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A Complete Guide to Dissertation Data Analysis

The analysis chapter is one of the most important parts of a dissertation where you demonstrate the unique research abilities. That is why it often accounts for up to 40% of the total mark. Given the significance of this chapter, it is essential to build your skills in dissertation data analysis .

Typically, the analysis section provides an output of calculations, interpretation of attained results and discussion of these results in light of theories and previous empirical evidence. Oftentimes, the chapter provides qualitative data analysis that do not require any calculations. Since there are different types of research design, let’s look at each type individually.

1. Types of Research

The dissertation topic you have selected, to a considerable degree, informs the way you are going to collect and analyse data. Some topics imply the collection of primary data, while others can be explored using secondary data. Selecting an appropriate data type is vital not only for your ability to achieve the main aim and objectives of your dissertation but also an important part of the dissertation writing process since it is what your whole project will rest on.

Selecting the most appropriate data type for your dissertation may not be as straightforward as it may seem. As you keep diving into your research, you will be discovering more and more details and nuances associated with this or that type of data. At some point, it is important to decide whether you will pursue the qualitative research design or the quantitative research design.

1.1. Qualitative vs Quantitative Research

1.1.1. quantitative research.

Quantitative data is any numerical data which can be used for statistical analysis and mathematical manipulations. This type of data can be used to answer research questions such as ‘How often?’, ‘How much?’, and ‘How many?’. Studies that use this type of data also ask the ‘What’ questions (e.g. What are the determinants of economic growth? To what extent does marketing affect sales? etc.).

An advantage of quantitative data is that it can be verified and conveniently evaluated by researchers. This allows for replicating the research outcomes. In addition, even qualitative data can be quantified and converted to numbers. For example, the use of the Likert scale allows researchers not only to properly assess respondents’ perceptions of and attitudes towards certain phenomena but also to assign a code to each individual response and make it suitable for graphical and statistical analysis. It is also possible to convert the yes/no responses to dummy variables to present them in the form of numbers. Quantitative data is typically analysed using dissertation data analysis software such as Eviews, Matlab, Stata, R, and SPSS.

On the other hand, a significant limitation of purely quantitative methods is that social phenomena explored in economic and behavioural sciences are often complex, so the use of quantitative data does not allow for thoroughly analysing these phenomena. That is, quantitative data can be limited in terms of breadth and depth as compared to qualitative data, which may allow for richer elaboration on the context of the study.

1.1.2. Qualitative Data

Studies that use this type of data usually ask the ‘Why’ and ‘How’ questions (e.g. Why does social media marketing is more effective than traditional marketing? How do consumers make their purchase decisions?). This is non-numerical primary data represented mostly by opinions of relevant persons.

Qualitative data also includes any textual or visual data (infographics) that have been gathered from reports, websites and other secondary sources that do not involve interactions between the researcher and human participants. Examples of the use of secondary qualitative data are texts, images and diagrams you can use in SWOT analysis, PEST analysis, 4Ps analysis, Porter’s Five Forces analysis, most types of Strategic Analysis, etc. Academic articles, journals, books, and conference papers are also examples of secondary qualitative data you can use in your study.

The analysis of qualitative data usually provides deep insights into the phenomenon or issue being under study because respondents are not limited in their ability to give detailed answers. Unlike quantitative research, collecting and analysing qualitative data is more open-ended in eliciting the anecdotes, stories, and lengthy descriptions and evaluations people make of products, services, lifestyle attributes, or any other phenomenon. This is best used in social studies including management and marketing.

It is not always possible to summarise qualitative data as opinions expressed by individuals are multi-faceted. This to some extent limits the dissertation data analysis  as it is not always possible to establish cause-and-effect links between factors represented in a qualitative manner. This is why the results of qualitative analysis can hardly be generalised, and case studies that explore very narrow contexts are often conducted.

For qualitative data analysis, you can use tools such as nVivo and Tableau.  

1.2. Primary vs Secondary Research

1.2.1. primary data.

Primary data is data that had not existed prior to your research and you collect it by means of a survey or interviews for the dissertation data analysis chapter. Interviews provide you with the opportunity to collect detailed insights from industry participants about their company, customers, or competitors. Questionnaire surveys allow for obtaining a large amount of data from a sizeable population in a cost-efficient way. Primary data is usually cross-sectional data (i.e., the data collected at one point of time from different respondents). Time-series are found very rarely or almost never in primary data. Nonetheless, depending on the research aims and objectives, certain designs of data collection instruments allow researchers to conduct a longitudinal study.

