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Writing the Data Analysis Chapter(s): Results and Evidence

Posted by Rene Tetzner | Oct 19, 2021 | PhD Success | 0 |

4.4 Writing the Data Analysis Chapter(s): Results and Evidence

Unlike the introduction, literature review and methodology chapter(s), your results chapter(s) will need to be written for the first time as you draft your thesis even if you submitted a proposal, though this part of your thesis will certainly build upon the preceding chapters. You should have carefully recorded and collected the data (test results, participant responses, computer print outs, observations, transcriptions, notes of various kinds etc.) from your research as you conducted it, so now is the time to review, organise and analyse the data. If your study is quantitative in nature, make sure that you know what all the numbers mean and that you consider them in direct relation to the topic, problem or phenomenon you are investigating, and especially in relation to your research questions and hypotheses. You may find that you require the services of a statistician to help make sense of the data, in which case, obtaining that help sooner rather than later is advisable, because you need to understand your results thoroughly before you can write about them. If, on the other hand, your study is qualitative, you will need to read through the data you have collected several times to become familiar with them both as a whole and in detail so that you can establish important themes, patterns and categories. Remember that ‘qualitative analysis is a creative process and requires thoughtful judgments about what is significant and meaningful in the data’ (Roberts, 2010, p.174; see also Miles & Huberman, 1994) – judgements that often need to be made before the findings can be effectively analysed and presented. If you are combining methodologies in your research, you will also need to consider relationships between the results obtained from the different methods, integrating all the data you have obtained and discovering how the results of one approach support or correlate with the results of another. Ideally, you will have taken careful notes recording your initial thoughts and analyses about the sources you consulted and the results and evidence provided by particular methods and instruments as you put them into practice (as suggested in Sections 2.1.2 and 2.1.4), as these will prove helpful while you consider how best to present your results in your thesis.

Although the ways in which to present and organise the results of doctoral research differ markedly depending on the nature of the study and its findings, as on author and committee preferences and university and department guidelines, there are several basic principles that apply to virtually all theses. First and foremost is the need to present the results of your research both clearly and concisely, and in as objective and factual a manner as possible. There will be time and space to elaborate and interpret your results and speculate on their significance and implications in the final discussion chapter(s) of your thesis, but, generally speaking, such reflection on the meaning of the results should be entirely separate from the factual report of your research findings. There are exceptions, of course, and some candidates, supervisors and departments may prefer the factual presentation and interpretive discussion of results to be blended, just as some thesis topics may demand such treatment, but this is rare and best avoided unless there are persuasive reasons to avoid separating the facts from your thoughts about them. If you do find that you need to blend facts and interpretation in reporting your results, make sure that your language leaves no doubt about the line between the two: words such as ‘seems,’ ‘appears,’ ‘may,’ ‘might,’ probably’ and the like will effectively distinguish analytical speculation from more factual reporting (see also Section 4.5).

You need not dedicate much space in this part of the thesis to the methods you used to arrive at your results because these have already been described in your methodology chapter(s), but they can certainly be revisited briefly to clarify or lend structure to your report. Results are most often presented in a straightforward narrative form which is often supplemented by tables and perhaps by figures such as graphs, charts and maps. An effective approach is to decide immediately which information would be best included in tables and figures, and then to prepare those tables and figures before you begin writing the text for the chapter (see Section 4.4.1 on designing effective tables and figures). Arranging your data into the visually immediate formats provided by tables and figures can, for one, produce interesting surprises by enabling you to see trends and details that you may not have noticed previously, and writing the report of your results will prove easier when you have the tables and figures to work with just as your readers ultimately will. In addition, while the text of the results chapter(s) should certainly highlight the most notable data included in tables and figures, it is essential not to repeat information unnecessarily, so writing with the tables and figures already constructed will help you keep repetition to a minimum. Finally, writing about the tables and figures you create will help you test their clarity and effectiveness for your readers, and you can make any necessary adjustments to the tables and figures as you work. Be sure to refer to each table and figure by number in your text and to make it absolutely clear what you want your readers to see or understand in the table or figure (e.g., ‘see Table 1 for the scores’ and ‘Figure 2 shows this relationship’).

Beyond combining textual narration with the data presented in tables and figures, you will need to organise your report of the results in a manner best suited to the material. You may choose to arrange the presentation of your results chronologically or in a hierarchical order that represents their importance; you might subdivide your results into sections (or separate chapters if there is a great deal of information to accommodate) focussing on the findings of different kinds of methodology (quantitative versus qualitative, for instance) or of different tests, trials, surveys, reviews, case studies and so on; or you may want to create sections (or chapters) focussing on specific themes, patterns or categories or on your research questions and/or hypotheses. The last approach allows you to cluster results that relate to a particular question or hypothesis into a single section and can be particularly useful because it provides cohesion for the thesis as a whole and forces you to focus closely on the issues central to the topic, problem or phenomenon you are investigating. You will, for instance, be able to refer back to the questions and hypotheses presented in your introduction (see Section 3.1), to answer the questions and confirm or dismiss the hypotheses and to anticipate in relation to those questions and hypotheses the discussion and interpretation of your findings that will appear in the next part of the thesis (see Section 4.5). Less effective is an approach that organises the presentation of results according to the items of a survey or questionnaire, because these lend the structure of the instrument used to the results instead of connecting those results directly to the aims, themes and argument of your thesis, but such an organisation can certainly be an important early step in your analysis of the findings and might even be valid for the final thesis if, for instance, your work focuses on developing the instrument involved.

The results generated by doctoral research are unique, and this book cannot hope to outline all the possible approaches for presenting the data and analyses that constitute research results, but it is essential that you devote considerable thought and special care to the way in which you structure the report of your results (Section 6.1 on headings may prove helpful). Whatever structure you choose should accurately reflect the nature of your results and highlight their most important and interesting trends, and it should also effectively allow you (in the next part of the thesis) to discuss and speculate upon your findings in ways that will test the premises of your study, work well in the overall argument of your thesis and lead to significant implications for your research. Regardless of how you organise the main body of your results chapter(s), however, you should include a final paragraph (or more than one paragraph if necessary) that briefly summarises and explains the key results and also guides the reader on to the discussion and interpretation of those results in the following chapter(s).

Why PhD Success?

To Graduate Successfully

This article is part of a book called "PhD Success" which focuses on the writing process of a phd thesis, with its aim being to provide sound practices and principles for reporting and formatting in text the methods, results and discussion of even the most innovative and unique research in ways that are clear, correct, professional and persuasive.

The assumption of the book is that the doctoral candidate reading it is both eager to write and more than capable of doing so, but nonetheless requires information and guidance on exactly what he or she should be writing and how best to approach the task. The basic components of a doctoral thesis are outlined and described, as are the elements of complete and accurate scholarly references, and detailed descriptions of writing practices are clarified through the use of numerous examples.

The basic components of a doctoral thesis are outlined and described, as are the elements of complete and accurate scholarly references, and detailed descriptions of writing practices are clarified through the use of numerous examples. PhD Success provides guidance for students familiar with English and the procedures of English universities, but it also acknowledges that many theses in the English language are now written by candidates whose first language is not English, so it carefully explains the scholarly styles, conventions and standards expected of a successful doctoral thesis in the English language.

Individual chapters of this book address reflective and critical writing early in the thesis process; working successfully with thesis supervisors and benefiting from commentary and criticism; drafting and revising effective thesis chapters and developing an academic or scientific argument; writing and formatting a thesis in clear and correct scholarly English; citing, quoting and documenting sources thoroughly and accurately; and preparing for and excelling in thesis meetings and examinations. 

Completing a doctoral thesis successfully requires long and penetrating thought, intellectual rigour and creativity, original research and sound methods (whether established or innovative), precision in recording detail and a wide-ranging thoroughness, as much perseverance and mental toughness as insight and brilliance, and, no matter how many helpful writing guides are consulted, a great deal of hard work over a significant period of time. Writing a thesis can be an enjoyable as well as a challenging experience, however, and even if it is not always so, the personal and professional rewards of achieving such an enormous goal are considerable, as all doctoral candidates no doubt realise, and will last a great deal longer than any problems that may be encountered during the process.

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Rene Tetzner

Rene Tetzner's blog posts dedicated to academic writing. Although the focus is on How To Write a Doctoral Thesis, many other important aspects of research-based writing, editing and publishing are addressed in helpful detail.

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• How to Write a Results Section | Tips & Examples

How to Write a Results Section | Tips & Examples

Published on August 30, 2022 by Tegan George . Revised on July 18, 2023.

