**How To Write The Results/Findings Chapter**

### For Quantitative Studies (Dissertations & Theses)

*By: Derek Jansen (MBA). Expert Reviewed By: Kerryn Warren (PhD) | July 2021*

So, you’ve completed your quantitative data analysis and it’s time to report on your findings. **But where do you start?** In this post, we’ll walk you through the **results chapter** (also called the findings or analysis chapter), step by step, so that you can craft this section of your dissertation or thesis with confidence. If you’re looking for information regarding the results chapter for qualitative studies, you can find that here.

**What exactly is the results chapter?**

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 findings) you’ve come across.

**But how’s that different from the discussion chapter?**

Well, in the results chapter, **you** **only present** your statistical findings. Only the numbers, so to speak – no more, no less. Contrasted to this, in the discussion chapter, **you interpret your findings** and link them to prior research (i.e. your literature review), as 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.

Let’s look at an example.

In your results chapter, you may have a plot that shows how respondents to a survey responded: 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. So, in your results chapter, make sure that you **don’t present anything other than the hard facts** – this is not the place for subjectivity.

It’s worth mentioning that some universities prefer you to **combine the results and discussion** chapters. 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. If you’re unsure, **chat with your research supervisor** or chair to find out what their preference is.

**What should you include in the results chapter?**

Following your analysis, it’s likely you’ll have **far more data than are necessary** to 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.

This doesn’t mean that those analyses were a waste of time – on the contrary, those analyses ensure that you have a good understanding of your dataset and how to interpret it. However, that doesn’t mean your reader or examiner needs to see the 165 histograms you created! Relevance is key.

**How do I decide what’s relevant?**

At this point, it can be difficult to strike a balance between what is and isn’t important. 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.

As a general guide, your results chapter will typically include the following:

- Some
**demographic**data about your sample **Reliability**tests (if you used measurement scales)**Descriptive**statistics**Inferential**statistics (if your research objectives and questions require these)**Hypothesis**tests (again, if your research objectives and questions require these)

We’ll discuss each of these points in more detail in the next section.

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.

**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**. However, we’ll outline the generic steps below.

**Step 1 – Revisit your 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 which 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.

At this stage, it’s also useful to think about the key points that you want to raise in your discussion chapter and note these down so that you have a clear reminder of which data points and analyses you want to highlight in the results chapter. 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? We’ll discuss the “standard structure” in more detail later, but it’s worth mentioning now that it’s always useful to draw up a rough outline before you start writing (this advice applies to any chapter).

**Step 2 – Craft an overview introduction**

As with all chapters in your dissertation or thesis, you should start your quantitative results chapter by providing **a brief overview of what you’ll do** in the chapter and **why**. For example, you’d explain that you will start by presenting demographic data to understand the representativeness of the sample, before moving onto X, Y and Z.

This section shouldn’t be lengthy – a paragraph or two maximum. Also, it’s a good idea to weave the research questions into this section so that there’s a **golden thread that runs through** the document.

**Step 3 – Present the sample demographic data**

The first set of data that you’ll present is an overview of the **sample demographics** – in other words, the demographics of your respondents.

For example:

- What age range are they?
- How is gender distributed?
- How is ethnicity distributed?
- What areas do the participants live in?

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. This is not necessarily the end of the world, but it is a limitation you’ll need to acknowledge.

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

**But what if I’m not interested in generalisability?**

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**. For example, if 80% of your sample was aged over 65, this may be a significant contextual factor to consider when interpreting the data. Therefore, it’s important to **understand and present** the demographic data.

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

Before you undertake any statistical analysis, you’ll need to do some checks to ensure that **your data are suitable** for the analysis methods and techniques you plan to use. If you try to analyse data that doesn’t meet the assumptions of a specific statistical technique, your results will be largely meaningless. Therefore, you may need to show that the methods and techniques you’ll use are “allowed”.