1.2.2. Secondary data

This data already exist before the research as they have already been generated, refined, summarized and published in official sources for purposes other than those of your study study. Secondary data often carries more legitimacy as compared to primary data and can help the researcher verify primary data. This is the data collected from databases or websites; it does not involve human participants. This can be both cross-sectional data (e.g. an indicator for different countries/companies at one point of time) and time-series (e.g. an indicator for one company/country for several years). A combination of cross-sectional data and time-series data is panel data. Therefore, all a researcher needs to do is to find the data that would be most appropriate for attaining the research objectives.

Examples of secondary quantitative data are share prices; accounting information such as earnings, total asset, revenue, etc.; macroeconomic variables such as GDP, inflation, unemployment, interest rates, etc.; microeconomic variables such as market share, concentration ratio, etc. Accordingly, dissertation topics that will most likely use secondary quantitative data are FDI dissertations, Mergers and Acquisitions dissertations, Event Studies, Economic Growth dissertations, International Trade dissertations, Corporate Governance dissertations.

Two main limitations of secondary data are the following. First, the freely available secondary data may not perfectly suit the purposes of your study so that you will have to additionally collect primary data or change the research objectives. Second, not all high-quality secondary data is freely available. Good sources of financial data such as WRDS, Thomson Bank Banker, Compustat and Bloomberg all stipulate pre-paid access which may not be affordable for a single researcher.

1.3. Quantitative or Qualitative Research… or Both?

Once you have formulated your research aim and objectives and reviewed the most relevant literature in your field, you should decide whether you need qualitative or quantitative data.

If you are willing to test the relationship between variables or examine hypotheses and theories in practice, you should rather focus on collecting quantitative data. Methodologies based on this data provide cut-and-dry results and are highly effective when you need to obtain a large amount of data in a cost-effective manner. Alternatively, qualitative research will help you better understand meanings, experience, beliefs, values and other non-numerical relationships.

While it is totally okay to use either a qualitative or quantitative methodology, using them together will allow you to back up one type of data with another type of data and research your topic in more depth. However, note that using qualitative and quantitative methodologies in combination can take much more time and effort than you originally planned.

2. Types of Analysis

2.1. basic statistical analysis.

The type of statistical analysis that you choose for the results and findings chapter depends on the extent to which you wish to analyse the data and summarise your findings. If you do not major in quantitative subjects but write a dissertation in social sciences, basic statistical analysis will be sufficient. Such an analysis would be based on descriptive statistics such as the mean, the median, standard deviation, and variance. Then, you can enhance the statistical analysis with visual information by showing the distribution of variables in the form of graphs and charts. However, if you major in a quantitative subject such as accounting, economics or finance, you may need to use more advanced statistical analysis.

2.2. Advanced Statistical Analysis

In order to run an advanced analysis, you will most likely need access to statistical software such as Matlab, R or Stata. Whichever program you choose to proceed with, make sure that it is properly documented in your research. Further, using an advanced statistical technique ensures that you are analysing all possible aspects of your data. For example, a difference between basic regression analysis and analysis at an advanced level is that you will need to consider additional tests and deeper explorations of statistical problems with your model. Also, you need to keep the focus on your research question and objectives as getting deeper into statistical details may distract you from the main aim. Ultimately, the aim of your dissertation is to find answers to the research questions that you defined.

Another important aspect to consider here is that the results and findings section is not all about numbers. Apart from tables and graphs, it is also important to ensure that the interpretation of your statistical findings is accurate as well as engaging for the users. Such a combination of advanced statistical software along with a convincing textual discussion goes a long way in ensuring that your dissertation is well received. Although the use of such advanced statistical software may provide you with a variety of outputs, you need to make sure to present the analysis output properly so that the readers understand your conclusions.

3. Examples of Methods of Analysis

3.1. event study.

If you are studying the effects of particular events on prices of financial assets, for example, it is worth to consider the Event Study Methodology. Events such as mergers and acquisitions, new product launches, expansion into new markets, earnings announcements and public offerings can have a major impact on stock prices and valuation of a firm. Event studies are methods used to measure the impact of a particular event or a series of events on the market value. The concept behind this is to try to understand whether sudden and abnormal stock returns can be attributed to market information pertaining to an event.

