15/06/ · Data Analysis and Findings. Reporting your findings is a huge part of your blogger.com is what makes up the bulk of your research as well as what the majority of your research viewers want to see; not your introduction, analysis, or abstract but your findings and the data gathered Quantitative data. The purpose of the results section of the thesis is to report the findings of your research. You usually present the data you obtained in appropriate figures (diagrams, graphs, tables and photographs) and you then comment on this data. Comments on figures and tables (data commentary) usually have the following elements The results chapter (also referred to as the findings or analysis chapter) is one of the most important chapters of your dissertation or thesis because it shows the reader what you’ve found in terms of the quantitative data you’ve collected. It presents the data using a clear text narrative, supported by tables, graphs and charts. In doing so, it also highlights any potential issues (such as outliers or unusual
Dissertation Results & Findings Chapter (Qualitative) - Grad Coach
By: Derek Jansen MBA, data analysis findings dissertation. Expert Reviewed By: Kerryn Warren PhD July But where do you start? It presents the data using a clear text narrative, supported by tables, graphs and charts. Well, in the results chapter, data analysis findings dissertation only present your statistical findings. Only the numbers, so to speak — no more, no less. Contrasted to this, in the discussion chapterdata analysis findings dissertation, you interpret your findings and link them to prior research i.
your literature reviewas well as your research objectives and research questions. In other words, the results chapter presents and describes the data, while the discussion chapter interprets the data. In your results chapter, you may have a plot that shows how respondents to a survey may have responded to your survey: the numbers of respondents per category, for instance. You may also state whether this supports a hypothesis by using a p-value from a statistical test.
But it is only in the discussion chapter where you will say why this is relevant or how it compares with the literature or the broader picture. Even so, it is good practice to separate the results and discussion elements within the chapter, as this ensures your findings are fully described.
Typically, though, the results and discussion chapters are split up in quantitative studies. Relevance is key. But the most important thing is to ensure your results reflect and align with the purpose of your study. So, you need to revisit your research aims, objectives and research questions and use these as a litmus test for relevance. Make sure that you refer back to these constantly when writing up your chapter so that you stay on track.
Importantly, your results chapter needs to lay the foundation for your discussion chapter. This means that, in your results chapter, you need to include all the data that you will use as the basis for your interpretation in the discussion chapter. For example, if you plan to highlight the strong relationship between Variable X and Variable Y in your discussion chapter, you need to present the respective analysis in your results chapter — perhaps a correlation or regression analysis.
See how Grad Coach can help you Book A Free Consultation How do I write the results chapter? There are multiple steps involved in writing up the results chapter for your quantitative research. The exact number of steps applicable to you will vary from study to study and will depend on the nature of the research aims, objectives and research questions.
The first step in writing your results chapter is to revisit your research objectives and research questions. These will be or at least, should be! the driving force behind your results and discussion chapters, so you need to review them and then ask yourself data analysis findings dissertation statistical analyses and tests from your mountain of data would specifically help you address these. For each research objective and research question, list the specific piece or pieces of analysis that address it, data analysis findings dissertation.
Again, list your points and then list the specific piece of analysis that addresses each point. Next, you should draw up a rough outline of how you plan to structure your chapter. Which analyses and statistical tests will you present and in what order? The purpose of this is to assess how representative the sample is of the broader population.
This is important for the sake of the generalisability of the results. If your sample is not representative of the population, you will not be able to generalise your findings. So, make sure that you design your survey to capture the correct demographic information that you will compare your sample to. Well, even if your purpose is not necessarily to extrapolate your findings to the broader population, understanding your sample will allow you to interpret your findings appropriately, considering who responded.
In other words, it will help you contextualise your findings. The first is when you have multiple 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 different ways.
In other words, in a survey, these four scales should all receive similar ratings. Internal consistency is not guaranteed though especially if you developed the measures yourselfso you need to assess the reliability of each composite measure using a test. A high alpha score means that your measure is internally consistent. A low alpha score means you may need to consider scrapping one data analysis findings dissertation more of the measures.
The second matter that you should address early on in your results chapter is data shape. In other words, you need to assess whether the data in your set are symmetrical i. normally distributed or not, data analysis findings dissertation, as data analysis findings dissertation will directly impact what type of analyses you can use. The first step is to present the descriptive statistics for your variables.
A large table that indicates all the above for multiple variables can be a very effective way to present your data economically. You can also use colour coding to help make the data more easily digestible. For categorical data, data analysis findings dissertation, where you show the percentage of people who chose or fit into a category, for instance, you can either just plain describe the percentages or numbers of people who responded to something or use graphs and charts such as bar graphs and pie charts to present your data in this section of the chapter.
