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• How to Write a Results Section | Tips & Examples

How to Write a Results Section | Tips & Examples

Published on 27 October 2016 by Bas Swaen . Revised on 25 October 2022 by Tegan George.

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

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Table of contents

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

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

Here are a few best practices:

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

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

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

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

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

A note on tables and figures

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

As a rule of thumb:

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

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

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

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

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

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

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

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

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

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

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

I have completed my data collection and analyzed the results.

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

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

I have stated whether each hypothesis was supported or refuted.

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

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

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

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

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

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

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

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

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

Don’t interpret or speculate in the results chapter.

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

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

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

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How to Write an Impressive Thesis Results Section

After collecting and analyzing your research data, it’s time to write the results section. This article explains how to write and organize the thesis results section, the differences in reporting qualitative and quantitative data, the differences in the thesis results section across different fields, and the best practices for tables and figures.

What is the thesis results section?

The thesis results section factually and concisely describes what was observed and measured during the study but does not interpret the findings. It presents the findings in a logical order.

What should the thesis results section include?

• Include all relevant results as text, tables, or figures
• Report the results of subject recruitment and data collection
• For qualitative research, present the data from all statistical analyses, whether or not the results are significant
• For quantitative research, present the data by coding or categorizing themes and topics
• Present all secondary findings (e.g., subgroup analyses)
• Include all results, even if they do not fit in with your assumptions or support your hypothesis

What should the thesis results section not include?

• If the study involves the thematic analysis of an interview, don’t include complete transcripts of all interviews. Instead, add these as appendices
• Don’t present raw data. These may be included in appendices
• Don’t include background information (this should be in the introduction section )
• Don’t speculate on the meaning of results that do not support your hypothesis. This will be addressed later in the discussion and conclusion sections.
• Don’t repeat results that have been presented in tables and figures. Only highlight the pertinent points or elaborate on specific aspects

How should the thesis results section be organized?

The opening paragraph of the thesis results section should briefly restate the thesis question. Then, present the results objectively as text, figures, or tables.

Quantitative research presents the results from experiments and  statistical tests , usually in the form of tables and figures (graphs, diagrams, and images), with any pertinent findings emphasized in the text. The results are structured around the thesis question. Demographic data are usually presented first in this section.

For each statistical test used, the following information must be mentioned:

• The type of analysis used (e.g., Mann–Whitney U test or multiple regression analysis)
• A concise summary of each result, including  descriptive statistics   (e.g., means, medians, and modes) and  inferential statistics   (e.g., correlation, regression, and  p  values) and whether the results are significant
• Any trends or differences identified through comparisons
• How the findings relate to your research and if they support or contradict your hypothesis

Qualitative research   presents results around key themes or topics identified from your data analysis and explains how these themes evolved. The data are usually presented as text because it is hard to present the findings as figures.

For each theme presented, describe:

• General trends or patterns observed
• Significant or representative responses
• Relevant quotations from your study subjects

Relevant characteristics about your study subjects

Differences among the results section in different fields of research

Nevertheless, results should be presented logically across all disciplines and reflect the thesis question and any hypotheses that were tested.

The presentation of results varies considerably across disciplines. For example, a thesis documenting how a particular population interprets a specific event and a thesis investigating customer service may both have collected data using interviews and analyzed it using similar methods. Still, the presentation of the results will vastly differ because they are answering different thesis questions. A science thesis may have used experiments to generate data, and these would be presented differently again, probably involving statistics. Nevertheless, results should be presented logically across all disciplines and reflect the thesis question and any  hypotheses that were tested.

Differences between reporting thesis results in the Sciences and the Humanities and Social Sciences (HSS) domains

In the Sciences domain (qualitative and experimental research), the results and discussion sections are considered separate entities, and the results from experiments and statistical tests are presented. In the HSS domain (qualitative research), the results and discussion sections may be combined.

There are two approaches to presenting results in the HSS field:

• If you want to highlight important findings, first present a synopsis of the results and then explain the key findings.
• If you have multiple results of equal significance, present one result and explain it. Then present another result and explain that, and so on. Conclude with an overall synopsis.

Best practices for using tables and figures

The use of figures and tables is highly encouraged because they provide a standalone overview of the research findings that are much easier to understand than wading through dry text mentioning one result after another. The text in the results section should not repeat the information presented in figures and tables. Instead, it should focus on the pertinent findings or elaborate on specific points.

Some popular software programs that can be used for the analysis and presentation of statistical data include  Statistical Package for the Social Sciences (SPSS ) ,  R software ,  MATLAB , Microsoft Excel,  Statistical Analysis Software (SAS) ,  GraphPad Prism , and  Minitab .

The easiest way to construct tables is to use the  Table function in Microsoft Word . Microsoft Excel can also be used; however, Word is the easier option.

General guidelines for figures and tables

• Figures and tables must be interpretable independent from the text
• Number tables and figures consecutively (in separate lists) in the order in which they are mentioned in the text
• All tables and figures must be cited in the text
• Provide clear, descriptive titles for all figures and tables
• Include a legend to concisely describe what is presented in the figure or table

Figure guidelines

• Label figures so that the reader can easily understand what is being shown
• Use a consistent font type and font size for all labels in figure panels
• All abbreviations used in the figure artwork should be defined in the figure legend

Table guidelines

• All table columns should have a heading abbreviation used in tables should be defined in the table footnotes
• All numbers and text presented in tables must correlate with the data presented in the manuscript body

Quantitative results example : Figure 3 presents the characteristics of unemployed subjects and their rate of criminal convictions. A statistically significant association was observed between unemployed people <20 years old, the male sex, and no household income.

Qualitative results example: Table 5 shows the themes identified during the face-to-face interviews about the application that we developed to anonymously report corruption in the workplace. There was positive feedback on the app layout and ease of use. Concerns that emerged from the interviews included breaches of confidentiality and the inability to report incidents because of unstable cellphone network coverage.

Table 5. Themes and selected quotes from the evaluation of our app designed to anonymously report workplace corruption.

Tips for writing the thesis results section

• Do not state that a difference was present between the two groups unless this can be supported by a significant  p-value .
• Present the findings only . Do not comment or speculate on their interpretation.
• Every result included  must have a corresponding method in the methods section. Conversely, all methods  must have associated results presented in the results section.
• Do not explain commonly used methods. Instead, cite a reference.
• Be consistent with the units of measurement used in your thesis study. If you start with kg, then use the same unit all throughout your thesis. Also, be consistent with the capitalization of units of measurement. For example, use either “ml” or “mL” for milliliters, but not both.
• Never manipulate measurement outcomes, even if the result is unexpected. Remain objective.

Results vs. discussion vs. conclusion

Results are presented in three sections of your thesis: the results, discussion, and conclusion.

• In the results section, the data are presented simply and objectively. No speculation or interpretation is given.
• In the discussion section, the meaning of the results is interpreted and put into context (e.g., compared with other findings in the literature ), and its importance is assigned.
• In the conclusion section, the results and the main conclusions are summarized.

A thesis is the most crucial document that you will write during your academic studies. For professional thesis editing and thesis proofreading services , visit Enago Thesis Editing for more information.

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Review Checklist

Have you  completed all data collection procedures and analyzed all results ?

Have you  included all results relevant to your thesis question, even if they do not support your hypothesis?

Have you reported the results  objectively , with no interpretation or speculation?

For quantitative research, have you included both  descriptive and  inferential statistical results and stated whether they support or contradict your hypothesis?

Have you used  tables and figures to present all results?

In your thesis body, have you presented only the pertinent results and elaborated on specific aspects that were presented in the tables and figures?

Are all tables and figures  correctly labeled and cited in numerical order in the text?

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Home » Research Results Section – Writing Guide and Examples

Research Results Section – Writing Guide and Examples

Table of Contents

Research Results

Research results refer to the findings and conclusions derived from a systematic investigation or study conducted to answer a specific question or hypothesis. These results are typically presented in a written report or paper and can include various forms of data such as numerical data, qualitative data, statistics, charts, graphs, and visual aids.

Results Section in Research

The results section of the research paper presents the findings of the study. It is the part of the paper where the researcher reports the data collected during the study and analyzes it to draw conclusions.

In the results section, the researcher should describe the data that was collected, the statistical analysis performed, and the findings of the study. It is important to be objective and not interpret the data in this section. Instead, the researcher should report the data as accurately and objectively as possible.

Structure of Research Results Section

The structure of the research results section can vary depending on the type of research conducted, but in general, it should contain the following components:

• Introduction: The introduction should provide an overview of the study, its aims, and its research questions. It should also briefly explain the methodology used to conduct the study.
• Data presentation : This section presents the data collected during the study. It may include tables, graphs, or other visual aids to help readers better understand the data. The data presented should be organized in a logical and coherent way, with headings and subheadings used to help guide the reader.
• Data analysis: In this section, the data presented in the previous section are analyzed and interpreted. The statistical tests used to analyze the data should be clearly explained, and the results of the tests should be presented in a way that is easy to understand.
• Discussion of results : This section should provide an interpretation of the results of the study, including a discussion of any unexpected findings. The discussion should also address the study’s research questions and explain how the results contribute to the field of study.
• Limitations: This section should acknowledge any limitations of the study, such as sample size, data collection methods, or other factors that may have influenced the results.
• Conclusions: The conclusions should summarize the main findings of the study and provide a final interpretation of the results. The conclusions should also address the study’s research questions and explain how the results contribute to the field of study.
• Recommendations : This section may provide recommendations for future research based on the study’s findings. It may also suggest practical applications for the study’s results in real-world settings.