Event studies are based on the efficient market hypothesis. According to the theory, in an efficient capital market, all the new and relevant information is immediately reflected in the respective asset prices. Although this theory is not universally applicable, there are many instances in which it holds true. An event study implies a step-by-step analysis of the impact that a particular announcement has on a company’s valuation. In normal conditions, without the influence of the analysed event, it is assumed that expected returns on a stock would be determined by the risk-free rate, systematic risk of the stock and risk premium required by investors. These conditions are measured by the capital asset pricing model (CAPM).

There can primarily be three types of announcements which can constitute event studies. These include corporate announcements, macroeconomic announcements, as well as regulatory events. As the name suggests, corporate announcements could include bankruptcies, asset sales, M&As, credit rating downgrades, earnings announcements and announcements of dividends. These events usually have a major impact on stock prices simply because they are directly interlinked with the company. Macroeconomic announcements can include central bank announcements of changes in interest rates, an announcement of inflation projections and economic growth projections. Finally, regulatory announcements such as policy changes and new laws announcement can also impact the stock prices of companies, and therefore can be measured using the method of event studies.

A critical issue in event studies is choosing the right event window during which the analysed announcements are assumed to produce the strongest effect on share prices. According to the efficient market hypothesis, no statistically significant abnormal returns connected with any events would be expected. However, in reality, there could be rumours before official announcements and some investors may act on such rumours. Moreover, investors may react at different times due to differences in speed of information processing and reaction. In order to account for all these factors, event windows usually capture a short period before the announcement to account for rumours and an asymmetrical period after the announcement.

In order to make event studies stronger and statistically meaningful, a large number of similar or related cases are analysed. Then, abnormal returns are cumulated, and their statistical significance is assessed. The t-statistic is often used to evaluate whether the average abnormal returns are different from zero. So, researchers who use event studies are concerned not only with the positive or negative effects of specific events but also with the generalisation of the results and measuring the statistical significance of abnormal returns.

3.2. Regression Analysis

Regression analysis is a mathematical method applied to determine how explored variables are interconnected. In particular, the following questions can be answered. Which factors are the most influential ones? Which of them can be ignored? How do the factors interact with one another? And the main question, how significant are the findings?

The type most often applied in the dissertation studies is the ordinary least squares (OLS) regression analysis that assesses parameters of linear relationships between explored variables. Typically, three forms of OLS analysis are used.

Longitudinal analysis is applied when a single object with several characteristics is explored over a long period of time. In this case, observations represent the changes of the same characteristics over time. Examples of longitudinal samples are macroeconomic parameters in a particular country, preferences and changes in health characteristics of particular persons during their lives etc. Cross-sectional studies on the contrary, explore characteristics of many similar objects such as respondents, companies, countries, students over cities in a certain moment of time. The main similarity between longitudinal and cross-sectional studies is that the data over one dimension, namely across periods of time (days, weeks, years) or across objects, respectively.

However, it is often the case that we need to explore data that change over two dimensions, both across objects and periods of time. In this case, we need to use a panel regression analysis. Its main distinction from the two mentioned above is that specifics of each object (person, company, country) are accounted for.

The common steps of the regression analysis are the following:

• Start with descriptive statistics of the data. This is done to indicate the scope of the data observations included in the sample and identify potential outliers. A common practice is to get rid of the outliers to avoid the distortion of the analysis results.
• Estimate potential multicollinearity. This phenomenon is connected with strong correlation between explanatory variables. Multicollinearity is an undesirable feature of the sample as regression results, in particular the significance of certain variables, may be distorted. Once multicollinearity is detected, the easiest way to eliminate it is to omit one of the correlated variables.
• Run Regressions. First, the overall significance of the model is estimated using the F-statistic. After that, the significance of particular variable coefficient is assessed using t-statistics.
• Don’t forget about diagnostic tests. They are conducted to detect potential imperfections of the sample that could affect the regression outcomes.

Some nuances should be mentioned. When a time series OLS regression analysis is conducted, it is feasible to conduct a full battery of diagnostic tests including the test of linearity (the relationship between the independent and dependent variables should be linear); homoscedasticity (regression residuals should have the same variance); independence of observations; normality of variables; serial correlation (there should no patterns in a particular time series). These tests for longitudinal regression models are available in most software tools such as Eviews and Stata.