A results section is where you report the main findings of the data collection and analysis you conducted for your thesis or dissertation . You should report all relevant results concisely and objectively, in a logical order. Don’t include subjective interpretations of why you found these results or what they mean—any evaluation should be saved for the discussion section .

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Table of contents

How to write a results section, reporting quantitative research results, reporting qualitative research results, results vs. discussion vs. conclusion, checklist: research results, other interesting articles, frequently asked questions about results sections.

When conducting research, it’s important to report the results of your study prior to discussing your interpretations of it. This gives your reader a clear idea of exactly what you found and keeps the data itself separate from your subjective analysis.

Here are a few best practices:

• Your results should always be written in the past tense.
• While the length of this section depends on how much data you collected and analyzed, it should be written as concisely as possible.
• Only include results that are directly relevant to answering your research questions . Avoid speculative or interpretative words like “appears” or “implies.”
• If you have other results you’d like to include, consider adding them to an appendix or footnotes.
• Always start out with your broadest results first, and then flow into your more granular (but still relevant) ones. Think of it like a shoe store: first discuss the shoes as a whole, then the sneakers, boots, sandals, etc.

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If you conducted quantitative research , you’ll likely be working with the results of some sort of statistical analysis .

Your results section should report the results of any statistical tests you used to compare groups or assess relationships between variables . It should also state whether or not each hypothesis was supported.

The most logical way to structure quantitative results is to frame them around your research questions or hypotheses. For each question or hypothesis, share:

• A reminder of the type of analysis you used (e.g., a two-sample t test or simple linear regression ). A more detailed description of your analysis should go in your methodology section.
• A concise summary of each relevant result, both positive and negative. This can include any relevant descriptive statistics (e.g., means and standard deviations ) as well as inferential statistics (e.g., t scores, degrees of freedom , and p values ). Remember, these numbers are often placed in parentheses.
• A brief statement of how each result relates to the question, or whether the hypothesis was supported. You can briefly mention any results that didn’t fit with your expectations and assumptions, but save any speculation on their meaning or consequences for your discussion  and conclusion.

A note on tables and figures

In quantitative research, it’s often helpful to include visual elements such as graphs, charts, and tables , but only if they are directly relevant to your results. Give these elements clear, descriptive titles and labels so that your reader can easily understand what is being shown. If you want to include any other visual elements that are more tangential in nature, consider adding a figure and table list .

As a rule of thumb:

• Tables are used to communicate exact values, giving a concise overview of various results
• Graphs and charts are used to visualize trends and relationships, giving an at-a-glance illustration of key findings

Don’t forget to also mention any tables and figures you used within the text of your results section. Summarize or elaborate on specific aspects you think your reader should know about rather than merely restating the same numbers already shown.

A two-sample t test was used to test the hypothesis that higher social distance from environmental problems would reduce the intent to donate to environmental organizations, with donation intention (recorded as a score from 1 to 10) as the outcome variable and social distance (categorized as either a low or high level of social distance) as the predictor variable.Social distance was found to be positively correlated with donation intention, t (98) = 12.19, p < .001, with the donation intention of the high social distance group 0.28 points higher, on average, than the low social distance group (see figure 1). This contradicts the initial hypothesis that social distance would decrease donation intention, and in fact suggests a small effect in the opposite direction.

Figure 1: Intention to donate to environmental organizations based on social distance from impact of environmental damage.

In qualitative research , your results might not all be directly related to specific hypotheses. In this case, you can structure your results section around key themes or topics that emerged from your analysis of the data.

For each theme, start with general observations about what the data showed. You can mention:

• Recurring points of agreement or disagreement
• Patterns and trends
• Particularly significant snippets from individual responses

Next, clarify and support these points with direct quotations. Be sure to report any relevant demographic information about participants. Further information (such as full transcripts , if appropriate) can be included in an appendix .

When asked about video games as a form of art, the respondents tended to believe that video games themselves are not an art form, but agreed that creativity is involved in their production. The criteria used to identify artistic video games included design, story, music, and creative teams.One respondent (male, 24) noted a difference in creativity between popular video game genres:

“I think that in role-playing games, there’s more attention to character design, to world design, because the whole story is important and more attention is paid to certain game elements […] so that perhaps you do need bigger teams of creative experts than in an average shooter or something.”

Responses suggest that video game consumers consider some types of games to have more artistic potential than others.

Your results section should objectively report your findings, presenting only brief observations in relation to each question, hypothesis, or theme.

It should not  speculate about the meaning of the results or attempt to answer your main research question . Detailed interpretation of your results is more suitable for your discussion section , while synthesis of your results into an overall answer to your main research question is best left for your conclusion .

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I have completed my data collection and analyzed the results.

I have included all results that are relevant to my research questions.

I have concisely and objectively reported each result, including relevant descriptive statistics and inferential statistics .

I have stated whether each hypothesis was supported or refuted.

I have used tables and figures to illustrate my results where appropriate.

All tables and figures are correctly labelled and referred to in the text.

There is no subjective interpretation or speculation on the meaning of the results.

You've finished writing up your results! Use the other checklists to further improve your thesis.

If you want to know more about AI for academic writing, AI tools, or research bias, make sure to check out some of our other articles with explanations and examples or go directly to our tools!

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The results chapter of a thesis or dissertation presents your research results concisely and objectively.

In quantitative research , for each question or hypothesis , state:

• The type of analysis used
• Relevant results in the form of descriptive and inferential statistics
• Whether or not the alternative hypothesis was supported

In qualitative research , for each question or theme, describe:

• Recurring patterns
• Significant or representative individual responses
• Relevant quotations from the data

Don’t interpret or speculate in the results chapter.

Results are usually written in the past tense , because they are describing the outcome of completed actions.

The results chapter or section simply and objectively reports what you found, without speculating on why you found these results. The discussion interprets the meaning of the results, puts them in context, and explains why they matter.

In qualitative research , results and discussion are sometimes combined. But in quantitative research , it’s considered important to separate the objective results from your interpretation of them.

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11 Tips For Writing a Dissertation Data Analysis

Since the evolution of the fourth industrial revolution – the Digital World; lots of data have surrounded us. There are terabytes of data around us or in data centers that need to be processed and used. The data needs to be appropriately analyzed to process it, and Dissertation data analysis forms its basis. If data analysis is valid and free from errors, the research outcomes will be reliable and lead to a successful dissertation. 

Considering the complexity of many data analysis projects, it becomes challenging to get precise results if analysts are not familiar with data analysis tools and tests properly. The analysis is a time-taking process that starts with collecting valid and relevant data and ends with the demonstration of error-free results.

So, in today’s topic, we will cover the need to analyze data, dissertation data analysis, and mainly the tips for writing an outstanding data analysis dissertation. If you are a doctoral student and plan to perform dissertation data analysis on your data, make sure that you give this article a thorough read for the best tips!

What is Data Analysis in Dissertation?

Dissertation Data Analysis  is the process of understanding, gathering, compiling, and processing a large amount of data. Then identifying common patterns in responses and critically examining facts and figures to find the rationale behind those outcomes.

Even f you have the data collected and compiled in the form of facts and figures, it is not enough for proving your research outcomes. There is still a need to apply dissertation data analysis on your data; to use it in the dissertation. It provides scientific support to the thesis and conclusion of the research.

Data Analysis Tools

There are plenty of indicative tests used to analyze data and infer relevant results for the discussion part. Following are some tests  used to perform analysis of data leading to a scientific conclusion:

11 Most Useful Tips for Dissertation Data Analysis

Doctoral students need to perform dissertation data analysis and then dissertation to receive their degree. Many Ph.D. students find it hard to do dissertation data analysis because they are not trained in it.

1. Dissertation Data Analysis Services

The first tip applies to those students who can afford to look for help with their dissertation data analysis work. It’s a viable option, and it can help with time management and with building the other elements of the dissertation with much detail.

Dissertation Analysis services are professional services that help doctoral students with all the basics of their dissertation work, from planning, research and clarification, methodology, dissertation data analysis and review, literature review, and final powerpoint presentation.

One great reference for dissertation data analysis professional services is Statistics Solutions , they’ve been around for over 22 years helping students succeed in their dissertation work. You can find the link to their website here .

For a proper dissertation data analysis, the student should have a clear understanding and statistical knowledge. Through this knowledge and experience, a student can perform dissertation analysis on their own. 

Following are some helpful tips for writing a splendid dissertation data analysis:

2. Relevance of Collected Data

If the data is irrelevant and not appropriate, you might get distracted from the point of focus. To show the reader that you can critically solve the problem, make sure that you write a theoretical proposition regarding the selection  and analysis of data.