Outline of Research Results Section

The following is an outline of the key components typically included in the Results section:

I. Introduction

• A brief overview of the research objectives and hypotheses
• A statement of the research question

II. Descriptive statistics

• Summary statistics (e.g., mean, standard deviation) for each variable analyzed
• Frequencies and percentages for categorical variables

III. Inferential statistics

• Results of statistical analyses, including tests of hypotheses
• Tables or figures to display statistical results

IV. Effect sizes and confidence intervals

• Effect sizes (e.g., Cohen’s d, odds ratio) to quantify the strength of the relationship between variables
• Confidence intervals to estimate the range of plausible values for the effect size

V. Subgroup analyses

• Results of analyses that examined differences between subgroups (e.g., by gender, age, treatment group)

VI. Limitations and assumptions

• Discussion of any limitations of the study and potential sources of bias
• Assumptions made in the statistical analyses

VII. Conclusions

• A summary of the key findings and their implications
• A statement of whether the hypotheses were supported or not
• Suggestions for future research

Example of Research Results Section

An Example of a Research Results Section could be:

• This study sought to examine the relationship between sleep quality and academic performance in college students.
• Hypothesis : College students who report better sleep quality will have higher GPAs than those who report poor sleep quality.
• Methodology : Participants completed a survey about their sleep habits and academic performance.

II. Participants

• Participants were college students (N=200) from a mid-sized public university in the United States.
• The sample was evenly split by gender (50% female, 50% male) and predominantly white (85%).
• Participants were recruited through flyers and online advertisements.

III. Results

• Participants who reported better sleep quality had significantly higher GPAs (M=3.5, SD=0.5) than those who reported poor sleep quality (M=2.9, SD=0.6).
• See Table 1 for a summary of the results.
• Participants who reported consistent sleep schedules had higher GPAs than those with irregular sleep schedules.

IV. Discussion

• The results support the hypothesis that better sleep quality is associated with higher academic performance in college students.
• These findings have implications for college students, as prioritizing sleep could lead to better academic outcomes.
• Limitations of the study include self-reported data and the lack of control for other variables that could impact academic performance.

V. Conclusion

• College students who prioritize sleep may see a positive impact on their academic performance.
• These findings highlight the importance of sleep in academic success.
• Future research could explore interventions to improve sleep quality in college students.

Example of Research Results in Research Paper :

Our study aimed to compare the performance of three different machine learning algorithms (Random Forest, Support Vector Machine, and Neural Network) in predicting customer churn in a telecommunications company. We collected a dataset of 10,000 customer records, with 20 predictor variables and a binary churn outcome variable.

Our analysis revealed that all three algorithms performed well in predicting customer churn, with an overall accuracy of 85%. However, the Random Forest algorithm showed the highest accuracy (88%), followed by the Support Vector Machine (86%) and the Neural Network (84%).

Furthermore, we found that the most important predictor variables for customer churn were monthly charges, contract type, and tenure. Random Forest identified monthly charges as the most important variable, while Support Vector Machine and Neural Network identified contract type as the most important.

Overall, our results suggest that machine learning algorithms can be effective in predicting customer churn in a telecommunications company, and that Random Forest is the most accurate algorithm for this task.

Example 3 :

Title : The Impact of Social Media on Body Image and Self-Esteem

Abstract : This study aimed to investigate the relationship between social media use, body image, and self-esteem among young adults. A total of 200 participants were recruited from a university and completed self-report measures of social media use, body image satisfaction, and self-esteem.

Results: The results showed that social media use was significantly associated with body image dissatisfaction and lower self-esteem. Specifically, participants who reported spending more time on social media platforms had lower levels of body image satisfaction and self-esteem compared to those who reported less social media use. Moreover, the study found that comparing oneself to others on social media was a significant predictor of body image dissatisfaction and lower self-esteem.

Conclusion : These results suggest that social media use can have negative effects on body image satisfaction and self-esteem among young adults. It is important for individuals to be mindful of their social media use and to recognize the potential negative impact it can have on their mental health. Furthermore, interventions aimed at promoting positive body image and self-esteem should take into account the role of social media in shaping these attitudes and behaviors.

Importance of Research Results

Research results are important for several reasons, including:

• Advancing knowledge: Research results can contribute to the advancement of knowledge in a particular field, whether it be in science, technology, medicine, social sciences, or humanities.
• Developing theories: Research results can help to develop or modify existing theories and create new ones.
• Improving practices: Research results can inform and improve practices in various fields, such as education, healthcare, business, and public policy.
• Identifying problems and solutions: Research results can identify problems and provide solutions to complex issues in society, including issues related to health, environment, social justice, and economics.
• Validating claims : Research results can validate or refute claims made by individuals or groups in society, such as politicians, corporations, or activists.
• Providing evidence: Research results can provide evidence to support decision-making, policy-making, and resource allocation in various fields.

How to Write Results in A Research Paper

Here are some general guidelines on how to write results in a research paper:

• Organize the results section: Start by organizing the results section in a logical and coherent manner. Divide the section into subsections if necessary, based on the research questions or hypotheses.
• Present the findings: Present the findings in a clear and concise manner. Use tables, graphs, and figures to illustrate the data and make the presentation more engaging.
• Describe the data: Describe the data in detail, including the sample size, response rate, and any missing data. Provide relevant descriptive statistics such as means, standard deviations, and ranges.
• Interpret the findings: Interpret the findings in light of the research questions or hypotheses. Discuss the implications of the findings and the extent to which they support or contradict existing theories or previous research.
• Discuss the limitations : Discuss the limitations of the study, including any potential sources of bias or confounding factors that may have affected the results.
• Compare the results : Compare the results with those of previous studies or theoretical predictions. Discuss any similarities, differences, or inconsistencies.
• Avoid redundancy: Avoid repeating information that has already been presented in the introduction or methods sections. Instead, focus on presenting new and relevant information.
• Be objective: Be objective in presenting the results, avoiding any personal biases or interpretations.

When to Write Research Results

Here are situations When to Write Research Results”

• After conducting research on the chosen topic and obtaining relevant data, organize the findings in a structured format that accurately represents the information gathered.
• Once the data has been analyzed and interpreted, and conclusions have been drawn, begin the writing process.
• Before starting to write, ensure that the research results adhere to the guidelines and requirements of the intended audience, such as a scientific journal or academic conference.
• Begin by writing an abstract that briefly summarizes the research question, methodology, findings, and conclusions.
• Follow the abstract with an introduction that provides context for the research, explains its significance, and outlines the research question and objectives.
• The next section should be a literature review that provides an overview of existing research on the topic and highlights the gaps in knowledge that the current research seeks to address.
• The methodology section should provide a detailed explanation of the research design, including the sample size, data collection methods, and analytical techniques used.
• Present the research results in a clear and concise manner, using graphs, tables, and figures to illustrate the findings.
• Discuss the implications of the research results, including how they contribute to the existing body of knowledge on the topic and what further research is needed.
• Conclude the paper by summarizing the main findings, reiterating the significance of the research, and offering suggestions for future research.

Purpose of Research Results

The purposes of Research Results are as follows:

• Informing policy and practice: Research results can provide evidence-based information to inform policy decisions, such as in the fields of healthcare, education, and environmental regulation. They can also inform best practices in fields such as business, engineering, and social work.
• Addressing societal problems : Research results can be used to help address societal problems, such as reducing poverty, improving public health, and promoting social justice.
• Generating economic benefits : Research results can lead to the development of new products, services, and technologies that can create economic value and improve quality of life.
• Supporting academic and professional development : Research results can be used to support academic and professional development by providing opportunities for students, researchers, and practitioners to learn about new findings and methodologies in their field.
• Enhancing public understanding: Research results can help to educate the public about important issues and promote scientific literacy, leading to more informed decision-making and better public policy.
• Evaluating interventions: Research results can be used to evaluate the effectiveness of interventions, such as treatments, educational programs, and social policies. This can help to identify areas where improvements are needed and guide future interventions.
• Contributing to scientific progress: Research results can contribute to the advancement of science by providing new insights and discoveries that can lead to new theories, methods, and techniques.
• Informing decision-making : Research results can provide decision-makers with the information they need to make informed decisions. This can include decision-making at the individual, organizational, or governmental levels.
• Fostering collaboration : Research results can facilitate collaboration between researchers and practitioners, leading to new partnerships, interdisciplinary approaches, and innovative solutions to complex problems.