3.3. Vector Autoregression

A vector autoregression model (VAR) is a model often used in statistical analysis, which explores interrelationships between several variables that are all treated as endogenous. So, a specific trait of this model is that it includes lagged values of the employed variables as regressors. This allows for estimating not only the instantaneous effects but also dynamic effects in the relationships up to n lags.

In fact, a VAR model consists of k OLS regression equations where k is the number of employed variables. Each equation has its own dependent variable while the explanatory variables are the lagged values of this variable and other variables.

• Selection of the optimal lag length

Information criteria (IC) are employed to determine the optimal lag length. The most commonly used ones are the Akaike, Hannah-Quinn and Schwarz criteria.

• Test for stationarity

A widely used method for estimating stationarity is the Augmented Dickey-Fuller test and the Phillips-Perron test.  If a variable is non-stationary, the first difference should be taken and tested for stationarity in the same way.

• Cointegration test

The variables may be non-stationary but integrated of the same order. In this case, they can be analysed with a Vector Error Correction Model (VECM) instead of VAR. The Johansen cointegration test is conducted to check whether the variables integrated of the same order share a common integrating vector(s). If the variables are cointegrated, VECM is applied in the following analysis instead of a VAR model. VECM is applied to non-transformed non-stationary series whereas VAR is run with transformed or stationary inputs.

• Model Estimation

A VAR model is run with the chosen number of lags and coefficients with standard errors and respective t-statistics are calculated to assess the statistical significance.

• Diagnostic tests

Next, the model is tested for serial correlation using the Breusch-Godfrey test, for heteroscedasticity using the Breusch-Pagan test and for stability.

• Impulse Response Functions (IRFs)

The IRFs are used to graphically represent the results of a VAR model and project the effects of variables on one another.

• Granger causality test

The variables may be related but there may exist no causal relationships between them, or the effect may be bilateral. The Granger test indicates the causal associations between the variables and shows the direction of causality based on interaction of current and past values of a pair of variables in the VAR system.

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• Getting started
• Criteria for a problem formulation
• Find who and what you are looking for
• Too broad, too narrow, or o.k.?
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Analysing data

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• Research methods /
• Lesson 1: Qualitative and quan… /

Just as there are different methods for collecting quantitative data and qualitative data, there are different ways of analysing the data collected.

Learn more about quantitative and qualitative methods and their strengths and limitations in the following pages.

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Data analysis and findings

Data analysis is the most crucial part of any research. Data analysis summarizes collected data. It involves the interpretation of data gathered through the use of analytical and logical reasoning to determine patterns, relationships or trends. 

Data Analysis Checklist

Cleaning  data

* Did you capture and code your data in the right manner?

*Do you have all data or missing data?

* Do you have enough observations?

* Do you have any outliers? If yes, what is the remedy for outlier?

* Does your data have the potential to answer your questions?

Analyzing data

* Visualize your data, e.g. charts, tables, and graphs, to mention a few.

*  Identify patterns, correlations, and trends

* Test your hypotheses

* Let your data tell a story

Reports the results

* Communicate and interpret the results

* Conclude and recommend

* Your targeted audience must understand your results

* Use more datasets and samples

* Use accessible and understandable data analytical tool

* Do not delegate your data analysis

* Clean data to confirm that they are complete and free from errors

* Analyze cleaned data

* Understand your results

* Keep in mind who will be reading your results and present it in a way that they will understand it

* Share the results with the supervisor oftentimes

Past presentations

• PhD Writing Retreat - Analysing_Fieldwork_Data by Cori Wielenga A clear and concise presentation on the ‘now what’ and ‘so what’ of data collection and analysis - compiled and originally presented by Cori Wielenga.

Online Resources

• Qualitative analysis of interview data: A step-by-step guide
• Qualitative Data Analysis - Coding & Developing Themes

Recommended Quantitative Data Analysis books

Recommended Qualitative Data Analysis books

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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.

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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

It involves  data collection  of your related topic for research. Carefully analyze the data that tends to be suitable for your analysis. Do not just go with irrelevant data leading to complications in the results. Your data must be relevant and fit with your objectives. You must be aware of how the data is going to help in analysis. 

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.

Data analysis involves two approaches –  Qualitative Data Analysis and Quantitative Data Analysis.   Qualitative data analysis  comprises research through experiments, focus groups, and interviews. This approach helps to achieve the objectives by identifying and analyzing common patterns obtained from responses. 