3. Data Analysis

For analysis, it is crucial to use such methods that fit best with the types of data collected and the research objectives. Elaborate on these methods and the ones that justify your data collection methods thoroughly. Make sure to make the reader believe that you did not choose your method randomly. Instead, you arrived at it after critical analysis and prolonged research.

On the other hand,  quantitative analysis  refers to the analysis and interpretation of facts and figures – to build reasoning behind the advent of primary findings. An assessment of the main results and the literature review plays a pivotal role in qualitative and quantitative analysis.

The overall objective of data analysis is to detect patterns and inclinations in data and then present the outcomes implicitly.  It helps in providing a solid foundation for critical conclusions and assisting the researcher to complete the dissertation proposal. 

4. Qualitative Data Analysis

Qualitative data refers to data that does not involve numbers. You are required to carry out an analysis of the data collected through experiments, focus groups, and interviews. This can be a time-taking process because it requires iterative examination and sometimes demanding the application of hermeneutics. Note that using qualitative technique doesn’t only mean generating good outcomes but to unveil more profound knowledge that can be transferrable.

Presenting qualitative data analysis in a dissertation  can also be a challenging task. It contains longer and more detailed responses. Placing such comprehensive data coherently in one chapter of the dissertation can be difficult due to two reasons. Firstly, we cannot figure out clearly which data to include and which one to exclude. Secondly, unlike quantitative data, it becomes problematic to present data in figures and tables. Making information condensed into a visual representation is not possible. As a writer, it is of essence to address both of these challenges.

          Qualitative Data Analysis Methods

Following are the methods used to perform quantitative data analysis. 

•   Deductive Method

This method involves analyzing qualitative data based on an argument that a researcher already defines. It’s a comparatively easy approach to analyze data. It is suitable for the researcher with a fair idea about the responses they are likely to receive from the questionnaires.

•  Inductive Method

In this method, the researcher analyzes the data not based on any predefined rules. It is a time-taking process used by students who have very little knowledge of the research phenomenon.

5. Quantitative Data Analysis

Quantitative data contains facts and figures obtained from scientific research and requires extensive statistical analysis. After collection and analysis, you will be able to conclude. Generic outcomes can be accepted beyond the sample by assuming that it is representative – one of the preliminary checkpoints to carry out in your analysis to a larger group. This method is also referred to as the “scientific method”, gaining its roots from natural sciences.

The Presentation of quantitative data  depends on the domain to which it is being presented. It is beneficial to consider your audience while writing your findings. Quantitative data for  hard sciences  might require numeric inputs and statistics. As for  natural sciences , such comprehensive analysis is not required.

                Quantitative Analysis Methods

Following are some of the methods used to perform quantitative data analysis. 

• Trend analysis:  This corresponds to a statistical analysis approach to look at the trend of quantitative data collected over a considerable period.
• Cross-tabulation:  This method uses a tabula way to draw readings among data sets in research.  
• Conjoint analysis :   Quantitative data analysis method that can collect and analyze advanced measures. These measures provide a thorough vision about purchasing decisions and the most importantly, marked parameters.
• TURF analysis:  This approach assesses the total market reach of a service or product or a mix of both. 
• Gap analysis:  It utilizes the  side-by-side matrix  to portray quantitative data, which captures the difference between the actual and expected performance. 
• Text analysis:  In this method, innovative tools enumerate  open-ended data  into easily understandable data. 

6. Data Presentation Tools

Since large volumes of data need to be represented, it becomes a difficult task to present such an amount of data in coherent ways. To resolve this issue, consider all the available choices you have, such as tables, charts, diagrams, and graphs. 

Tables help in presenting both qualitative and quantitative data concisely. While presenting data, always keep your reader in mind. Anything clear to you may not be apparent to your reader. So, constantly rethink whether your data presentation method is understandable to someone less conversant with your research and findings. If the answer is “No”, you may need to rethink your Presentation. 

7. Include Appendix or Addendum

After presenting a large amount of data, your dissertation analysis part might get messy and look disorganized. Also, you would not be cutting down or excluding the data you spent days and months collecting. To avoid this, you should include an appendix part. 

The data you find hard to arrange within the text, include that in the  appendix part of a dissertation . And place questionnaires, copies of focus groups and interviews, and data sheets in the appendix. On the other hand, one must put the statistical analysis and sayings quoted by interviewees within the dissertation. 

8. Thoroughness of Data

It is a common misconception that the data presented is self-explanatory. Most of the students provide the data and quotes and think that it is enough and explaining everything. It is not sufficient. Rather than just quoting everything, you should analyze and identify which data you will use to approve or disapprove your standpoints. 

Thoroughly demonstrate the ideas and critically analyze each perspective taking care of the points where errors can occur. Always make sure to discuss the anomalies and strengths of your data to add credibility to your research.

9. Discussing Data

Discussion of data involves elaborating the dimensions to classify patterns, themes, and trends in presented data. In addition, to balancing, also take theoretical interpretations into account. Discuss the reliability of your data by assessing their effect and significance. Do not hide the anomalies. While using interviews to discuss the data, make sure you use relevant quotes to develop a strong rationale. 

It also involves answering what you are trying to do with the data and how you have structured your findings. Once you have presented the results, the reader will be looking for interpretation. Hence, it is essential to deliver the understanding as soon as you have submitted your data.

10. Findings and Results

Findings refer to the facts derived after the analysis of collected data. These outcomes should be stated; clearly, their statements should tightly support your objective and provide logical reasoning and scientific backing to your point. This part comprises of majority part of the dissertation. 

In the finding part, you should tell the reader what they are looking for. There should be no suspense for the reader as it would divert their attention. State your findings clearly and concisely so that they can get the idea of what is more to come in your dissertation.

11. Connection with Literature Review

At the ending of your data analysis in the dissertation, make sure to compare your data with other published research. In this way, you can identify the points of differences and agreements. Check the consistency of your findings if they meet your expectations—lookup for bottleneck position. Analyze and discuss the reasons behind it. Identify the key themes, gaps, and the relation of your findings with the literature review. In short, you should link your data with your research question, and the questions should form a basis for literature.

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Wrapping Up

Writing data analysis in the dissertation involves dedication, and its implementations demand sound knowledge and proper planning. Choosing your topic, gathering relevant data, analyzing it, presenting your data and findings correctly, discussing the results, connecting with the literature and conclusions are milestones in it. Among these checkpoints, the Data analysis stage is most important and requires a lot of keenness.

In this article, we thoroughly looked at the tips that prove valuable for writing a data analysis in a dissertation. Make sure to give this article a thorough read before you write data analysis in the dissertation leading to the successful future of your research.

Oxbridge Essays. Top 10 Tips for Writing a Dissertation Data Analysis.

Emidio Amadebai

As an IT Engineer, who is passionate about learning and sharing. I have worked and learned quite a bit from Data Engineers, Data Analysts, Business Analysts, and Key Decision Makers almost for the past 5 years. Interested in learning more about Data Science and How to leverage it for better decision-making in my business and hopefully help you do the same in yours.

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• GETTING STARTED
• Introduction
• FUNDAMENTALS

Getting to the main article

Choosing your route

Setting research questions/ hypotheses

Assessment point

Building the theoretical case

Setting your research strategy

Data collection

Data analysis

Data analysis techniques

In STAGE NINE: Data analysis , we discuss the data you will have collected during STAGE EIGHT: Data collection . However, before you collect your data, having followed the research strategy you set out in this STAGE SIX , it is useful to think about the data analysis techniques you may apply to your data when it is collected.

The statistical tests that are appropriate for your dissertation will depend on (a) the research questions/hypotheses you have set, (b) the research design you are using, and (c) the nature of your data. You should already been clear about your research questions/hypotheses from STAGE THREE: Setting research questions and/or hypotheses , as well as knowing the goal of your research design from STEP TWO: Research design in this STAGE SIX: Setting your research strategy . These two pieces of information - your research questions/hypotheses and research design - will let you know, in principle , the statistical tests that may be appropriate to run on your data in order to answer your research questions.

We highlight the words in principle and may because the most appropriate statistical test to run on your data not only depend on your research questions/hypotheses and research design, but also the nature of your data . As you should have identified in STEP THREE: Research methods , and in the article, Types of variables , in the Fundamentals part of Lærd Dissertation, (a) not all data is the same, and (b) not all variables are measured in the same way (i.e., variables can be dichotomous, ordinal or continuous). In addition, not all data is normal , nor is the data when comparing groups necessarily equal , terms we explain in the Data Analysis section in the Fundamentals part of Lærd Dissertation. As a result, you might think that running a particular statistical test is correct at this point of setting your research strategy (e.g., a statistical test called a dependent t-test ), based on the research questions/hypotheses you have set, but when you collect your data (i.e., during STAGE EIGHT: Data collection ), the data may fail certain assumptions that are important to such a statistical test (i.e., normality and homogeneity of variance ). As a result, you have to run another statistical test (e.g., a Wilcoxon signed-rank test instead of a dependent t-test ).

