How to Write a Thesis Results Section [For Quantitative Studies]

The results section is one of the most important parts of a dissertation/thesis. It is where you present the key findings of your research in a clear and organized way. This section focuses on what you discovered, without adding interpretations or personal opinions. Presenting your thesis results section clearly helps readers understand your study and its outcomes.

In this article, we will explain how to write the Results section step by step. The article will specifically focus on writing the results section for quantitative studies. You will also learn how to structure your findings, use tables and charts effectively, and avoid common mistakes. By following these guidelines, you can create a Results section that is clear, professional, and easy to understand.

What is a Thesis Results Section?

The Results section is a key part of a dissertation/thesis where you present the main findings of your research. It provides a factual and objective account of the data you collected, focusing only on what was discovered during your study. This section answers your research questions or hypotheses by showcasing the evidence, whether it involves statistical outcomes, themes from interviews, or a combination of both. It does not include explanations, interpretations, or discussions about the meaning of the results—those belong in the Discussion section.

Thus, the main aim of the Results section is to give readers a clear understanding of your findings. It organizes the data in a logical way and often includes tables, graphs, or charts to enhance clarity. This section sets the foundation for later interpretation, so precision and accuracy are critical.

What’s the Difference Between the Results and Discussion Chapters?

The Results and Discussion chapters serve distinct purposes in a dissertation/thesis, but they often confuse writers. The Results chapter focuses on presenting your findings objectively and systematically. It answers your research questions by showing the evidence. This chapter avoids interpretation but only provides a factual account of what the data reveals.

In contrast, the Discussion chapter is where you interpret and analyze the findings. This chapter explains what the results mean in relation to your research objectives, existing literature, and broader implications. It also addresses unexpected findings, limitations, and potential future research.

To differentiate, think of the Results chapter as “what you found” and the Discussion chapter as “why it matters.” Keeping these sections separate ensures clarity and helps readers understand the progression from evidence to interpretation in your research.

Preparing to Write the Results Section of Your Dissertation/Thesis

Writing the Results section for your dissertation/thesis can feel overwhelming, especially with a large amount of data to present. However, with the right approach, you can craft a section that is clear, concise, and impactful. Think of it as showcasing your discoveries—your opportunity to let the data speak for itself without interference. The key is preparation and organization.

Here are some best practices to help you get started:

• Use past tense when describing your findings, as they are based on completed research.
• Keep it concise. While the length depends on your data, include only the findings directly tied to your research questions.
• Avoid interpretations in this section. Words like “suggests” or “indicates” belong in the Discussion chapter.
• Organize findings logically. Start with the broadest results and move to more specific ones, maintaining a natural flow.
• If you have extra data that isn’t central to your research questions, consider adding it to an appendix or footnotes.
• Visual aids like tables, charts, and graphs can simplify complex findings but don’t overuse them.

Writing the Results Section: Step-by-Step Guide

The Results section of a dissertation/thesis is your chance to showcase the findings of your research. It’s where you reveal what your data says—without over-explaining or interpreting. This section should be clear, organized, and free of unnecessary details. Think of it as presenting the evidence before you explain what it means in the Discussion chapter. To make sure you do this effectively, follow this step-by-step guide that breaks down how to write a Results section specifically for quantitative research.

Step 1: Write An Overview (Introduction section)

The introduction to the Results section should be a concise paragraph that outlines what the section will cover. This sets the stage for the reader and provides context for the data that will follow. The introduction should explain the structure of the section and mention the key aspects of the results you will present. In quantitative research, this includes both descriptive and inferential statistics.

An effective overview introduces the key results and helps organize the section logically. For example, if you are reporting both descriptive statistics (like means and standard deviations) and inferential statistics (like p-values and t-tests), it’s useful to briefly mention which results will be discussed and in what order. This way, the reader knows whether to expect a discussion of group means first, followed by correlations, and then the results of statistical tests.