When using figures, make sure that you label them simply data analysis findings dissertation clearlyso that your reader can easily understand them. Figures and tables should complement the writing, not carry it. Depending on your research aims, objectives and research questions, you may stop your analysis at this point i. descriptive statistics. Inferential statistics are used to make generalisations about a populationdata analysis findings dissertation, whereas descriptive statistics focus purely on the sample.
Inferential statistical techniques, broadly speaking, can be data analysis findings dissertation down into two groups. First, there are those that compare measurements between groupssuch as t-tests which measure differences between two groups and ANOVAs which measure differences between multiple groups.
Second, there are techniques that assess the relationships between variablessuch as correlation analysis and regression analysis. Within each of these, some tests can be used for normally distributed parametric data and some tests are designed specifically for use on non-parametric data. Ultimately, the most important thing is to make sure that you adopt the tests and techniques that allow you to achieve data analysis findings dissertation research objectives and answer your research questions.
In this section of the results chapter, you should try to make use of figures and visual components as effectively as possible, data analysis findings dissertation. For example, if you present a correlation table, data analysis findings dissertation, use colour coding to highlight the significance of the correlation values, or scatterplots to visually demonstrate what the trend is.
If your study requires it, the next stage is hypothesis testing. A hypothesis is a statementoften indicating a difference between groups or relationship between variables, that can be supported or rejected by a statistical test. Finally, if the aim of your study is to develop and test a theoretical frameworkthis is the time to present it, following the testing of your hypotheses.
To wrap up your results chapter and transition to the discussion chapter, you should provide a brief summary of the key findings. Highlight the findings most relevant to your research objectives and research questions, and wrap it up.
This post is part of our research writing mini-course, which covers everything you need to get started with your dissertation, thesis or research project.
Your email address will not be published. Save my name, email, and website in this browser for the next time I comment. What exactly is the results chapter? What should you include in the results chapter? Need a helping hand? Book A Free Consultation. How do I write the results chapter? Step 1 — Revisit your research questions The first step in writing your results chapter is to revisit your research objectives and research questions.
For example: What age range are they? How is gender distributed? How is ethnicity distributed? What areas do the participants live in? Most commonly, there are two areas you need to pay attention to: 1: Composite measures The first is when you have multiple scale-based measures that combine to capture one construct — this is called a composite measure. For scaled data, this usually includes statistics such as: The mean — this is simply the mathematical average of a range of numbers.
The median — this is the midpoint in a range of numbers when the numbers are arranged in order. The mode — this is the most commonly repeated number in the data set, data analysis findings dissertation. Standard deviation — this metric indicates how dispersed a range of numbers is. In other words, how close all the numbers are to the mean the average. Skewness — this indicates how symmetrical a range of numbers is.
In other words, do they tend to cluster into data analysis findings dissertation smooth bell curve shape in the middle of the graph this is called a normal or parametric distributionor do they lean to the left or right this is called a non-normal or non-parametric distribution. Kurtosis — this metric indicates whether the data are heavily or lightly-tailed, relative to the normal distribution.
In other words, how peaked or flat the distribution is. Step 6 — Present the inferential statistics Inferential statistics are used to make generalisations about a populationwhereas descriptive statistics focus purely on data analysis findings dissertation sample. Step 7 — Test your hypotheses If your study requires it, the next stage is hypothesis testing. Specify your alternative hypothesis e.
Step 8 — Provide a chapter summary To wrap up your results chapter and transition to the discussion chapter, you should provide a brief summary of the key findings.
Structure your results chapter systematically and sequentially. If you had two experiments where findings from the one generated inputs into the other, report on them in order. Make your own tables and graphs rather than copying and pasting them from statistical analysis programmes like SPSS. Check out the DataIsBeautiful reddit for some inspiration.
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This can include the literature review, analysis of findings, methodology, and discussion and conclusion - but ultimately it is up to you. News and events 17/05/06 Ever thought how much time and effort it would take to write a dissertation? The results chapter (also referred to as the findings or analysis chapter) is one of the most important chapters of your dissertation or thesis because it shows the reader what you’ve found in terms of the quantitative data you’ve collected. It presents the data using a clear text narrative, supported by tables, graphs and charts. In doing so, it also highlights any potential issues (such as outliers or unusual Aug 11, · It is vitally important to focus only on clear and precise observations. The findings chapter of the dissertation is theoretically the easiest to write. It includes statistical analysis and a brief write-up about whether or not the results emerging as a result of analysis are blogger.comted Reading Time: 12 mins
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