Advantages of Research Results

Some Advantages of Research Results are as follows:

• Improved decision-making: Research results can help inform decision-making in various fields, including medicine, business, and government. For example, research on the effectiveness of different treatments for a particular disease can help doctors make informed decisions about the best course of treatment for their patients.
• Innovation : Research results can lead to the development of new technologies, products, and services. For example, research on renewable energy sources can lead to the development of new and more efficient ways to harness renewable energy.
• Economic benefits: Research results can stimulate economic growth by providing new opportunities for businesses and entrepreneurs. For example, research on new materials or manufacturing techniques can lead to the development of new products and processes that can create new jobs and boost economic activity.
• Improved quality of life: Research results can contribute to improving the quality of life for individuals and society as a whole. For example, research on the causes of a particular disease can lead to the development of new treatments and cures, improving the health and well-being of millions of people.

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How to Write the Results/Findings Section in Research

What is the research paper Results section and what does it do?

The Results section of a scientific research paper represents the core findings of a study derived from the methods applied to gather and analyze information. It presents these findings in a logical sequence without bias or interpretation from the author, setting up the reader for later interpretation and evaluation in the Discussion section. A major purpose of the Results section is to break down the data into sentences that show its significance to the research question(s).

The Results section appears third in the section sequence in most scientific papers. It follows the presentation of the Methods and Materials and is presented before the Discussion section —although the Results and Discussion are presented together in many journals. This section answers the basic question “What did you find in your research?”

What is included in the Results section?

The Results section should include the findings of your study and ONLY the findings of your study. The findings include:

• Data presented in tables, charts, graphs, and other figures (may be placed into the text or on separate pages at the end of the manuscript)
• A contextual analysis of this data explaining its meaning in sentence form
• All data that corresponds to the central research question(s)
• All secondary findings (secondary outcomes, subgroup analyses, etc.)

If the scope of the study is broad, or if you studied a variety of variables, or if the methodology used yields a wide range of different results, the author should present only those results that are most relevant to the research question stated in the Introduction section .

As a general rule, any information that does not present the direct findings or outcome of the study should be left out of this section. Unless the journal requests that authors combine the Results and Discussion sections, explanations and interpretations should be omitted from the Results.

How are the results organized?

The best way to organize your Results section is “logically.” One logical and clear method of organizing research results is to provide them alongside the research questions—within each research question, present the type of data that addresses that research question.

Let’s look at an example. Your research question is based on a survey among patients who were treated at a hospital and received postoperative care. Let’s say your first research question is:

“What do hospital patients over age 55 think about postoperative care?”

This can actually be represented as a heading within your Results section, though it might be presented as a statement rather than a question:

Attitudes towards postoperative care in patients over the age of 55

Now present the results that address this specific research question first. In this case, perhaps a table illustrating data from a survey. Likert items can be included in this example. Tables can also present standard deviations, probabilities, correlation matrices, etc.

Following this, present a content analysis, in words, of one end of the spectrum of the survey or data table. In our example case, start with the POSITIVE survey responses regarding postoperative care, using descriptive phrases. For example:

“Sixty-five percent of patients over 55 responded positively to the question “ Are you satisfied with your hospital’s postoperative care ?” (Fig. 2)

Include other results such as subcategory analyses. The amount of textual description used will depend on how much interpretation of tables and figures is necessary and how many examples the reader needs in order to understand the significance of your research findings.

Next, present a content analysis of another part of the spectrum of the same research question, perhaps the NEGATIVE or NEUTRAL responses to the survey. For instance:

  “As Figure 1 shows, 15 out of 60 patients in Group A responded negatively to Question 2.”

After you have assessed the data in one figure and explained it sufficiently, move on to your next research question. For example:

  “How does patient satisfaction correspond to in-hospital improvements made to postoperative care?”

This kind of data may be presented through a figure or set of figures (for instance, a paired T-test table).

Explain the data you present, here in a table, with a concise content analysis:

“The p-value for the comparison between the before and after groups of patients was .03% (Fig. 2), indicating that the greater the dissatisfaction among patients, the more frequent the improvements that were made to postoperative care.”

Let’s examine another example of a Results section from a study on plant tolerance to heavy metal stress . In the Introduction section, the aims of the study are presented as “determining the physiological and morphological responses of Allium cepa L. towards increased cadmium toxicity” and “evaluating its potential to accumulate the metal and its associated environmental consequences.” The Results section presents data showing how these aims are achieved in tables alongside a content analysis, beginning with an overview of the findings:

“Cadmium caused inhibition of root and leave elongation, with increasing effects at higher exposure doses (Fig. 1a-c).”

The figure containing this data is cited in parentheses. Note that this author has combined three graphs into one single figure. Separating the data into separate graphs focusing on specific aspects makes it easier for the reader to assess the findings, and consolidating this information into one figure saves space and makes it easy to locate the most relevant results.

Following this overall summary, the relevant data in the tables is broken down into greater detail in text form in the Results section.

• “Results on the bio-accumulation of cadmium were found to be the highest (17.5 mg kgG1) in the bulb, when the concentration of cadmium in the solution was 1×10G2 M and lowest (0.11 mg kgG1) in the leaves when the concentration was 1×10G3 M.”

Captioning and Referencing Tables and Figures

Tables and figures are central components of your Results section and you need to carefully think about the most effective way to use graphs and tables to present your findings . Therefore, it is crucial to know how to write strong figure captions and to refer to them within the text of the Results section.

The most important advice one can give here as well as throughout the paper is to check the requirements and standards of the journal to which you are submitting your work. Every journal has its own design and layout standards, which you can find in the author instructions on the target journal’s website. Perusing a journal’s published articles will also give you an idea of the proper number, size, and complexity of your figures.

Regardless of which format you use, the figures should be placed in the order they are referenced in the Results section and be as clear and easy to understand as possible. If there are multiple variables being considered (within one or more research questions), it can be a good idea to split these up into separate figures. Subsequently, these can be referenced and analyzed under separate headings and paragraphs in the text.

To create a caption, consider the research question being asked and change it into a phrase. For instance, if one question is “Which color did participants choose?”, the caption might be “Color choice by participant group.” Or in our last research paper example, where the question was “What is the concentration of cadmium in different parts of the onion after 14 days?” the caption reads:

 “Fig. 1(a-c): Mean concentration of Cd determined in (a) bulbs, (b) leaves, and (c) roots of onions after a 14-day period.”

Steps for Composing the Results Section

Because each study is unique, there is no one-size-fits-all approach when it comes to designing a strategy for structuring and writing the section of a research paper where findings are presented. The content and layout of this section will be determined by the specific area of research, the design of the study and its particular methodologies, and the guidelines of the target journal and its editors. However, the following steps can be used to compose the results of most scientific research studies and are essential for researchers who are new to preparing a manuscript for publication or who need a reminder of how to construct the Results section.

Step 1 : Consult the guidelines or instructions that the target journal or publisher provides authors and read research papers it has published, especially those with similar topics, methods, or results to your study.

• The guidelines will generally outline specific requirements for the results or findings section, and the published articles will provide sound examples of successful approaches.
• Note length limitations on restrictions on content. For instance, while many journals require the Results and Discussion sections to be separate, others do not—qualitative research papers often include results and interpretations in the same section (“Results and Discussion”).
• Reading the aims and scope in the journal’s “ guide for authors ” section and understanding the interests of its readers will be invaluable in preparing to write the Results section.

Step 2 : Consider your research results in relation to the journal’s requirements and catalogue your results.

• Focus on experimental results and other findings that are especially relevant to your research questions and objectives and include them even if they are unexpected or do not support your ideas and hypotheses.
• Catalogue your findings—use subheadings to streamline and clarify your report. This will help you avoid excessive and peripheral details as you write and also help your reader understand and remember your findings. Create appendices that might interest specialists but prove too long or distracting for other readers.
• Decide how you will structure of your results. You might match the order of the research questions and hypotheses to your results, or you could arrange them according to the order presented in the Methods section. A chronological order or even a hierarchy of importance or meaningful grouping of main themes or categories might prove effective. Consider your audience, evidence, and most importantly, the objectives of your research when choosing a structure for presenting your findings.

Step 3 : Design figures and tables to present and illustrate your data.

• Tables and figures should be numbered according to the order in which they are mentioned in the main text of the paper.
• Information in figures should be relatively self-explanatory (with the aid of captions), and their design should include all definitions and other information necessary for readers to understand the findings without reading all of the text.
• Use tables and figures as a focal point to tell a clear and informative story about your research and avoid repeating information. But remember that while figures clarify and enhance the text, they cannot replace it.

Step 4 : Draft your Results section using the findings and figures you have organized.

• The goal is to communicate this complex information as clearly and precisely as possible; precise and compact phrases and sentences are most effective.
• In the opening paragraph of this section, restate your research questions or aims to focus the reader’s attention to what the results are trying to show. It is also a good idea to summarize key findings at the end of this section to create a logical transition to the interpretation and discussion that follows.
• Try to write in the past tense and the active voice to relay the findings since the research has already been done and the agent is usually clear. This will ensure that your explanations are also clear and logical.
• Make sure that any specialized terminology or abbreviation you have used here has been defined and clarified in the  Introduction section .