Your overview should be brief—usually no more than a few sentences—but it should provide a roadmap for how the Results chapter will be structured. Avoid including specific findings in this part; save those for the detailed presentation of results that follow.

Results Section Introduction Example

“In this section, we will present the findings of the study in relation to the research questions and hypotheses. The results will be organized to provide a clear and concise summary of the key outcomes derived from the data analysis. First, we will provide an overview of the sample demographics, followed by the presentation of descriptive statistics for the key variables. We will then move on to inferential statistics, reporting the results of the hypothesis tests conducted, including t-tests and regression analysis, to examine the relationships between variables. Each hypothesis will be tested, and the statistical significance of the findings will be discussed. The section will conclude with a brief summary of the key findings.”

Step 2: Revisit Your Research Questions or Hypotheses

Before diving into the actual writing of the Results section, it is essential to revisit your research questions or hypotheses. These form the foundation of your study and ensure that your results align with the objectives you set out to address. By focusing on these guiding questions, you ensure that your findings are presented in a way that is relevant to the overall aim of your research.

For example, if your hypothesis is that “Students who participate in daily exercise will score higher on standardized tests,” your Results section should focus on reporting data related to student performance and exercise habits. At this stage, do not interpret the results—simply report the data. For each research question or hypothesis, present the relevant findings in an organized manner.

Start by stating each hypothesis clearly and follow up with the results you obtained. Organize your findings based on the research questions. This helps readers easily trace the results back to the initial research goals. You should avoid presenting irrelevant data, keeping the focus tightly on answering the questions you posed at the beginning of your study.

Example:
In a study on the relationship between daily exercise and academic performance, your Results section might start by clearly restating the hypothesis: “This study hypothesized that daily exercise improves academic performance.” Then, you would follow with descriptive statistics that demonstrate the relationship, e.g., “Students who exercised daily had an average test score of 85.4, while those who did not had an average score of 78.2.”

Step 3: Present Demographics Distribution Results

In quantitative research, presenting your sample demographics is essential for contextualizing the results. This allows the reader to understand who participated in your study and whether the sample is representative of the population. This step includes demographic characteristics such as age, gender, socioeconomic status, education level, and any other relevant factors based on the scope of your research.

The demographic information should be clear, concise, and relevant to the analysis. It’s important to use tables, charts, or graphs to display this information, as they make it easier for the reader to absorb and compare the data. Keep in mind that you should not delve too deeply into irrelevant details, but focus on what is necessary for understanding the sample characteristics that may impact the findings.

The demographic section will often provide the foundation for later analyses, such as stratifying results by age or gender. For instance, if you’re studying the effects of a health intervention across different age groups, it is important to present how many participants belong to each group and their characteristics.

Example:
“The sample consisted of 150 participants, 70% of whom were female and 30% male. The age range was 18 to 65 years, with a mean age of 34.2 years (SD = 12.3). The majority of participants were employed full-time (78%), with 22% working part-time. These demographics are important as they may influence the generalizability of the study’s findings to other populations.”

Step 4: Review and Explain Composite Measures

When working with quantitative research, it’s often necessary to use composite measures , such as scales or indices, to summarize multiple variables. If your study includes any such composite measures, you should briefly explain how they were calculated. Additionally, you should review the distribution and shape of your data to identify any patterns or abnormalities.

For example, if you created a composite scale to measure job satisfaction using several survey items, you would describe how this index was constructed. You might also want to include information on whether the data is normally distributed, as this can inform the statistical tests you choose. Describing the “shape” of the data—whether it follows a normal distribution or has skewness or outliers—helps the reader understand the robustness of your analysis.

Example:
“A composite measure of job satisfaction was created by averaging responses to a 10-item survey. The internal consistency of the scale was confirmed with a Cronbach’s alpha of 0.87. Data analysis revealed a slightly negatively skewed distribution (skew = -0.24), indicating that most participants reported relatively high levels of job satisfaction.”