At this stage in the dissertation process, it is important, or at the very least, useful to think about the data analysis techniques you may apply to your data when it is collected. We suggest that you do this for two reasons:

REASON A Supervisors sometimes expect you to know what statistical analysis you will perform at this stage of the dissertation process

This is not always the case, but if you have had to write a Dissertation Proposal or Ethics Proposal , there is sometimes an expectation that you explain the type of data analysis that you plan to carry out. An understanding of the data analysis that you will carry out on your data can also be an expected component of the Research Strategy chapter of your dissertation write-up (i.e., usually Chapter Three: Research Strategy ). Therefore, it is a good time to think about the data analysis process if you plan to start writing up this chapter at this stage.

REASON B It takes time to get your head around data analysis

When you come to analyse your data in STAGE NINE: Data analysis , you will need to think about (a) selecting the correct statistical tests to perform on your data, (b) running these tests on your data using a statistics package such as SPSS, and (c) learning how to interpret the output from such statistical tests so that you can answer your research questions or hypotheses. Whilst we show you how to do this for a wide range of scenarios in the in the Data Analysis section in the Fundamentals part of Lærd Dissertation, it can be a time consuming process. Unless you took an advanced statistics module/option as part of your degree (i.e., not just an introductory course to statistics, which are often taught in undergraduate and master?s degrees), it can take time to get your head around data analysis. Starting this process at this stage (i.e., STAGE SIX: Research strategy ), rather than waiting until you finish collecting your data (i.e., STAGE EIGHT: Data collection ) is a sensible approach.

Final thoughts...

Setting the research strategy for your dissertation required you to describe, explain and justify the research paradigm, quantitative research design, research method(s), sampling strategy, and approach towards research ethics and data analysis that you plan to follow, as well as determine how you will ensure the research quality of your findings so that you can effectively answer your research questions/hypotheses. However, from a practical perspective, just remember that the main goal of STAGE SIX: Research strategy is to have a clear research strategy that you can implement (i.e., operationalize ). After all, if you are unable to clearly follow your plan and carry out your research in the field, you will struggle to answer your research questions/hypotheses. Once you are sure that you have a clear plan, it is a good idea to take a step back, speak with your supervisor, and assess where you are before moving on to collect data. Therefore, when you are ready, proceed to STAGE SEVEN: Assessment point .

ON YOUR 1ST ORDER

Mastering Dissertation Data Analysis: A Comprehensive Guide

By Laura Brown on 29th December 2023

To craft an effective dissertation data analysis chapter, you need to follow some simple steps:

• Start by planning the structure and objectives of the chapter.
• Clearly set the stage by providing a concise overview of your research design and methodology.
• Proceed to thorough data preparation, ensuring accuracy and organisation.
• Justify your methods and present the results using visual aids for clarity.
• Discuss the findings within the context of your research questions.
• Finally, review and edit your chapter to ensure coherence.

This approach will ensure a well-crafted and impactful analysis section.

Before delving into details on how you can come up with an engaging data analysis show in your dissertation, we first need to understand what it is and why it is required.

What Is Data Analysis In A Dissertation?

The data analysis chapter is a crucial section of a research dissertation that involves the examination, interpretation, and synthesis of collected data. In this chapter, researchers employ statistical techniques, qualitative methods, or a combination of both to make sense of the data gathered during the research process.

Why Is The Data Analysis Chapter So Important?

The primary objectives of the data analysis chapter are to identify patterns, trends, relationships, and insights within the data set. Researchers use various tools and software to conduct a thorough analysis, ensuring that the results are both accurate and relevant to the research questions or hypotheses. Ultimately, the findings derived from this chapter contribute to the overall conclusions of the dissertation, providing a basis for drawing meaningful and well-supported insights.

Steps Required To Craft Data Analysis Chapter To Perfection

Now that we have an idea of what a dissertation analysis chapter is and why it is necessary to put it in the dissertation, let’s move towards how we can create one that has a significant impact. Our guide will move around the bulleted points that have been discussed initially in the beginning. So, it’s time to begin.

Step 1: Planning Your Data Analysis Chapter

Planning your data analysis chapter is a critical precursor to its successful execution.

• Begin by outlining the chapter structure to provide a roadmap for your analysis.
• Start with an introduction that succinctly introduces the purpose and significance of the data analysis in the context of your research.
• Following this, delineate the chapter into sections such as Data Preparation, where you detail the steps taken to organise and clean your data.
• Plan on to clearly define the Data Analysis Techniques employed, justifying their relevance to your research objectives.
• As you progress, plan for the Results Presentation, incorporating visual aids for clarity. Lastly, earmark a section for the Discussion of Findings, where you will interpret results within the broader context of your research questions.

This structured approach ensures a comprehensive and cohesive data analysis chapter, setting the stage for a compelling narrative that contributes significantly to your dissertation. You can always seek our dissertation data analysis help to plan your chapter.

Step 2: Setting The Stage – Introduction to Data Analysis

Your primary objective is to establish a solid foundation for the analytical journey. You need to skillfully link your data analysis to your research questions, elucidating the direct relevance and purpose of the upcoming analysis.

Simultaneously, define key concepts to provide clarity and ensure a shared understanding of the terms integral to your study. Following this, offer a concise overview of your data set characteristics, outlining its source, nature, and any noteworthy features.

This meticulous groundwork alongside our help with dissertation data analysis lays the base for a coherent and purposeful chapter, guiding readers seamlessly into the subsequent stages of your dissertation.

Step 3: Data Preparation

Now this is another pivotal phase in the data analysis process, ensuring the integrity and reliability of your findings. You should start with an insightful overview of the data cleaning and preprocessing procedures, highlighting the steps taken to refine and organise your dataset. Then, discuss any challenges encountered during the process and the strategies employed to address them.

Moving forward, delve into the specifics of data transformation procedures, elucidating any alterations made to the raw data for analysis. Clearly describe the methods employed for normalisation, scaling, or any other transformations deemed necessary. It will not only enhance the quality of your analysis but also foster transparency in your research methodology, reinforcing the robustness of your data-driven insights.

Step 4: Data Analysis Techniques

The data analysis section of a dissertation is akin to choosing the right tools for an artistic masterpiece. Carefully weigh the quantitative and qualitative approaches, ensuring a tailored fit for the nature of your data.

Quantitative Analysis

• Descriptive Statistics: Paint a vivid picture of your data through measures like mean, median, and mode. It’s like capturing the essence of your data’s personality.
• Inferential Statistics:Take a leap into the unknown, making educated guesses and inferences about your larger population based on a sample. It’s statistical magic in action.

Qualitative Analysis

• Thematic Analysis: Imagine your data as a novel, and thematic analysis as the tool to uncover its hidden chapters. Dissect the narrative, revealing recurring themes and patterns.
• Content Analysis: Scrutinise your data’s content like detectives, identifying key elements and meanings. It’s a deep dive into the substance of your qualitative data.

Providing Rationale for Chosen Methods

You should also articulate the why behind the chosen methods. It’s not just about numbers or themes; it’s about the story you want your data to tell. Through transparent rationale, you should ensure that your chosen techniques align seamlessly with your research goals, adding depth and credibility to the analysis.

Step 5: Presentation Of Your Results

You can simply break this process into two parts.

a.    Creating Clear and Concise Visualisations

Effectively communicate your findings through meticulously crafted visualisations. Use tables that offer a structured presentation, summarising key data points for quick comprehension. Graphs, on the other hand, visually depict trends and patterns, enhancing overall clarity. Thoughtfully design these visual aids to align with the nature of your data, ensuring they serve as impactful tools for conveying information.

b.    Interpreting and Explaining Results

Go beyond mere presentation by providing insightful interpretation by taking data analysis services for dissertation. Show the significance of your findings within the broader research context. Moreover, articulates the implications of observed patterns or relationships. By weaving a narrative around your results, you guide readers through the relevance and impact of your data analysis, enriching the overall understanding of your dissertation’s key contributions.

Step 6: Discussion of Findings

While discussing your findings and dissertation discussion chapter , it’s like putting together puzzle pieces to understand what your data is saying. You can always take dissertation data analysis help to explain what it all means, connecting back to why you started in the first place.