Step 5 : Review your draft; edit and revise until it reports results exactly as you would like to have them reported to your readers.

• Double-check the accuracy and consistency of all the data, as well as all of the visual elements included.
• Read your draft aloud to catch language errors (grammar, spelling, and mechanics), awkward phrases, and missing transitions.
• Ensure that your results are presented in the best order to focus on objectives and prepare readers for interpretations, valuations, and recommendations in the Discussion section . Look back over the paper’s Introduction and background while anticipating the Discussion and Conclusion sections to ensure that the presentation of your results is consistent and effective.
• Consider seeking additional guidance on your paper. Find additional readers to look over your Results section and see if it can be improved in any way. Peers, professors, or qualified experts can provide valuable insights.

One excellent option is to use a professional English proofreading and editing service  such as Wordvice, including our paper editing service . With hundreds of qualified editors from dozens of scientific fields, Wordvice has helped thousands of authors revise their manuscripts and get accepted into their target journals. Read more about the  proofreading and editing process  before proceeding with getting academic editing services and manuscript editing services for your manuscript.

As the representation of your study’s data output, the Results section presents the core information in your research paper. By writing with clarity and conciseness and by highlighting and explaining the crucial findings of their study, authors increase the impact and effectiveness of their research manuscripts.

For more articles and videos on writing your research manuscript, visit Wordvice’s Resources page.

Wordvice Resources

• How to Write a Research Paper Introduction 
• Which Verb Tenses to Use in a Research Paper
• How to Write an Abstract for a Research Paper
• How to Write a Research Paper Title
• Useful Phrases for Academic Writing
• Common Transition Terms in Academic Papers
• Active and Passive Voice in Research Papers
• 100+ Verbs That Will Make Your Research Writing Amazing
• Tips for Paraphrasing in Research Papers

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Research Results Template

The fastest (and smartest) way to craft a strong results section for your dissertation, thesis or research project.

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What It Covers

This template covers all the core components required in the results chapter of a typical dissertation, thesis or research project:

• The opening /overview section
• The body section for qualitative studies
• The body section for quantitative studies
• Concluding summary

The purpose of each section is explained in plain language, followed by an overview of the key elements that you need to cover. The template also includes practical examples to help you understand exactly what’s required, along with links to additional free resources (articles, videos, etc.) to help you along your research journey.

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How to Write an APA Results Section

Verywell / Nusha Ashjaee 

What to Include in an APA Results Section

• Justify Claims
• Summarize Results

Report All Relevant Results

• Report Statistical Findings

Include Tables and Figures

What not to include in an apa results section.

Psychology papers generally follow a specific structure. One important section of a paper is known as the results section. An APA results section of a psychology paper summarizes the data that was collected and the statistical analyses that were performed. The goal of this section is to report the results of your study or experiment without any type of subjective interpretation.

At a Glance

The results section is a vital part of an APA paper that summarizes a study's findings and statistical analysis. This section often includes descriptive text, tables, and figures to help summarize the findings.

The focus is purely on summarizing and presenting the findings and should not include any interpretation, since you'll cover that in the subsequent discussion section.

This article covers how to write an APA results section, including what to include and what to avoid.

The results section is the third section of a psychology paper. It will appear after the introduction and methods sections and before the discussion section.

The results section should include:

• A summary of the research findings.
• Information about participant flow, recruitment , retention, and attrition. If some participants started the study and later left or failed to complete the study, then this should be described. 
• Information about any reasons why some data might have been excluded from the study. 
• Statistical information including samples sizes and statistical tests that were used. It should report standard deviations, p-values, and other measures of interest.

Results Should Justify Your Claims

Report data in order to sufficiently justify your conclusions. Since you'll be talking about your own interpretation of the results in the discussion section, you need to be sure that the information reported in the results section justifies your claims.

When you start writing your discussion section, you can then look back on your results to ensure that all the data you need are there to fully support your conclusions. Be sure not to make claims in your discussion section that are not supported by the findings described in your results section.

Summarize Your Results

Remember, you are summarizing the results of your psychological study, not reporting them in full detail. The results section should be a relatively brief overview of your findings, not a complete presentation of every single number and calculation.

If you choose, you can create a supplemental online archive where other researchers can access the raw data if they choose.

How long should a results section be?

The length of your results section will vary depending on the nature of your paper and the complexity of your research. In most cases, this will be the shortest section of your paper.

Just as the results section of your psychology paper should sufficiently justify your claims, it should also provide an accurate look at what you found in your study. Be sure to mention all relevant information.

Don't omit findings simply because they failed to support your predictions.

Your hypothesis may have expected more statistically significant results or your study didn't support your hypothesis , but that doesn't mean that the conclusions you reach are not useful. Provide data about what you found in your results section, then save your interpretation for what the results might mean in the discussion section.

While your study might not have supported your original predictions, your finding can provide important inspiration for future explorations into a topic.

How is the results section different from the discussion section?

The results section provides the results of your study or experiment . The goal of the section is to report what happened and the statistical analyses you performed. The discussion section is where you will examine what these results mean and whether they support or fail to support your hypothesis.

Report Your Statistical Findings

Always assume that your readers have a solid understanding of statistical concepts. There's no need to explain what a t-test is or how a one-way ANOVA works. Your responsibility is to report the results of your study, not to teach your readers how to analyze or interpret statistics.

Include Effect Sizes

The Publication Manual of the American Psychological Association recommends including effect sizes in your results section so that readers can appreciate the importance of your study's findings.

Your results section should include both text and illustrations. Presenting data in this way makes it easier for readers to quickly look at your results.

Structure your results section around tables or figures that summarize the results of your statistical analysis. In many cases, the easiest way to accomplish this is to first create your tables and figures and then organize them in a logical way. Next, write the summary text to support your illustrative materials.

Only include tables and figures if you are going to talk about them in the body text of your results section.

In addition to knowing what you should include in the results section of your psychology paper, it's also important to be aware of things that you should avoid putting in this section:

Cause-and-Effect Conclusions

Don't draw cause-effect conclusions. Avoid making any claims suggesting that your result "proves" that something is true. 

Interpretations

Present the data without editorializing it. Save your comments and interpretations for the discussion section of your paper. 

Statistics Without Context

Don't include statistics without narration. The results section should not be a numbers dump. Instead, you should sequentially narrate what these numbers mean.

Don't include the raw data in the results section. The results section should be a concise presentation of the results. If there is raw data that would be useful, include it in the appendix .

Don't only rely on descriptive text. Use tables and figures to present these findings when appropriate. This makes the results section easier to read and can convey a great deal of information quickly.

Repeated Data

Don't present the same data twice in your illustrative materials. If you have already presented some data in a table, don't present it again in a figure. If you have presented data in a figure, don't present it again in a table.

All of Your Findings

Don't feel like you have to include everything. If data is irrelevant to the research question, don't include it in the results section.

But Don't Skip Relevant Data

Don't leave out results because they don't support your claims. Even if your data does not support your hypothesis, including it in your findings is essential if it's relevant.

More Tips for Writing a Results Section

If you are struggling, there are a few things to remember that might help:

• Use the past tense . The results section should be written in the past tense.
• Be concise and objective . You will have the opportunity to give your own interpretations of the results in the discussion section.
• Use APA format . As you are writing your results section, keep a style guide on hand. The Publication Manual of the American Psychological Association is the official source for APA style .
• Visit your library . Read some journal articles that are on your topic. Pay attention to how the authors present the results of their research.
• Get a second opinion . If possible, take your paper to your school's writing lab for additional assistance.

What This Means For You

Remember, the results section of your paper is all about providing the data from your study. This section is often the shortest part of your paper, and in most cases, the most clinical.

Be sure not to include any subjective interpretation of the results. Simply relay the data in the most objective and straightforward way possible. You can then provide your own analysis of what these results mean in the discussion section of your paper.

Bavdekar SB, Chandak S. Results: Unraveling the findings . J Assoc Physicians India . 2015 Sep;63(9):44-6. PMID:27608866.

Snyder N, Foltz C, Lendner M, Vaccaro AR. How to write an effective results section .  Clin Spine Surg . 2019;32(7):295-296. doi:10.1097/BSD.0000000000000845

American Psychological Association.  Publication Manual of the American Psychological Association  (7th ed.). Washington DC: The American Psychological Association; 2019.

Purdue Online Writing Lab. APA sample paper: Experimental psychology .

Berkeley University. Reviewing test results .

Tuncel A, Atan A. How to clearly articulate results and construct tables and figures in a scientific paper ? Turk J Urol . 2013;39(Suppl 1):16-19. doi:10.5152/tud.2013.048

By Kendra Cherry, MSEd Kendra Cherry, MS, is a psychosocial rehabilitation specialist, psychology educator, and author of the "Everything Psychology Book."

Research Voyage

Research Tips and Infromation

How to Write the Results Section of your Dissertation or Thesis?

Introduction

Organizing your results, providing context, presenting the data in results section, describing statistical analysis, reporting the findings in results section, supporting the findings, visual representation in results section.