Step 5: Present Descriptive Statistics Results

Descriptive statistics serve to summarize your data and provide an overview of the central tendencies, variability, and overall distribution of the variables you’ve measured. This step is crucial for providing a snapshot of your sample and the key outcomes of your study. Descriptive statistics include measures such as the mean, median, standard deviation, range, and frequencies.

The aim is to present the data in a manner that allows the reader to quickly grasp the main trends or patterns without wading through complex details. It’s also helpful to use tables, bar charts, or histograms for easy visualization of these statistics.

In the Results section, descriptive statistics are often presented first, as they give readers an initial understanding of the data before diving into more complex inferential statistics.

Example:
“For the experimental group, the mean score on the post-test was 85.6 (SD = 9.4), while the control group had a mean score of 76.2 (SD = 10.1). The mean difference between the two groups was statistically significant, t(98) = 3.45, p < 0.01. The range of scores in the experimental group was 65 to 100, while the control group’s range was 55 to 92.”

Step 6: Present Inferential Statistics Results

Inferential statistics allow you to draw conclusions about the relationships between variables and assess the significance of your findings. This section should include the results of hypothesis tests (like t-tests, ANOVA, chi-square tests), regression models, and other statistical analyses you conducted. Be sure to include the necessary statistical values such as test statistics (e.g., t-values, F-values), degrees of freedom, p-values, and effect sizes.

The goal of this section is to report the outcomes of the tests you performed, highlighting whether the results are statistically significant. Always include the relevant statistical notation and provide a clear explanation of what each test is measuring, but avoid interpreting the results—that comes later in the Discussion chapter.

Example:
“A one-way ANOVA was conducted to compare test scores among three teaching methods. Results indicated that there was a significant difference between the groups, F(2, 147) = 5.62, p < 0.01. Post-hoc comparisons using the Tukey HSD test revealed that Method C was significantly more effective than Methods A and B.”

Step 7: Test Your Hypotheses and Present the Results

The results of your hypothesis tests should be presented clearly and directly. Each hypothesis should be addressed separately, with a clear statement of whether the data supports or rejects it. It’s important to avoid drawing conclusions or offering explanations at this point—simply present the findings related to each hypothesis.

If your hypotheses are based on relationships between variables, such as the effect of one variable on another, use appropriate statistical tests to assess these relationships. Be sure to include p-values, confidence intervals, and any other relevant statistics to support your conclusions about whether or not your hypotheses are confirmed.

Example:
“This study hypothesized that there would be a positive relationship between daily exercise and test performance. The results support this hypothesis, with a statistically significant correlation between hours of exercise and test scores, r(98) = 0.45, p < 0.01.”

Step 8: Write the Results Section Summary

To conclude the Results section, provide a brief summary of the key findings. This should highlight the most important outcomes of your analysis and provide a bridge to the Discussion chapter, where you will interpret these findings in the context of your research questions and existing literature. The summary should not include any interpretation or explanation, as these belong in the Discussion section.

The summary should be clear, concise, and focused on the key findings that directly answer your research questions or test your hypotheses. By summarizing your results in a straightforward manner, you set the stage for a more in-depth analysis of their meaning in the next chapter.

Example:
“In summary, the results indicate that daily exercise is significantly associated with higher test scores, supporting the hypothesis that physical activity improves academic performance. The following chapter will explore the implications of these findings in relation to existing research on exercise and cognitive function.”

Common Mistakes to Avoid in the Results Section

Writing the results section of your thesis can be challenging. In fact, many students make common mistakes that hinder the clarity and impact of their findings. However, by identifying and avoiding these pitfalls, you can present your results effectively and professionally. Below are the most common mistakes to avoid, along with tips on how to steer clear of them.

Mixing Results with Interpretations

One of the most frequent mistakes is combining results with interpretations or discussions. The results section should focus strictly on presenting data, leaving the interpretation for the discussion chapter. Mixing the two can confuse readers and make your argument less cohesive.