Be honest about any limitations or possible biases in your study; it’s like showing your cards to make your research more trustworthy. Comparing your results to what other smart people have found before you adds to the conversation, showing where your work fits in.

Looking ahead, you suggest ideas for what future researchers could explore, keeping the conversation going. So, it’s not just about what you found, but also about what comes next and how it all fits into the big picture of what we know.

Step 7: Writing Style and Tone

In order to perfectly come up with this chapter, follow the below points in your writing and adjust the tone accordingly,

• Use clear and concise language to ensure your audience easily understands complex concepts.
• Avoid unnecessary jargon in data analysis for thesis, and if specialised terms are necessary, provide brief explanations.
• Keep your writing style formal and objective, maintaining an academic tone throughout.
• Avoid overly casual language or slang, as the data analysis chapter is a serious academic document.
• Clearly define terms and concepts, providing specific details about your data preparation and analysis procedures.
• Use precise language to convey your ideas, minimising ambiguity.
• Follow a consistent formatting style for headings, subheadings, and citations to enhance readability.
• Ensure that tables, graphs, and visual aids are labelled and formatted uniformly for a polished presentation.
• Connect your analysis to the broader context of your research by explaining the relevance of your chosen methods and the importance of your findings.
• Offer a balance between detail and context, helping readers understand the significance of your data analysis within the larger study.
• Present enough detail to support your findings but avoid overwhelming readers with excessive information.
• Use a balance of text and visual aids to convey information efficiently.
• Maintain reader engagement by incorporating transitions between sections and effectively linking concepts.
• Use a mix of sentence structures to add variety and keep the writing engaging.
• Eliminate grammatical errors, typos, and inconsistencies through thorough proofreading.
• Consider seeking feedback from peers or mentors to ensure the clarity and coherence of your writing.

You can seek a data analysis dissertation example or sample from CrowdWriter to better understand how we write it while following the above-mentioned points.

Step 8: Reviewing and Editing

Reviewing and editing your data analysis chapter is crucial for ensuring its effectiveness and impact. By revising your work, you refine the clarity and coherence of your analysis, enhancing its overall quality.

Seeking feedback from peers, advisors or dissertation data analysis services provides valuable perspectives, helping identify blind spots and areas for improvement. Addressing common writing pitfalls, such as grammatical errors or unclear expressions, ensures your chapter is polished and professional.

Taking the time to review and edit not only strengthens the academic integrity of your work but also contributes to a final product that is clear, compelling, and ready for scholarly scrutiny.

Concluding On This Data Analysis Help

Be it master thesis data analysis, an undergraduate one or for PhD scholars, the steps remain almost the same as we have discussed in this guide. The primary focus is to be connected with your research questions and objectives while writing your data analysis chapter.

Do not lose your focus and choose the right analysis methods and design. Make sure to present your data through various visuals to better explain your data and engage the reader as well. At last, give it a detailed read and seek assistance from experts and your supervisor for further improvement.

Laura Brown, a senior content writer who writes actionable blogs at Crowd Writer.

How To Write The Results/Findings Chapter

For quantitative graduate (dissertations & theses).

By: Derek Johansen (MBA). Accomplished Reviewed The: Kerryn Warren (PhD) | July 2021

So, you’ve completed your quantitative data analysis and it’s time to report on own findings. But show do him start? In this post, we’ll take you through which results chapter (also labeled the findings or study chapter), step by step, so that you can craft this section of your research conversely thesis with confidence. If you’re looking on information regarding the end chapter for qualitative studies, you can find that here .

Summary: Quantitative Results Chapter

• What exactly the results/findings/analysis chapter is
• What you need to include int your results chapter
• How to structure your results chapter
• AMPERE few tips and tricks for writing top-notch chapter

What exactly is the results chapter?

The results chapter (also referred to as the findings or analysis chapter) shall one of the most important chapters of your dissertation or thesis because it exhibitions the reader what you’ve found in terms of the quantitative data you’ve collected. It presents the date using ampere clear write narrative, supported the charts, diagram and charts. In doing so, it also climax any potential issues (such as irregularities button unusual findings) you’ve come across.

Although how’s that different from to discussion chapter?

Well, by the results chapter, you only present your statistical findings. Only the numbers, so to address – no more, no less. Contrasts to this, in the panel chapters , you interpret your findings and linked yours to prior research (i.e. your literature review), as well as your search objectives also research questions. Stylish other words, the results chapter presents both describes the data, while this discussion chapter interprets the data.

Let’s look at an example.

In your result chapter, you maybe have a design is shows how respondents to a survey  responded: to numbers of respondents per category, for instance. You may other state whether this supports a hypothesis by after a p-value from an statistical exam. Aber it is only in an discussion chapter where you will say why this is relevant or how it compares with which literature or the broader picture. So, in your results chapter, induce certainly that her don’t present anything other than the hard facts – is is not the place in subjectivity.

It’s merit mentioning that some universities prefer you to combine the results or discussion click. Even so, it is goody practical to separate the results and discussion elements within the chapter, as this guarantee choose outcome are fully portrayed. Normal, though, the results and discussion books are split boost in quantitative studies. If you’re unsure, chats with your research supervisor or chair to find out what their preference is.

What should to include in one results section?

Following you analysis, it’s likely you’ll have far more datas from are necessary go include in your chapter. In all likelihood, you’ll have a mountain of SPSS or R output data, and it’s your job to decide what’s most relevant. You’ll need to cut through the noise and focus on the data that matters.

Like doesn’t mean that those analyses was a waste out date – on the contrary, who analyses ensure so they have a good understating the your dataset plus how to schauspieler it. However, that doesn’t mean your reader or researcher needs to see the 165 histograms you created! Relevance is key. Data analysis write-ups

How do I decide what’s relevant?

At this point, it may be difficult to strike a balance between what will and isn’t important. But the bulk important things is to ensure your earnings reflect and orient with the purpose of your study .  Like, you need to revisit your choose aims, objectives and search questions plus use these as a litmus test with relevance. Take sure that you refer back to these constantly while print up your chapter so that you stay on pisten.

As a general guided, your results book will usually include the following:

• Some demographic data about is sample
• Reliability tests (if you used measurement scales)
• Descriptive statistics
• Inferential online (if your choose objectives and questions order these)
• Hypothesis tests (again, if your research your and questions require these)

We’ll explore each off these points in more detailed within the next section.

Importantly, your ergebnisse chapter needed to lay the foundation for your discussion chapter . This means that, in your results chapter, you need to include all the date is she will use more the basis forward your interpretation in who discussion chapter.

For example, if you draft to set the strong relationship between Variable X and Variable UNKNOWN in your discussion chapter, you need to present the respective analysis in your results chapter – perhaps a compare or regressive analysis.

Needing a helping hand?

How execute I write the results chapter?

There are repeatedly steps complicated in writing up who results chapter for owner quantitative research. The exact number of steps applicable toward you will vary from study to investigate and will depend on the nature of the research aims, objectives and research questions . However, we’ll outlines the generic stepping below.

Step 1 – Revisit your choose questions

The first step in writing autochthonous results chapter is to revisit your research objectives additionally research questions . Dieser will been (or at least, should be!) the driving forces behind your results and discussion chapters, to you need to review them and then ask your which statistical tests and tests (from choose mountain for data) would specifically help you address these . For each research objective and exploring go, list the specific part (or pieces) of analysis that address it.

At this stage, it’s also useful to thinks about the key points that her want to raise in your topic chapter press note diesen down so this you can a clear reminder of which data points and analyses you what to highlight int the results chapter. Again, list your points and following list the specific piece of analysis that addresses either point.  The analysis chapter is one of the most important spare of a thesis. Here is a finish guide to dissertation data analysis!

Next, you should pull up a rough outline of method you plan to structure your chapter . Which analyses and statistical tests will you present and in about order? We’ll discussed the “standard structure” in more detail later, not it’s worth mentioning now this it’s always useful to tie up a crude outline before you start writing (this advice applies to any chapter).

Speed 2 – Craft any overview introduction

As with all chapters in thy dissertation or dissertation, you should start your quantitative resultat chapter by providing a brief overview of get you’ll do in the chapters and wherefore . For example, you’d explain that thee will start by presenting statistical intelligence into understand the representativeness of the sample, before moving onto X, Y furthermore Z.

This section shouldn’t be lengthy – a paragraph instead two maximum. Also, it’s a good idea to web the research questions on this section so that there’s a glitter thread that runs trough the documents.