As you progress on your journey towards completing your PhD or Post Graduate dissertation, one of the most critical sections that holds immense significance is the results section.

Results section serves as the pinnacle of your research, where you unveil the outcomes of your exhaustive efforts and shed light on the answers to your research questions. In this blog post, we will delve into the intricacies of the results section and explore how to effectively present and interpret your findings to leave a lasting impact.

Whether you’re conducting research in the field of biology, psychology, computer science, or any other discipline, the results section is where your data takes center stage. It is a space where you showcase your meticulous analysis, statistical methods, and the discoveries you’ve made along the way. By understanding the key components and best practices for constructing a compelling results section, you can present your findings in a manner that resonates with both your academic peers and the wider research community.

In this comprehensive guide, we will walk you through the fundamental elements of the results section, from organizing your data to choosing the appropriate visual representations. We will explore the importance of clear and concise reporting, emphasizing the significance of providing contextual information and highlighting any unexpected or groundbreaking discoveries.

Furthermore, we will discuss strategies for effectively interpreting your results, discussing their implications, and connecting them back to your research objectives. By mastering these skills, you will be able to demonstrate the significance of your work, contribute to the existing body of knowledge, and potentially pave the way for further research in your field.

Throughout the blog post, I will provide concrete examples from various disciplines to illustrate the implementation of these techniques. Additionally, I will offer valuable tips on avoiding common pitfalls, ensuring the accuracy and reliability of your results, and seeking feedback from your advisors or peers to enhance the quality of your analysis.

If you are in paucity of time, not confident of your writing skills and in a hurry to complete the writing task then you can think of hiring a research consultant that solves all your problems. Please visit my article on Hiring a Research consultant for your PhD tasks for further details.

Organizing the results of your study in a logical and coherent manner is crucial for effectively communicating your findings. By presenting your results in an organized structure, you enhance the clarity and readability of your dissertation. Here are some key considerations for organizing your results:

• Research studies often involve complex algorithms, software implementations, experimental data, and performance metrics. It is essential to organize these diverse elements in a cohesive manner to make it easier for readers to follow your research. A well-structured results section enables readers to understand the progression of your experiments and the relationship between different findings.
• Begin by reminding readers of the research questions or hypotheses that guided your study. This alignment helps establish a clear connection between the objectives of your research and the subsequent presentation of results. For example, if your research question focuses on evaluating the efficiency of a new sorting algorithm, you would present the experimental data, performance metrics, and comparative analyses specific to that algorithm in relation to the research question.
• Subsubsection 1.1: Experimental Setup
• Subsubsection 1.2: Experimental Results and Analysis
• Subsubsection 2.1: Performance Metrics
• Subsubsection 2.2: Comparative Results and Discussion

Remember to tailor the organization of your results section to the specific requirements of your research. The key is to provide a logical flow and structure that enables readers to easily comprehend and interpret your findings.

Providing context for the results of your study is essential to help readers understand the significance and implications of your findings. By offering background information and study design details, you establish a foundation upon which the results can be properly interpreted. Here are some key considerations for providing context:

• Before delving into the results, it is important to provide readers with relevant background information about the topic or problem being addressed. This may include a literature review of existing research, theories, or methodologies in the field. By doing so, you situate your work within the broader landscape of and demonstrate its relevance. Additionally, explain the design of your study, such as the specific algorithms, software frameworks, datasets, or hardware setups used. This ensures that readers understand the context in which your results were obtained.
• Provide a brief overview of the current state-of-the-art in image recognition algorithms and their limitations.
• Explain the specific challenges or gaps in the existing methods that motivated your research.
• Describe the design of your study, including the choice of machine learning techniques, datasets used for training and evaluation, preprocessing steps, and any hardware or software configurations.

By providing context, you allow readers to understand the background, motivation, and methodology behind your research. This sets the stage for better comprehension and interpretation of your results. Contextualizing your findings, as it helps establish the relevance, novelty, and potential impact of your research within the larger field.

Presenting data in a clear and organized manner is crucial for effectively communicating your results. The way you present your data can greatly impact the reader’s understanding and interpretation of your findings. Here are some key considerations for presenting data:

• Presenting performance metrics of different algorithms using a table to allow for easy comparison.
• Using a line graph to depict the improvement in accuracy over training iterations in a machine learning model.
• Employing a bar chart to compare the execution times of different algorithms on a specific dataset.
• Clear labelling and formatting of your data ensure that readers can easily understand and interpret the information presented. Label each table, figure, chart, or graph with a concise and descriptive title. Ensure that axes, legends, and labels are clearly labelled and units of measurement are specified. Use appropriate fonts, colours, and styles to enhance readability. Consider providing captions or footnotes to provide additional context or explanations where necessary.
• In the text, refer to a specific table presenting the accuracy results of different algorithms and explain how these results support your research hypothesis or contribute to the field.
• Discuss a figure showing the relationship between the number of training examples and the performance of a machine learning model, emphasizing its implications for scalability and generalization.

By presenting data in a visually appealing and well-organized manner, you enhance the clarity and accessibility of your results. Proper labelling, formatting, and referring to each table or figure in the text help readers navigate the information and grasp its significance. Remember to choose the most appropriate format for your data and use visuals to support and reinforce your findings.

The inclusion of statistical analyses in the results section is crucial for providing objective and quantitative evidence to support your findings. Statistical analyses help you draw meaningful conclusions from your data and determine the significance of observed results. Here are some key considerations for describing statistical analyses:

• Statistical analyses play a vital role in determining the reliability and significance of your findings. They provide a systematic and objective framework for interpreting the data and testing hypotheses. Discuss the importance of including statistical analyses in the results section to demonstrate the rigour and validity of your research.
• Describe using a t-test to compare the means of two groups in a user study, as it is appropriate for assessing the statistical significance of differences.
• Explain employing logistic regression to model the relationship between independent variables and a binary outcome in a predictive analytics study.
• Report the p-value as 0.032, indicating a statistically significant difference between the two groups at the 0.05 significance level.
• Interpret an effect size of 0.40 as a medium-sized effect, highlighting its practical importance in the context of the research.

By describing the statistical analyses conducted, explaining the rationale behind the chosen tests, and accurately presenting the statistical values and interpretations, you strengthen the validity and reliability of your findings. Statistical analyses provide an objective framework for drawing conclusions from your data and lend credibility to your research in the computer science domain.

Reporting the findings of your research in an objective, concise, and clear manner is essential for effectively communicating your results. Here are some key considerations for reporting the findings:

• Summarize the key findings of a machine learning study by stating that “the proposed algorithm achieved an average accuracy of 85% on the test dataset, outperforming existing state-of-the-art methods by 10%.”
• For a research question about the impact of different programming languages on software performance, present specific metrics such as execution time or memory usage for each language, along with a comparison and interpretation of the results.
• Instead of using overly technical language, communicate the results in a more accessible way: “The experimental results showed a significant correlation between the number of training samples and the accuracy of the model, indicating that a larger training dataset leads to improved prediction performance.”

By guiding readers on summarizing the results objectively and concisely, addressing each research question or hypothesis, and using clear and concise language, you ensure that your findings are communicated effectively. This approach allows readers to understand the core contributions of your research and how they align with the research questions or hypotheses you set out to investigate.

Providing strong evidence from the data to support your findings, addressing unexpected or contradictory results, and discussing limitations and potential explanations are essential components of reporting research findings. Here are some key considerations for supporting the findings:

• Present empirical evidence from a user study, such as participant feedback or performance metrics, to support the usability and effectiveness of a proposed user interface design.
• If a software system performed unexpectedly poorly in certain scenarios, discuss potential factors such as data bias, implementation issues, or limitations of the evaluation methodology that could have influenced the results.
• Acknowledge limitations such as a small sample size, limited dataset availability, or computational constraints that might affect the generalizability or robustness of the results.
• Discuss potential explanations for unexpected results, such as issues with data quality, algorithmic complexity, or model assumptions.

By providing evidence from the data to support the findings, addressing unexpected or contradictory results, and discussing limitations and potential explanations, you demonstrate a rigorous and reflective approach to your research in the computer science domain. This allows readers to assess the strength and reliability of your findings and gain a deeper understanding of the nuances and implications of your work.