For example, if your analysis shows that students with higher study hours scored better on tests, this should be reported as: “The results indicate a positive correlation between study hours and test scores (r = 0.67, p < 0.05).” Avoid adding interpretive comments like: “This suggests that students who are more diligent achieve better academic outcomes.”

To avoid this mistake:

1. Stick to describing what the data shows.
2. Use clear, factual statements without drawing conclusions.
3. Keep interpretation notes separate while writing the results section.

This approach ensures that readers focus on your findings without being distracted by your interpretations, which they can expect in the discussion chapter.

Overwhelming Readers with Raw Data or Statistical Jargon

Presenting excessive raw data or complex statistical terms can overwhelm readers, especially those who are not experts in your field. While your data is critical, flooding the section with every detail can obscure your key findings.

For example, instead of listing the entire data set in a table, provide summaries such as: “The mean test score for Group A was 75.4 (SD = 5.2), compared to Group B’s mean of 68.7 (SD = 6.1).” Use graphs and tables sparingly to highlight the most important patterns and trends.

To avoid overwhelming your readers:

1. Focus on key findings relevant to your research questions.
2. Summarize data in a concise and readable manner.
3. Include detailed data in appendices for reference, if needed.

This way, your results section remains clear, concise, and accessible, helping readers focus on the insights you’ve uncovered.

Failing to Report Non-Significant Results

Another common mistake is ignoring non-significant results. While significant findings are exciting, non-significant results can also provide valuable insights and are essential for a complete and honest report of your research.

For example, if your hypothesis was that students who attended tutoring sessions would perform better, but the results showed no significant difference (p = 0.12), it is important to report this. You could write: “The analysis did not reveal a statistically significant difference in test scores between students who attended tutoring sessions (M = 72.3, SD = 8.1) and those who did not (M = 70.8, SD = 7.5).”

To avoid this mistake:

1. Report all results, whether significant or not, with equal clarity.
2. Highlight the implications of non-significant findings in your discussion chapter.
3. Be transparent about limitations that might have influenced the results.

By addressing non-significant results, you demonstrate integrity and provide a balanced view of your research, which strengthens the credibility of your study.

Key Takeaways

1. The thesis results section is crucial for presenting the findings of your research in a clear and objective manner. You should always focus on what was discovered without any interpretations or opinions.
2. The results section of a thesis/dissertation provides a factual account of the data, answering research questions without interpretation, while the Discussion section focuses on analyzing and explaining the meaning and implications of the findings.
3. A well-structured Results section should present findings logically, starting with broader results and progressing to more specific details, ensuring readers can easily follow the narrative.
4. Preparing the Results section involves revisiting research questions, using past tense for completed research, avoiding unnecessary data, and leveraging tables, charts, and graphs to present findings effectively.
5. Descriptive statistics summarize central tendencies and variability (e.g., means, standard deviations), while inferential statistics assess relationships and test hypotheses (e.g., t-tests, ANOVA, regression).
6. Presenting the demographics of the study sample provides essential context for understanding the findings and their applicability to the broader population.
7. Tables, charts, and graphs play a vital role in simplifying complex findings, making the data more accessible and easier to interpret for readers.
8. Avoid including interpretations, redundant information, or extraneous data in the Results section. Keep it concise, precise, and focused on directly answering research questions.

Conclusion

Crafting a compelling Results section is vital for the success of your thesis. It requires a clear presentation of your data and findings, aligned with your research objectives. At DissertationDataAnalysisHelp.com, we specialize in providing expert assistance to help you navigate this complex process. Whether you’re struggling with organizing your findings or interpreting your data, our team is here to support you every step of the way.

For more insights on handling your thesis data, check out our detailed guide on how to analyze quantitative data for a dissertation. Let us help you achieve academic excellence with precision and confidence!