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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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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• Cookies & Privacy
• 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 .

How do I make a data analysis for my bachelor, master or PhD thesis?

A data analysis is an evaluation of formal data to gain knowledge for the bachelor’s, master’s or doctoral thesis. The aim is to identify patterns in the data, i.e. regularities, irregularities or at least anomalies.

Data can come in many forms, from numbers to the extensive descriptions of objects. As a rule, this data is always in numerical form such as time series or numerical sequences or statistics of all kinds. However, statistics are already processed data.

Data analysis requires some creativity because the solution is usually not obvious. After all, no one has conducted an analysis like this before, or at least you haven't found anything about it in the literature.

The results of a data analysis are answers to initial questions and detailed questions. The answers are numbers and graphics and the interpretation of these numbers and graphics.

What are the advantages of data analysis compared to other methods?

• Numbers are universal
• The data is tangible.
• There are algorithms for calculations and it is easier than a text evaluation.
• The addressees quickly understand the results.
• You can really do magic and impress the addressees.
• It’s easier to visualize the results.

What are the disadvantages of data analysis?

• Garbage in, garbage out. If the quality of the data is poor, it’s impossible to obtain reliable results.
• The dependency in data retrieval can be quite annoying. Here are some tips for attracting participants for a survey.
• You have to know or learn methods or find someone who can help you.
• Mistakes can be devastating.
• Missing substance can be detected quickly.
• Pictures say more than a thousand words. Therefore, if you can’t fill the pages with words, at least throw in graphics. However, usually only the words count.

Under what conditions can or should I conduct a data analysis?

• If I have to.
• You must be able to get the right data.
• If I can perform the calculations myself or at least understand, explain and repeat the calculated evaluations of others.
• You want a clear personal contribution right from the start.

How do I create the evaluation design for the data analysis?

The most important thing is to ask the right questions, enough questions and also clearly formulated questions. Here are some techniques for asking the right questions:

Good formulation: What is the relationship between Alpha and Beta?

Poor formulation: How are Alpha and Beta related?

Now it’s time for the methods for the calculation. There are dozens of statistical methods, but as always, most calculations can be done with only a handful of statistical methods.

• Which detailed questions can be formulated as the research question?
• What data is available? In what format? How is the data prepared?
• Which key figures allow statements?
• What methods are available to calculate such indicators? Do my details match? By type (scales), by size (number of records).
• Do I not need to have a lot of data for a data analysis?

It depends on the media, the questions and the methods I want to use.

A fixed rule is that I need at least 30 data sets for a statistical analysis in order to be able to make representative statements about the population. So statistically it doesn't matter if I have 30 or 30 million records. That's why statistics were invented...

What mistakes do I need to watch out for?

• Don't do the analysis at the last minute.
• Formulate questions and hypotheses for evaluation BEFORE data collection!
• Stay persistent, keep going.
• Leave the results for a while then revise them.
• You have to combine theory and the state of research with your results.
• You must have the time under control

Which tools can I use?

You can use programs of all kinds for calculations. But asking questions is your most powerful aide.

Who can legally help me with a data analysis?

The great intellectual challenge is to develop the research design, to obtain the data and to interpret the results in the end.

Am I allowed to let others perform the calculations?

That's a thing. In the end, every program is useful. If someone else is operating a program, then they can simply be seen as an extension of the program. But this is a comfortable view... Of course, it’s better if you do your own calculations.

A good compromise is to find some help, do a practical calculation then follow the calculation steps meticulously so next time you can do the math yourself. Basically, this functions as a permitted training. One can then justify each step of the calculation in the defense.

What's the best place to start?

Clearly with the detailed questions and hypotheses. These two guide the entire data analysis. So formulate as many detailed questions as possible to answer your main question or research question. You can find detailed instructions and examples for the formulation of these so-called detailed questions in the Thesis Guide.

How does the Aristolo Guide help with data evaluation for the bachelor’s or master’s thesis or dissertation?

The Thesis Guide or Dissertation Guide has instructions for data collection, data preparation, data analysis and interpretation. The guide can also teach you how to formulate questions and answer them with data to create your own experiment. We also have many templates for questionnaires and analyses of all kinds. Good luck writing your text! Silvio and the Aristolo Team PS: Check out the Thesis-ABC and the Thesis Guide for writing a bachelor or master thesis in 31 days.