Step 3 – Present one sample demographic dating

The first selected of data that you’ll presence is an overview of the sample demographics – includes other words, the census of your respondents.

In example:

• What age range are they?
• How is sexuality distributed?
• How is race distributed?
• What areas do the participants live in?

The purpose of aforementioned is to assess how spokesperson the sample is of and broader population. This is major required the sake of and generalisability of this results. If your sample is not representative of the population, you will not be able to generalise your findings. Which is not necessarily of end of the world, but a is a limitation you’ll need into acknowledge.

To course, to manufacture this representativeness assessment, you’ll need to have adenine clear view are of demographics of aforementioned population. So, make sure that you design your survey to capture the correct demographic information that you will compare your sample to.

But get if I’m not interested in generalisability?

Okay, even if your purpose is not necessarily until extend autochthonous findings to the broader population, understanding your sample will allow you until ausleger is findings suitably, considering who reply. On other words, is will help you contextualise is findings . For example, are 80% the your sample was senior over 65, this may be a significant contextual factor to consider at interpreting the data. Therefore, it’s essential toward understand and present the demographic file.

 Step 4 – Examination composite measures and the data “shape”.

Before you undertake any statistical analysis, you’ll need to do some inspection to ensure that will details be suitable for the analysis processes also techniques you set toward use. If you try to analyse intelligence ensure doesn’t meet the conjectures a a specific statistical technique, own results will be largely meaningless. Therefore, you may need to show that the processes and abilities you’ll getting are “allowed”.

Most commonly, there are two areas you needed to pay attention to:

#1: Composition measures

The first remains when you have repeatedly scale-based measures that combine to capture one construct – this is called a composite measure .  For example, you may have four Likert scale-based measures that (should) all measure the same thing, but in separate ways. In other words, in a scrutinize, these quad scales should all receive similar ratings. On is called “ internal consistency ”.

Internal consistency is not guaranteed though (especially if you developed the measures yourself), so you need until assess the vertrauen of each composed evaluate by a exam. Typically, Cronbach’s Alpha is a joint check used to assess user consistency – i.e., to shows ensure the product you’re combing are more or less sayings the same thing. ONE height alpha score means that your take is internally consistent. ADENINE low alpha score means you may need to consider scrapping one or find of the measures.

#2: Product shape

The second mattigkeit that you should address early go in your results chapter is data shapes. In other words, you need to assess whether the data at insert set are symmetry (i.e. normally distributed) or not, as this will directly impact what type starting analyses you can use. For loads common inferential testing such than T-tests or ANOVAs (we’ll discuss above-mentioned a bit later), your data needed to is normally distributed. If it’s not, you’ll need into adjust your strategy and use alternative tests.

To assess the shape off the data, you’ll typical assess a choose of descriptive statistics (such as the mean, median and skewness), which exists what we’ll look at next.

Step 5 – Present the descriptive vital

Now that you’ve laid the foundation by discussing the presence of our sample, as well as the reliability of your measures and an shapes of owner data, you can get started the the actual statistical analysis. The first step is to present the descriptive statistics for your variables.

By scaled data, this usually includes statistics such as:

• The mean – this is merely the mathematical average of a range of numbers.
• The median – like is who midpoint in a range of numbers at the numbers are arranged in order.
• Aforementioned run – this is who highest commonly repeated number in the file set.
• Standard variance – this metric indicates how dispersed a range of numbering is. In other words, how close all that numbers can to the mean (the average).
• Skewness – this indicates how symmetrical a range of numbers is. In other words, do it tend to clustering into a smooth bell curve shaped in the median of the graph (this is calls a normal or parametric distribution), either do they lean to the left or right (this is called a non-normal instead non-parametric distribution).
• Kurtosis – this metric points whether the data belong heavily or lightly-tailed, relative to the normal dissemination. In other words, how peaked or flat the distribution is.

A bigger table that indicates all to above for repeated variables can be a very effective way to present thy data economically. It can also use colour coding to help make the product more easily digestible.

For categorical data, location you show the percentage of people who chose or perfect into a choose, for instance, it bottle either simply plain describe this percentages or numbers of people who responded to something or usage graphs additionally schedules (such as bar graphs and pie charts) into present your details in this section by the chapter.

When using numbers, produce sure that you label their single and clear , so that your reader can easily understand them. There’s nothing more frustrating than a graph that’s missing axis labels! Keep in mind that although you’ll be presenting charts and graphs, your theme content needs to present an clear narrative that can stand on seine own. By other words, don’t rely purely on insert numbers and tables to convey your main points: highlight the crucial trends and values in the text. Figures and tables have complement who writing, not portable itp .

Depending on your explore aims, objectives and research questions, your may stop your analysis at this point (i.e. descriptive statistics). However, if your study requires inferential statistics, then it’s time to deep dive into those .

Step 6 – Present the inferential statistischen

Inferential statistiken are used to make generalisations about a population , whereas descriptive zahlen focus purely on the sample . Logical statistical capabilities, broadly speaking, can be broken down into two groups .

First, there are those that comparing measurements with bunches , create as t-tests (which measure differences between two groups) and ANOVAs (which gauge discrepancies between multiple groups). Second, there are techniques that assess the relationships between variables , such as correlation analysis and regression analyzed. Within each of these, certain tests can be previously for normally distributed (parametric) data and certain tests are planned specifically for uses on non-parametric data.

Thither are a seemingly endless number of tests that you ability use to crunch your data, so it’s easily go run down a rabbit hole and end up to piles of test data. Ultimately, the most important thing belongs to create sure that you adopt the tests and techniques that authorize it to realisieren your research objectives and answer your research questions .

In this section in the results chapter, you should try to doing use of figures and visual equipment as effectively as possible. For real, if you presenting a relationship charts, use colour encryption to bright the significance of the correlation principles, or scatterplots to optically demonstrate where the trend remains. The light you make it for your reader to digest your findings, the more effectively you’ll will able to make your arguments in the next chapter.

Level 7 – Test your research

If your study requires it, the upcoming stage is hypothesis test. A hypothesis is a statement , often indicating a gauge between groups or relationship between variables, that can becoming supported or rejected by a statistiken test. However, don see studies will involve conjectures (again, it depends on the investigate objectives), so don’t feelings like you “must” present and test hypotheses just because you’re undertaking quantitive research.

Of basic process for hypothesis testing is because follows:

• Specify your null hypothesis (for example, “The chemical psilocybin has no effect on time perception).
• Specify is alternative hypothesis (e.g., “The synthesized psilocybin has an affect up time perception)
• Set your significance level (this is commonly 0.05)
• Count the statistics and find yours p-value (e.g., p=0.01)
• Draw your conclusions (e.g., “The chemical psilocybin does have an effects on time perception”)

Finally, if the aim of your study is to develop and test an notions framework , this is the time to present thereto, following the testing of your hypotheses. While you don’t need to develop other discuss these findings further in the results branch, indicating whether the tests (and their p-values) support or reject the hypotheses is vital.

Step 8 – Offer a chapter summarized

For wrap up your results chapter and transition to the discussion chapter, you should provide a brief summary of an key findings . “Brief” is the keyword her – much like the chapter preamble, this shouldn’t be extensive – a paragraph instead second maximum. Stress the find most relevant to your research objectives and research frequently, and wrap it up.

Needed a helping hand-held?

Some final my, tip and tricks.

Now that you’ve got the essentials downhill, here are a few tips and trick to perform respective quantify results chapter brilliance:

• Available writing your results chapter, report get what in the past tense . You’re talking around what you’ve found in choose data, not what you are currently find for or trying to find.
• Structure your results chapter systematically both sequentially . Supposing you must two experiments where findings from which one generated inputs into the other, submit on them at command.
• Make your own tables and graphs rather than copied press pasting them from static analysis software like SPSS. Checking out the DataIsBeautiful reddit for several inspiration.
• Once you’re done writing, review thy work to make sure that you had provided enough information to answer your research matter , but additionally that them didn’t include superfluous information.

If you’ve got any questions about writing up the quantitative scores chapter, please walk a commenting below. If you’d like 1-on-1 helps with your quantitative analysis and topic, check out the hands-on coaching service , or book ampere free consultation with a friendly coach.

Psst… there’s better (for free)

This post exists part of our dissertation mini-course, that covers everything you must to get launched with your dissertation, thesis or research project. 

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Writing a Dissertation Data Analysis the Right Way

Do you want to be a college professor? Most teaching positions at four-year universities and colleges require the applicants to have at least a doctoral degree in the field they wish to teach in. If you are looking for information about the dissertation data analysis, it means you have already started working on yours. Congratulations!