Using visual representations, such as tables, graphs, and figures, alongside the text can greatly enhance the understanding and impact of your findings. Here are some key considerations for visual representation:

Visual representations offer several benefits in presenting research findings. They provide a concise and intuitive way to convey complex information, trends, and patterns. Visuals can help readers grasp key insights at a glance, enhance the overall readability of the document, and make the findings more memorable. Visual representations also facilitate effective comparisons, highlight important relationships, and aid in storytelling. Example:

When creating visual representations, consider the following tips to ensure clarity and effectiveness: a. Choose the appropriate visual format: Select the most suitable format, such as tables, line graphs, scatter plots, or heatmaps, based on the nature of the data and the message you want to convey.

b. Simplify and declutter: Avoid overwhelming the visuals with excessive data points, labels, or unnecessary decorations. Keep the design clean and focused on conveying the essential information.

c. Label and title clearly: Provide descriptive and informative titles for tables, graphs, and figures. Label the axes, data points, or components clearly to facilitate understanding.

d. Use colors and visual cues purposefully: Utilize colors and visual cues to highlight important information or differentiate between categories. Ensure that the chosen colors are distinguishable and accessible. e. Provide legends and captions: Include legends to explain symbols, colors, or abbreviations used in the visuals. Provide informative captions or annotations to guide readers in interpreting the visuals accurately. Example:

By incorporating clear and effective visual representations alongside the text, you enhance the presentation and understanding of your research findings in the computer science domain. Well-designed tables, graphs, and figures can simplify complex information, facilitate comparisons, and enhance the visual appeal of your dissertation. Remember to choose appropriate formats, keep the visuals uncluttered, label clearly, and use colors and visual cues purposefully to maximize their impact.

Writing the results section of a dissertation or thesis is a critical task that requires careful attention to detail, organization, and effective communication. Throughout this blog post, we have explored key elements to consider when crafting this section.

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How to Write the Results Section: Guide to Structure and Key Points

• 4 minute read
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Table of Contents

The ‘ Results’ section of a research paper, like the ‘Introduction’ and other key parts, attracts significant attention from editors, reviewers, and readers. The reason lies in its critical role — that of revealing the key findings of a study and demonstrating how your research fills a knowledge gap in your field of study. Given its importance, crafting a clear and logically structured results section is essential.   

In this article, we will discuss the key elements of an effective results section and share strategies for making it concise and engaging. We hope this guide will help you quickly grasp ways of writing the results section, avoid common pitfalls, and make your writing process more efficient and effective.  

Structure of the results section  

Briefly restate the research topic in the introduction : Although the main purpose of the  results section  in a research paper is to list the notable findings of a study, it is customary to start with a brief repetition of the research question. This helps refocus the reader, allowing them to better appreciate the relevance of the findings. Additionally, restating the research question establishes a connection to the previous section of the paper, creating a smoother flow of information.  

Systematically present your research findings : Address the primary research question first, followed by the secondary research questions. If your research addresses multiple questions, mention the findings related to each one individually to ensure clarity and coherence.  

Represent your results visually: Graphs, tables, and other figures can help illustrate the findings of your paper, especially if there is a large amount of data in the results. As a rule of thumb, use a visual medium like a graph or a table if you wish to present three or more statistical values simultaneously.  

Graphical or tabular representations of data can also make your results section more visually appealing. Remember, an appealing and well-organized results section can help peer reviewers better understand the merits of your research, thereby increasing your chances of publication.  

Practical guidance for writing an effective ‘Results’ section   

• Always use simple and plain language. Avoid the use of uncertain or unclear expressions.  
• The findings of the study must be expressed in an objective and unbiased manner.  While it is acceptable to correlate certain findings , it is best to avoid over-interpreting the results. In addition, avoid using subjective or emotional words , such as “interestingly” or “unfortunately”, to describe the results as this may cause readers to doubt the objectivity of the paper.  
• The content balances simplicity with comprehensiveness . For statistical data, simply describe the relevant tests and explain their results without mentioning raw data. If the study involves multiple hypotheses, describe the results for each one separately to avoid confusion and aid understanding. To enhance credibility, e nsure that negative results , if any, are included in this section, even if they do not support the research hypothesis.  
• Wherever possible, use illustrations like tables, figures, charts, or other visual representations to highlight the results of your research paper. Mention these illustrations in the text, but do not repeat the information that they convey ¹ .  

Difference between data, results, and discussion sections  

Data ,  results,  and  discussion  sections all communicate the findings of a study, but each serves a distinct purpose with varying levels of interpretation.   

In the  results section , one cannot provide data without interpreting its relevance or make statements without citing data ² . In a sense, the  results section  does not draw connections between different data points. Therefore, there is a certain level of interpretation involved in drawing results out of data.

(The example is intended to showcase how the visual elements and text in the results section complement each other ³ . The academic viewpoints included in the illustrative screenshots should not be used as references.)  

The discussion section allows authors even more interpretive freedom compared to the results section. Here, data and patterns within the data are compared with the findings from other studies to make more generalized points. Unlike the results section , which focuses purely on factual data, the discussion section touches upon hypothetical information, drawing conjectures and suggesting future directions for research.  

The ‘ Results’ section serves as the core of a research paper, capturing readers’ attention and providing insights into the study’s essence. Regardless of the subject of your research paper, a well-written results section can generate interest in your research. By following the tips outlined here, you can create a results section that effectively communicates your finding and invites further exploration. Remember, clarity is the key, and with the right approach, your results section can guide readers through the intricacies of your research.  

Professionals at Elsevier Language Services know the secret to writing a well-balanced results section. With their expert suggestions, you can ensure that your findings come across clearly to the reader. To maximize your chances of publication, reach out to Elsevier Language Services today !  

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Reference  

• Cetin, S., & Hackam, D. J. (2005). An approach to the writing of a scientific manuscript. Journal of Surgical Research, 128(2), 165–167. https://doi.org/10.1016/j.jss.2005.07.002  
• Bahadoran, Z., Mirmiran, P., Zadeh-Vakili, A., Hosseinpanah, F., & Ghasemi, A. (2019). The Principles of Biomedical Scientific Writing: Results. International Journal of Endocrinology and Metabolism/International Journal of Endocrinology and Metabolism., In Press (In Press). https://doi.org/10.5812/ijem.92113  
• Guo, J., Wang, J., Zhang, P., Wen, P., Zhang, S., Dong, X., & Dong, J. (2024). TRIM6 promotes glioma malignant progression by enhancing FOXO3A ubiquitination and degradation. Translational Oncology, 46, 101999. https://doi.org/10.1016/j.tranon.2024.101999  

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How to Write a Results Section for a Dissertation or Research Paper: Guide & Examples

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A results section is a crucial part of a research paper or dissertation, where you analyze your major findings. This section goes beyond simply presenting study outcomes. You should also include a comprehensive statistical analysis and interpret the collected data in detail.

Without dissertation research results, it is impossible to imagine a scientific work. Your task here is to present your study findings. What are qualitative or quantitative indicators? How to use tables and diagrams? How to describe data? Our article answers all these questions and many more. So, read further to discover how to analyze and describe your research indexes or contact or professionals for dissertation help from StudyCrumb.

What Is a Results Section of Dissertation?

The results section of a dissertation is a data statement from your research. Here you should present the main findings of your study to your readers. This section aims to show information objectively, systematically, concisely. It is allowed using text supplemented with illustrations.  In general, this section's length is not limited but should include all necessary data. Interpretations or conclusions should not be included in this section. Therefore, in theory, this is one of your shortest sections. But it can also be one of the most challenging sections.  The introduction presents a research topic and answers the question "why?". The Methods section explains the data collection process and answers "how?". Meanwhile, the result section shows actual data gained from experiments and tells "what?" Thus, this part plays a critical role in highlighting study's relevance. This chapter gives reader study relevance with novelty. So, you should figure out how to write it correctly. Here are main tasks that you should keep in mind while writing:

• Results answer the question "What was found in your research?"
• Results contain only your study's outcome. They do not include comments or interpretations.
• Results must always be presented accurately & objectively.
• Tables & figures are used to draw readers' attention. But the same data should never be presented in the form of a table and a figure. Don't repeat anything from a table also in text.

Dissertation: Results vs Discussion vs Conclusion

Results and discussion sections of a dissertation are often confused among researchers. Sometimes both these parts are mixed up with a conclusion for thesis . Figured out what is covered in each of these important chapters. Your readers should see that you notice how different they are. A clear understanding of differences will help you write your dissertation more effectively. 5 differences between Results VS Discussion VS Conclusion:

Wanna figure out the actual difference between discussion vs conclusion? Check out our helpful articles about Dissertation Discussion or Dissertation Conclusion.

Present Your Findings When Writing Results Section of Dissertation

Now it's time to understand how to arrange the results section of the dissertation. First, present most general findings, then narrow it down to a more specific one. Describe both qualitative & quantitative results. For example, imagine you are comparing the behavior of hamsters and mice. First, say a few words about the behavioral type of mammals that you studied. Then, mention rodents in general. At end, describe specific species of animals you carried out an experiment on.

Qualitative Results Section in Dissertation

In your dissertation results section, qualitative data may not be directly related to specific sub-questions or hypotheses. You can structure this chapter around main issues that arise when analyzing data. For each question, make a general observation of what data show. For example, you may recall recurring agreements or differences, patterns, trends. Personal answers are the basis of your research. Clarify and support these views with direct quotes. Add more information to the thesis appendix if it's needed.

Quantitative Results Section in a Dissertation

The easiest way to write a quantitative dissertation results section is to build it around a sub-question or hypothesis of your research. For each subquery, provide relevant results and include statistical analysis . Then briefly evaluate importance & reliability. Notice how each result relates to the problem or whether it supports the hypothesis. Focus on key trends, differences, and relationships between data. But don't speculate about their meaning or consequences. This should be put in the discussion vs conclusion section. Suppose your results are not directly related to answering your questions. Maybe there is additional information that helps readers understand how you collect data. In that case, you can include them in the appendix. It is often helpful to include visual elements such as graphs, charts, and tables. But only if they accurately support your results and add value.

