

The dissertation data analysis is one of the most important chapters of your dissertation. It involves the critical evaluation of your results. Consequently, you will tell the reader how the specific methodology and the draw conclusions are sound to be used in your research design. 

The data analysis section of a dissertation is not only essential but also difficult. Many students struggle here as they do not know how to identify the related pattern and organize their thoughts. That is why we presented expert tips to assist the process smoothly. It involves using relevant data, an appropriate approach, a thorough explanation, infographics, an appendix, or relating your findings to previous literature. All of this means contributing to the quality of your paper.

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