Truth be told, learning how to write a data analysis the right way can be tricky. This is, after all, one of the most important chapters of your paper. It is also the most difficult to write, unfortunately. The good news is that we will help you with all the information you need to write a good data analysis chapter right now. And remember, if you need an original dissertation data analysis example, our PhD experts can write one for you in record time. You’ll be amazed how much you can learn from a well-written example.

OK, But What Is the Data Analysis Section?

Don’t know what the data analysis section is or what it is used for? No problem, we’ll explain it to you. Understanding the data analysis meaning is crucial to understanding the next sections of this blog post.

Basically, the data analysis section is the part where you analyze and discuss the data you’ve uncovered. In a typical dissertation, you will present your findings (the data) in the Results section. You will explain how you obtained the data in the Methodology chapter.

The data analysis section should be reserved just for discussing your findings. This means you should refrain from introducing any new data in there. This is extremely important because it can get your paper penalized quite harshly. Remember, the evaluation committee will look at your data analysis section very closely. It’s extremely important to get this chapter done right.

Learn What to Include in Data Analysis

Don’t know what to include in data analysis? Whether you need to do a quantitative data analysis or analyze qualitative data, you need to get it right. Learning how to analyze research data is extremely important, and so is learning what you need to include in your analysis. Here are the basic parts that should mandatorily be in your dissertation data analysis structure:

• The chapter should start with a brief overview of the problem. You will need to explain the importance of your research and its purpose. Also, you will need to provide a brief explanation of the various types of data and the methods you’ve used to collect said data. In case you’ve made any assumptions, you should list them as well.
• The next part will include detailed descriptions of each and every one of your hypotheses. Alternatively, you can describe the research questions. In any case, this part of the data analysis chapter will make it clear to your readers what you aim to demonstrate.
• Then, you will introduce and discuss each and every piece of important data. Your aim is to demonstrate that your data supports your thesis (or answers an important research question). Go in as much detail as possible when analyzing the data. Each question should be discussed in a single paragraph and the paragraph should contain a conclusion at the end.
• The very last part of the data analysis chapter that an undergraduate must write is the conclusion of the entire chapter. It is basically a short summary of the entire chapter. Make it clear that you know what you’ve been talking about and how your data helps answer the research questions you’ve been meaning to cover.

Dissertation Data Analysis Methods

If you are reading this, it means you need some data analysis help. Fortunately, our writers are experts when it comes to the discussion chapter of a dissertation, the most important part of your paper. To make sure you write it correctly, you need to first ensure you learn about the various data analysis methods that are available to you. Here is what you can – and should – do during the data analysis phase of the paper:

• Validate the data. This means you need to check for fraud (were all the respondents really interviewed?), screen the respondents to make sure they meet the research criteria, check that the data collection procedures were properly followed, and then verify that the data is complete (did each respondent receive all the questions or not?). Validating the data is no as difficult as you imagine. Just pick several respondents at random and call them or email them to find out if the data is valid.

For example, an outlier can be identified using a scatter plot or a box plot. Points (values) that are beyond an inner fence on either side are mild outliers, while points that are beyond an outer fence are called extreme outliers.

• If you have a large amount of data, you should code it. Group similar data into sets and code them. This will significantly simplify the process of analyzing the data later.

For example, the median is almost always used to separate the lower half from the upper half of a data set, while the percentage can be used to make a graph that emphasizes a small group of values in a large set o data.

ANOVA, for example, is perfect for testing how much two groups differ from one another in the experiment. You can safely use it to find a relationship between the number of smartphones in a family and the size of the family’s savings.

Analyzing qualitative data is a bit different from analyzing quantitative data. However, the process is not entirely different. Here are some methods to analyze qualitative data:

You should first get familiar with the data, carefully review each research question to see which one can be answered by the data you have collected, code or index the resulting data, and then identify all the patterns. The most popular methods of conducting a qualitative data analysis are the grounded theory, the narrative analysis, the content analysis, and the discourse analysis. Each has its strengths and weaknesses, so be very careful which one you choose.

Of course, it goes without saying that you need to become familiar with each of the different methods used to analyze various types of data. Going into detail for each method is not possible in a single blog post. After all, there are entire books written about these methods. However, if you are having any trouble with analyzing the data – or if you don’t know which dissertation data analysis methods suits your data best – you can always ask our dissertation experts. Our customer support department is online 24 hours a day, 7 days a week – even during holidays. We are always here for you!

Tips and Tricks to Write the Analysis Chapter

Did you know that the best way to learn how to write a data analysis chapter is to get a great example of data analysis in research paper? In case you don’t have access to such an example and don’t want to get assistance from our experts, we can still help you. Here are a few very useful tips that should make writing the analysis chapter a lot easier:

• Always start the chapter with a short introductory paragraph that explains the purpose of the chapter. Don’t just assume that your audience knows what a discussion chapter is. Provide them with a brief overview of what you are about to demonstrate.
• When you analyze and discuss the data, keep the literature review in mind. Make as many cross references as possible between your analysis and the literature review. This way, you will demonstrate to the evaluation committee that you know what you’re talking about.
• Never be afraid to provide your point of view on the data you are analyzing. This is why it’s called a data analysis and not a results chapter. Be as critical as possible and make sure you discuss every set of data in detail.
• If you notice any patterns or themes in the data, make sure you acknowledge them and explain them adequately. You should also take note of these patterns in the conclusion at the end of the chapter.
• Do not assume your readers are familiar with jargon. Always provide a clear definition of the terms you are using in your paper. Not doing so can get you penalized. Why risk it?
• Don’t be afraid to discuss both the advantage and the disadvantages you can get from the data. Being biased and trying to ignore the drawbacks of the results will not get you far.
• Always remember to discuss the significance of each set of data. Also, try to explain to your audience how the various elements connect to each other.
• Be as balanced as possible and make sure your judgments are reasonable. Only strong evidence should be used to support your claims and arguments. Weak evidence just shows that you did not do your best to uncover enough information to answer the research question.
• Get dissertation data analysis help whenever you feel like you need it. Don’t leave anything to chance because the outcome of your dissertation depends in large part on the data analysis chapter.

Finally, don’t be afraid to make effective use of any quantitative data analysis software you can get your hands on. We know that many of these tools can be quite expensive, but we can assure you that the investment is a good idea. Many of these tools are of real help when it comes to analyzing huge amounts of data.

Final Considerations

Finally, you need to be aware that the data analysis chapter should not be rushed in any way. We do agree that the Results chapter is extremely important, but we consider that the Discussion chapter is equally as important. Why? Because you will be explaining your findings and not just presenting some results. You will have the option to talk about your personal opinions. You are free to unleash your critical thinking and impress the evaluation committee. The data analysis section is where you can really shine.

Also, you need to make sure that this chapter is as interesting as it can be for the reader. Make sure you discuss all the interesting results of your research. Explain peculiar findings. Make correlations and reference other works by established authors in your field. Show your readers that you know that subject extremely well and that you are perfectly capable of conducting a proper analysis no matter how complex the data may be. This way, you can ensure that you get maximum points for the data analysis chapter. If you can’t do a great job, get help ASAP!

Need Some Assistance With Data Analysis?

If you are a university student or a graduate, you may need some cheap help with writing the analysis chapter of your dissertation. Remember, time saving is extremely important because finishing the dissertation on time is mandatory. You should consider our amazing services the moment you notice you are not on track with your dissertation. Also, you should get help from our dissertation writing service in case you can’t do a terrific job writing the data analysis chapter. This is one of the most important chapters of your paper and the supervisor will look closely at it.

Why risk getting penalized when you can get high quality academic writing services from our team of experts? All our writers are PhD degree holders, so they know exactly how to write any chapter of a dissertation the right way. This also means that our professionals work fast. They can get the analysis chapter done for you in no time and bring you back on track. It’s also worth noting that we have access to the best software tools for data analysis. We will bring our knowledge and technical know-how to your project and ensure you get a top grade on your paper. Get in touch with us and let’s discuss the specifics of your project right now!

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Whether you’ve just started collecting your data, are in the thick of analysing it, or you’ve already written a draft chapter – we’re here to help. 

Make sense of the data

If you’ve collected your data, but are feeling confused about what to do and how to make sense of it all, we can help. One of our friendly coaches will hold your hand through each step and help you interpret your dataset .

Alternatively, if you’re still planning your data collection and analysis strategy, we can help you craft a rock-solid methodology  that sets you up for success.

Get your thinking onto paper

If you’ve analysed your data, but are struggling to get your thoughts onto paper, one of our friendly Grad Coaches can help you structure your results and/or discussion chapter to kickstart your writing.