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 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. This is called “**internal consistency**”.

Internal consistency is not guaranteed though (especially if you developed the measures yourself), so you **need to assess the reliability** of each composite measure using a test. Typically, Cronbach’s Alpha is a common test used to assess internal consistency – i.e., to show that the items you’re combining are more or less saying the same thing. A high alpha score means that your measure is internally consistent. A low alpha score means you may need to consider scrapping one or more of the measures.

**#2: Data shape**

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.e. normally distributed) or not, as this will directly impact what type of analyses you can use. For many common inferential tests such as T-tests or ANOVAs (we’ll discuss these a bit later), your data needs to be normally distributed. If it’s not, you’ll need to adjust your strategy and use alternative tests.

To assess the shape of the data, you’ll usually assess a variety of **descriptive statistics** (such as the mean, median and skewness), which is what we’ll look at next.

**Step 5 – Present the descriptive statistics**

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

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. - 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 a smooth bell curve shape in the middle of the graph (this is called a normal or parametric distribution), or 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.

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, 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 and clearly**, 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 text content needs to **present a clear narrative** that can stand on its own. In other words, don’t rely purely on your figures and tables to convey your key points: highlight the crucial trends and values in the text. 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.e. descriptive statistics). However, if your study requires inferential statistics, then it’s** time to deep dive into those**.

**Step 6 – Present the inferential statistics**

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

First, there are those that **compare measurements between groups**, such 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 variables**, such 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.

There are a seemingly endless number of tests that you can use to crunch your data, so it’s easy to run down a rabbit hole and end up with piles of test data. Ultimately, the most important thing is to make sure that you adopt the tests and techniques that allow you to achieve your **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. For example, if you present a correlation table, use colour coding to highlight the significance of the correlation values, or scatterplots to visually demonstrate what the trend is. The **easier you make it for your reader to digest **your findings, the more effectively you’ll be able to make your arguments in the next chapter.

**Step 7 – Test your hypotheses**

If your study requires it, the next stage is hypothesis testing. A **hypothesis is a statement**, often indicating a difference between groups or relationship between variables, that can be supported or rejected by a statistical test. However, not all studies will involve hypotheses (again, it depends on the research objectives), so don’t feel like you “must” present and test hypotheses just because you’re undertaking quantitative research.

The basic **process for hypothesis testing** is as follows:

- Specify your null hypothesis (for example, “The chemical psilocybin has no effect on time perception).
- Specify your alternative hypothesis (e.g., “The chemical psilocybin has an effect on time perception)
- Set your significance level (this is usually 0.05)
- Calculate your statistics and find your p-value (e.g., p=0.01)
- Draw your conclusions (e.g., “The chemical psilocybin does have an effect on time perception”)

Finally, if the aim of your study is to develop and test a **conceptual framework**, this is the time to present it, following the testing of your hypotheses. While you don’t need to develop or discuss these findings further in the results chapter, indicating whether the tests (and their p-values) support or reject the hypotheses is crucial.

**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**. “Brief” is the keyword here – much like the chapter introduction, this shouldn’t be lengthy – a paragraph or two maximum. Highlight the findings most relevant to your research objectives and research questions, and wrap it up.

**Some final thoughts, tips and tricks**

Now that you’ve got the essentials down, here are a few tips and tricks to make your quantitative results chapter shine:

- When writing your results chapter, report your findings in the
**past tense**. You’re talking about what you’ve found in your data, not what you are currently looking for or trying to find. - 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. - Once you’re done writing, review your work to make sure that you have provided
**enough information to answer your research questions**, but also that you didn’t include superfluous information.

If you’ve got any questions about writing up the quantitative results chapter, please leave a comment below. If you’d like 1-on-1 assistance with your quantitative analysis and discussion, check out our hands-on coaching service, or book a free consultation with a friendly coach.

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Thank you. I will try my best to write my results.

Awesome content 👏🏾

this was great explaination