Tables and Figures in Results Section in Dissertation

We recommend you use tables or figures in the dissertation results section correctly. Such interpretation can effectively present complex data concisely and visually. It allows readers to quickly gain a statistical overview. On the contrary, poorly designed graphs can confuse readers. That will reduce the effectiveness of your article.  Here are our recommendations that help you understand how to use tables and figures:

• Make sure tables and figures are self-explanatory. Sometimes, your readers may look at tables and figures before reading the entire text. So they should make sense as separate elements.
• Do not repeat the content of tables and figures in text. Text can be used to highlight key points from tables and figures. But do not repeat every element.
• Make sure that values ​​or information in tables and text are consistent. Make sure that abbreviations, group names, interpretations are the same as in text.
• Use clear, informative titles for tables and figures. Do not leave any table or figure without a title or legend. Otherwise, readers will not be able to understand data's meaning. Also, make sure column names, labels, figures are understandable.
• Check accuracy of data presented in tables and figures. Always double-check tables and figures to make sure numbers converge.
• Tables should not contain redundant information. Make sure tables in the article are not too crowded. If you need to provide extensive data, use Appendixes.
• Make sure images are clear. Make sure images and all parts of drawings are precise. Lettering should be in a standard font and legible against the background of the picture.
• Ask for permission to use illustrations. If you use illustrations, be sure to ask copyright holders and indicate them.

Tips on How to Write a Results Section

We have prepared several tips on how to write the results section of the dissertation!  Present data collected during study objectively, logically, and concisely. Highlight most important results and organize them into specific sections. It is an excellent way to show that you have covered all the descriptive information you need. Correct usage of visual elements effectively helps your readers with understanding. So, follow main 3 rules for writing this part:

• State only actual results. Leave explanations and comments for Discussion.
• Use text, tables, and pictures to orderly highlight key results.
• Make sure that contents of tables and figures are not repeated in text.

In case you have questions about a  conceptual framework in research , you will find a blog dedicated to this issue in our database.

What to Avoid When Writing the Results Section of a Dissertation

Here we will discuss how NOT to write the results section of a dissertation. Or simply, what points to avoid:

• Do not make your research too complicated. Your paper, tables, and graphs should be clearly marked and follow order. So that they can exist independently without further explanation.
• Do not include raw data. Remember, you are summarizing relevant results, not reporting them in detail. This chapter should briefly summarize your findings. Avoid complete introduction to each number and calculation.
• Do not contradict errors or false results. Explain these errors and contradictions in conclusions. This often happens when different research methods have been used.
• Do not write a conclusion or discussion. Instead, this part should contain summaries of findings.
• Do not tend to include explanations and inferences from results. Such an approach can make this chapter subjective, unclear, and confusing to the reader.
• Do not forget about novelty. Its lack is one of the main reasons for the paper's rejection.

Dissertation Results Section Example

Let's take a look at some good results section of dissertation examples. Remember that this part shows fundamental research you've done in detail. So, it has to be clear and concise, as you can see in the sample.

Final Thoughts on Writing Results Section of Dissertation

When writing a results section of a dissertation, highlight your achievements by data. The main chapter's task is to convince the reader of conclusions' validity of your research. You should not overload text with too detailed information. Never use words whose meanings you do not understand. Also, oversimplification may seem unconvincing for readers. But on the other hand, writing this part can even be fun. You can directly see your study results, which you'll interpret later. So keep going, and we wish you courage!

Writing any academic paper is long and thorough work. But StudyCrumb got you back! Our professional writers will deliver any type of work quickly and excellently! 

Joe Eckel is an expert on Dissertations writing. He makes sure that each student gets precious insights on composing A-grade academic writing.

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How to Write the Dissertation Findings or Results – Tips

Published by Grace Graffin at August 11th, 2021 , Revised On August 13, 2024

Each  part of the dissertation is unique, and some general and specific rules must be followed. The dissertation’s findings section presents the key results of your research without interpreting their meaning .

Theoretically, this is an exciting section of a dissertation because it involves writing what you have observed and found. However, it can be a little tricky if there is too much information to confuse the readers.

The goal is to include only the essential and relevant findings in this section. The results must be presented in an orderly sequence to provide clarity to the readers.

This section of the dissertation should be easy for the readers to follow, so you should avoid going into a lengthy debate over the interpretation of the results.

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 from the analysis are significant. This segment should be written in the past sentence as you describe what you have done in the past.

This article will provide detailed information about  how to   write the findings of a dissertation .

When to Write Dissertation Findings Chapter

As soon as you have gathered and analysed your data, you can start to write up the findings chapter of your dissertation paper. Remember that it is your chance to report the most notable findings of your research work and relate them to the research hypothesis  or  research questions set out in  the introduction chapter of the dissertation .

You will be required to separately report your study’s findings before moving on to the discussion chapter  if your dissertation is based on the  collection of primary data  or experimental work.

However, you may not be required to have an independent findings chapter if your dissertation is purely descriptive and focuses on the analysis of case studies or interpretation of texts.

• Always report the findings of your research in the past tense.
• The dissertation findings chapter varies from one project to another, depending on the data collected and analyzed.
• Avoid reporting results that are not relevant to your research questions or research hypothesis.

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1. Reporting Quantitative Findings

The best way to present your quantitative findings is to structure them around the research  hypothesis or  questions you intend to address as part of your dissertation project.

Report the relevant findings for each research question or hypothesis, focusing on how you analyzed them.

Analysis of your findings will help you determine how they relate to the different research questions and whether they support the hypothesis you formulated.

While you must highlight meaningful relationships, variances, and tendencies, it is important not to guess their interpretations and implications because this is something to save for the discussion  and  conclusion  chapters.

Any findings not directly relevant to your research questions or explanations concerning the data collection process  should be added to the dissertation paper’s appendix section.

Use of Figures and Tables in Dissertation Findings

Suppose your dissertation is based on quantitative research. In that case, it is important to include charts, graphs, tables, and other visual elements to help your readers understand the emerging trends and relationships in your findings.

Repeating information will give the impression that you are short on ideas. Refer to all charts, illustrations, and tables in your writing but avoid recurrence.

The text should be used only to elaborate and summarize certain parts of your results. On the other hand, illustrations and tables are used to present multifaceted data.

It is recommended to give descriptive labels and captions to all illustrations used so the readers can figure out what each refers to.

How to Report Quantitative Findings

Here is an example of how to report quantitative results in your dissertation findings chapter;

Two hundred seventeen participants completed both the pretest and post-test and a Pairwise T-test was used for the analysis. The quantitative data analysis reveals a statistically significant difference between the mean scores of the pretest and posttest scales from the Teachers Discovering Computers course. The pretest mean was 29.00 with a standard deviation of 7.65, while the posttest mean was 26.50 with a standard deviation of 9.74 (Table 1). These results yield a significance level of .000, indicating a strong treatment effect (see Table 3). With the correlation between the scores being .448, the little relationship is seen between the pretest and posttest scores (Table 2). This leads the researcher to conclude that the impact of the course on the educators’ perception and integration of technology into the curriculum is dramatic.

Paired Samples

Paired Samples Correlation

Paired Samples Test

Also Read: How to Write the Abstract for the Dissertation.

2. Reporting Qualitative Findings

A notable issue with reporting qualitative findings is that not all results directly relate to your research questions or hypothesis.

The best way to present the results of qualitative research is to frame your findings around the most critical areas or themes you obtained after you examined the data.

In-depth data analysis will help you observe what the data shows for each theme. Any developments, relationships, patterns, and independent responses directly relevant to your research question or hypothesis should be mentioned to the readers.

Additional information not directly relevant to your research can be included in the appendix .

How to Report Qualitative Findings

Here is an example of how to report qualitative results in your dissertation findings chapter;

The last question of the interview focused on the need for improvement in Thai ready-to-eat products and the industry at large, emphasizing the need for enhancement in the current products being offered in the market. When asked if there was any particular need for Thai ready-to-eat meals to be improved and how to improve them in case of ‘yes,’ the males replied mainly by saying that the current products need improvement in terms of the use of healthier raw materials and preservatives or additives. There was an agreement amongst all males concerning the need to improve the industry for ready-to-eat meals and the use of more healthy items to prepare such meals. The females were also of the opinion that the fast-food items needed to be improved in the sense that more healthy raw materials such as vegetable oil and unsaturated fats, including whole-wheat products, to overcome risks associated with trans fat leading to obesity and hypertension should be used for the production of RTE products. The frozen RTE meals and packaged snacks included many preservatives and chemical-based flavouring enhancers that harmed human health and needed to be reduced. The industry is said to be aware of this fact and should try to produce RTE products that benefit the community in terms of healthy consumption.