Refine your writing

If you’ve already written up your results but need a second set of eyes, our popular Content Review service can help you identify and address key issues within your writing, before you submit it for grading .

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We take the time to understand your unique challenges and work with you to achieve your specific academic goals . Whether you're aiming to earn top marks or just need to cross the finish line, we're here to help.

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Our award-winning Dissertation Coaches all hold doctoral-level degrees and share 100+ years of combined academic experience. Having worked on "the inside", we know exactly what markers want .

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Have a question ?

Below we address some of the most popular questions we receive regarding our data analysis support, but feel free to get in touch if you have any other questions.

Dissertation Coaching

I have no idea where to start. can you help.

Absolutely. We regularly work with students who are completely new to data analysis (both qualitative and quantitative) and need step-by-step guidance to understand and interpret their data.

Can you analyse my data for me?

The short answer – no. 

The longer answer:

If you’re undertaking qualitative research , we can fast-track your project with our Qualitative Coding Service. With this service, we take care of the initial coding of your dataset (e.g., interview transcripts), providing a firm foundation on which you can build your qualitative analysis (e.g., thematic analysis, content analysis, etc.).

If you’re undertaking quantitative research , we can fast-track your project with our Statistical Testing Service . With this service, we run the relevant statistical tests using SPSS or R, and provide you with the raw outputs. You can then use these outputs/reports to interpret your results and develop your analysis.

Importantly, in both cases, we are not analysing the data for you or providing an interpretation or write-up for you. If you’d like coaching-based support with that aspect of the project, we can certainly assist you with this (i.e., provide guidance and feedback, review your writing, etc.). But it’s important to understand that you, as the researcher, need to engage with the data and write up your own findings. 

Can you help me choose the right data analysis methods?

Yes, we can assist you in selecting appropriate data analysis methods, based on your research aims and research questions, as well as the characteristics of your data.

Which data analysis methods can you assist with?

We can assist with most qualitative and quantitative analysis methods that are commonplace within the social sciences.

Qualitative methods:

• Qualitative content analysis
• Thematic analysis
• Discourse analysis
• Narrative analysis
• Grounded theory

Quantitative methods:

• Descriptive statistics
• Inferential statistics

Can you provide data sets for me to analyse?

If you are undertaking secondary research , we can potentially assist you in finding suitable data sets for your analysis.

If you are undertaking primary research , we can help you plan and develop data collection instruments (e.g., surveys, questionnaires, etc.), but we cannot source the data on your behalf. 

Can you write the analysis/results/discussion chapter/section for me?

No. We can provide you with hands-on guidance through each step of the analysis process, but the writing needs to be your own. Writing anything for you would constitute academic misconduct .

Can you help me organise and structure my results/discussion chapter/section?

Yes, we can assist in structuring your chapter to ensure that you have a clear, logical structure and flow that delivers a clear and convincing narrative.

Can you review my writing and give me feedback?

Absolutely. Our Content Review service is designed exactly for this purpose and is one of the most popular services here at Grad Coach. In a Content Review, we carefully read through your research methodology chapter (or any other chapter) and provide detailed comments regarding the key issues/problem areas, why they’re problematic and what you can do to resolve the issues. You can learn more about Content Review here .

Do you provide software support (e.g., SPSS, R, etc.)?

It depends on the software package you’re planning to use, as well as the analysis techniques/tests you plan to undertake. We can typically provide support for the more popular analysis packages, but it’s best to discuss this in an initial consultation.

Can you help me with other aspects of my research project?

Yes. Data analysis support is only one aspect of our offering at Grad Coach, and we typically assist students throughout their entire dissertation/thesis/research project. You can learn more about our full service offering here .

Can I get a coach that specialises in my topic area?

It’s important to clarify that our expertise lies in the research process itself , rather than specific research areas/topics (e.g., psychology, management, etc.).

In other words, the support we provide is topic-agnostic, which allows us to support students across a very broad range of research topics. That said, if there is a coach on our team who has experience in your area of research, as well as your chosen methodology, we can allocate them to your project (dependent on their availability, of course).

If you’re unsure about whether we’re the right fit, feel free to drop us an email or book a free initial consultation.

What qualifications do your coaches have?

All of our coaches hold a doctoral-level degree (for example, a PhD, DBA, etc.). Moreover, they all have experience working within academia, in many cases as dissertation/thesis supervisors. In other words, they understand what markers are looking for when reviewing a student’s work.

Is my data/topic/study kept confidential?

Yes, we prioritise confidentiality and data security. Your written work and personal information are treated as strictly confidential. We can also sign a non-disclosure agreement, should you wish.

I still have questions…

No problem. Feel free to email us or book an initial consultation to discuss.

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Cyntia Sacani (US)

I had been struggling with the first 3 chapters of my dissertation for over a year. I finally decided to give GradCoach a try and it made a huge difference. Alexandra provided helpful suggestions along with edits that transformed my paper. My advisor was very impressed.

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Working with Kerryn has been brilliant. She has guided me through that pesky academic language that makes us all scratch our heads. I can't recommend Grad Coach highly enough; they are very professional, humble, and fun to work with. If like me, you know your subject matter but you're getting lost in the academic language, look no further, give them a go.

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So helpful! Amy assisted me with an outline for my literature review and with organizing the results for my MBA applied research project. Having a road map helped enormously and saved a lot of time. Definitely worth it.

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Everything about my experience was great, from Dr. Shaeffer’s expertise, to her patience and flexibility. I reached out to GradCoach after receiving a 78 on a midterm paper. Not only did I get a 100 on my final paper in the same class, but I haven’t received a mark less than A+ since. I recommend GradCoach for everyone who needs help with academic research.

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Research Gateway

Data Analysis Chapter

Writing a dissertation is complicated. It is also the single most important document you will ever create; offering not just the potential for higher graduation placement, but opening doors to publication, employment, and even the opportunity to further expand upon your research. The last thing you want is to receive lacklustre results because of the presentation of you dissertation, which makes it an intelligent decision to seek help for writing your Data Analysis Chapter . Data analysis refers to the sifting, assimilating, modelling and transforming of data collected by the researcher. It involves tabulation or presentation of data in some other form, done with the purpose of suggesting conclusions and supporting the hypothesis presented in the dissertation. It can be done in different ways and has many connotations, depending on the field and subject of research. The analysis of data can be done in descriptive, exploratory or confirmatory manner. The method to be used for data analysis depends on whether you are exploring a new idea in your research, or set out to prove some ideology already present. These issues relating to decision making can be easily resolved with the help of our experts. The process of data analysis is a detailed one. It starts from managing the data in the right format to final interpretation of it for arriving at the conclusions. Data must be cleaned, i.e., irrelevant and wrong data must be removed or corrected at the stage of data entry itself. Next is the stage of checking the quality of data collected; its adequacy and accuracy for the research must be determined. These steps precede the actual analysis for data. These complexities are sure to confuse a research scholar who is working on a dissertation for the first time. Data analysis chapter writing for quantitative study is done after conducting statistical tests . Our statisticians are well versed with SPSS , Stata, E-Views and R for conducting such statistical tests. The data analysis chapter written by our experts will be accurate and easy to understand. This is because our team of statistical analysts have an in-depth knowledge and experience in the field. They can assist you in writing the chapter yourself, and resolve all your problems in a most customised manner. Our statisticians first understand the complexity and spread of the data you have collected and then select the right tool for analysis. They will not just write the chapter, but also explain to you the details of the processes used and the logic behind it. The data analysis chapter of your dissertation is where you put all the research together and really get into the pith of your findings. Here, you will need to present both qualitative and quantitative data, and be able to clearly explain your findings. Your data must be easy to understand, and it should clearly explain how your research methodology generated the results. It should essentially be the glue that holds your dissertation together. Presenting everything in the right format and detail is crucial and can have a major impact on how well your dissertation is received. When you have spent a year or more performing all of the research and worked hard on various techniques to ensure that you get the most accurate results possible, not getting the right result can be devastating. To ensure that your dissertation is understood and that your hard work and effort really pays off, it is important to make certain that you have a data analysis chapter that is well written. A quality writing service will be able to take up all of your information, including your research methodology and your results and create a data analysis chapter that explains your findings in the most technical and in-depth way possible. When you need your dissertation to not just help you graduate, but to really open doors for you, doing whatever it takes to keep your data analysis chapter tight and well written is absolutely critical. All you need to do is get in touch with us and we will hand over your work to the best possible person, who will understand your study and create a data analysis that hits the targeted question on the head.