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What to Avoid in Dissertation Findings Chapter

• Avoid using interpretive and subjective phrases and terms such as “confirms,” “reveals,” “suggests,” or “validates.” These terms are more suitable for the discussion chapter , where you will be expected to interpret the results in detail.
• Only briefly explain findings in relation to the key themes, hypothesis, and research questions. You don’t want to write a detailed subjective explanation for any research questions at this stage.

The Do’s of Writing the Findings or Results Section

• Ensure you are not presenting results from other research studies in your findings.
• Observe whether or not your hypothesis is tested or research questions answered.
• Illustrations and tables present data and are labelled to help your readers understand what they relate to.
• Use software such as Excel, STATA, and SPSS to analyse results and important trends.

Essential Guidelines on How to Write Dissertation Findings

The dissertation findings chapter should provide the context for understanding the results. The research problem should be repeated, and the research goals should be stated briefly.

This approach helps to gain the reader’s attention toward the research problem. The first step towards writing the findings is identifying which results will be presented in this section.

The results relevant to the questions must be presented, considering whether the results support the hypothesis. You do not need to include every result in the findings section. The next step is ensuring the data can be appropriately organized and accurate.

You will need to have a basic idea about writing the findings of a dissertation because this will provide you with the knowledge to arrange the data chronologically.

Start each paragraph by writing about the most important results and concluding the section with the most negligible actual results.

A short paragraph can conclude the findings section, summarising the findings so readers will remember as they transition to the next chapter. This is essential if findings are unexpected or unfamiliar or impact the study.

Our writers can help you with all parts of your dissertation, including statistical analysis of your results . To obtain free non-binding quotes, please complete our online quote form here .

Be Impartial in your Writing

When crafting your findings, knowing how you will organize the work is important. The findings are the story that needs to be told in response to the research questions that have been answered.

Therefore, the story needs to be organized to make sense to you and the reader. The findings must be compelling and responsive to be linked to the research questions being answered.

Always ensure that the size and direction of any changes, including percentage change, can be mentioned in the section. The details of p values or confidence intervals and limits should be included.

The findings sections only have the relevant parts of the primary evidence mentioned. Still, it is a good practice to include all the primary evidence in an appendix that can be referred to later.

The results should always be written neutrally without speculation or implication. The statement of the results mustn’t have any form of evaluation or interpretation.

Negative results should be added in the findings section because they validate the results and provide high neutrality levels.

The length of the dissertation findings chapter is an important question that must be addressed. It should be noted that the length of the section is directly related to the total word count of your dissertation paper.

The writer should use their discretion in deciding the length of the findings section or refer to the dissertation handbook or structure guidelines.

It should neither belong nor be short nor concise and comprehensive to highlight the reader’s main findings.

Ethically, you should be confident in the findings and provide counter-evidence. Anything that does not have sufficient evidence should be discarded. The findings should respond to the problem presented and provide a solution to those questions.

Structure of the Findings Chapter

The chapter should use appropriate words and phrases to present the results to the readers. Logical sentences should be used, while paragraphs should be linked to produce cohesive work.

You must ensure all the significant results have been added in the section. Recheck after completing the section to ensure no mistakes have been made.

The structure of the findings section is something you may have to be sure of primarily because it will provide the basis for your research work and ensure that the discussions section can be written clearly and proficiently.

One way to arrange the results is to provide a brief synopsis and then explain the essential findings. However, there should be no speculation or explanation of the results, as this will be done in the discussion section.

Another way to arrange the section is to present and explain a result. This can be done for all the results while the section is concluded with an overall synopsis.

This is the preferred method when you are writing more extended dissertations. It can be helpful when multiple results are equally significant. A brief conclusion should be written to link all the results and transition to the discussion section.

Numerous data analysis dissertation examples are available on the Internet, which will help you improve your understanding of writing the dissertation’s findings.

Problems to Avoid When Writing Dissertation Findings

One of the problems to avoid while writing the dissertation findings is reporting background information or explaining the findings. This should be done in the introduction section .

You can always revise the introduction chapter based on the data you have collected if that seems an appropriate thing to do.

Raw data or intermediate calculations should not be added in the findings section. Always ask your professor if raw data needs to be included.

If the data is to be included, then use an appendix or a set of appendices referred to in the text of the findings chapter.

Do not use vague or non-specific phrases in the findings section. It is important to be factual and concise for the reader’s benefit.

The findings section presents the crucial data collected during the research process. It should be presented concisely and clearly to the reader. There should be no interpretation, speculation, or analysis of the data.

The significant results should be categorized systematically with the text used with charts, figures, and tables. Furthermore, avoiding using vague and non-specific words in this section is essential.

It is essential to label the tables and visual material properly. You should also check and proofread the section to avoid mistakes.

The dissertation findings chapter is a critical part of your overall dissertation paper. If you struggle with presenting your results and statistical analysis, our expert dissertation writers can help you get things right. Whether you need help with the entire dissertation paper or individual chapters, our dissertation experts can provide customized dissertation support .

FAQs About Findings of a Dissertation

How do i report quantitative findings.

The best way to present your quantitative findings is to structure them around the research hypothesis or research questions you intended to address as part of your dissertation project. Report the relevant findings for each of the research questions or hypotheses, focusing on how you analyzed them.

How do I report qualitative findings?

The best way to present the qualitative research results is to frame your findings around the most important areas or themes that you obtained after examining the data.

An in-depth analysis of the data will help you observe what the data is showing for each theme. Any developments, relationships, patterns, and independent responses that are directly relevant to your research question or hypothesis should be clearly mentioned for the readers.

Can I use interpretive phrases like ‘it confirms’ in the finding chapter?

No, It is highly advisable to avoid using interpretive and subjective phrases in the finding chapter. These terms are more suitable for the discussion chapter , where you will be expected to provide your interpretation of the results in detail.

Can I report the results from other research papers in my findings chapter?

NO, you must not be presenting results from other research studies in your findings.

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If your dissertation includes many abbreviations, it would make sense to define all these abbreviations in a list of abbreviations in alphabetical order.

Not sure how to write dissertation title page? All dissertations must have a dissertation title page where necessary information should be clearly presented

A list of glossary in a dissertation contains all the terms that were used in your dissertation but the meanings of which may not be obvious to the readers.

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APA Results Section – Explanation & Examples

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The APA results section summarizes data and includes reporting statistics in a quantitative research study. The APA results section is an essential part of your research paper and typically begins with a brief overview of the data followed by a systematic and detailed reporting of each hypothesis tested. The interpreted results will then be presented in the discussion sections. Ensure you adhere to APA style guidelines consistently throughout the paper.

Inhaltsverzeichnis

• 1 APA Results Section – In a Nutshell
• 2 Definition: APA results section
• 3 What’s included in the APA results section?
• 4 APA results section: Introducing the data
• 5 APA results section: Summarizing the data
• 6 APA results section: Reporting the results
• 7 APA results section: Formatting numbers
• 8 APA results section: Don’t include these

APA Results Section – In a Nutshell

• The APA results section of empirical manuscripts reports the quantitative results of a study conducted on a data set.
• The APA results section provides concrete evidence to disprove or confirm the hypothesis.

Definition: APA results section

The American Psychological Association recommends the APA style guide for presenting results in a manuscript. A research manuscript’s APA results section describes the researcher’s findings following a thorough data analysis and interpretation of the results. It uses obtained data to test or refute the theory of a research study.

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What’s included in the APA results section?

The APA results section includes preliminary details on the data, participants, statistics , and the results of the explanatory analysis , as discussed below.

• Participants – The number of participants is reported at every study stage
• Missing data – Identifying the amount of data excluded from the final analysis.
• Adverse effects – Report any unforeseen events for clinical studies
• Descriptive statistics – Summarize the secondary and primary outcomes of a study
• Inferential statistics – Helps researchers draw conclusions and make predictions from the data.
• Confidence interval and effect size  – Confidence intervals are a range of possible values for the data set mean.
• Results of explanatory analysis – An exploratory research investigates data to test a hypothesis, check assumptions, and find anomalies.

APA results section: Introducing the data

Before you discuss your research findings, start by clearly describing the participants at each study stage. If any data was excluded from the eventual analysis, indicate that too.

Participants

Recruitment, participant flow, and attrition should be reported. Attrition bias affects external and internal validity and produces erroneous results.

A flow chart is often the best way to report the number of participants per group per stage and their reasons for attrition. Below is an example of how to report participant flow.

• 25% of the 400 participants who signed up and completed the first survey were eliminated for not fitting the research criteria.
• 15% didn’t use fiber optics internet exclusively.
• 10% did not have internet access.
• 300 participants progressed to the final survey round for a gift bag.
• 52 people didn’t complete the survey.

This resulted in 248 research participants.

Missing data & adverse effects

In any study, missing data must be reported. Unexpected events, poor storage, and equipment failures can cause missing data. In any instance, clearly explain why you couldn’t use the data.

Data outliers can be excluded from the final study, but you must explain why. Include how you handled missing data. Standard procedures include mean-value imputation, interpolation, extrapolation, and substitution.

• Results of 33 participants were excluded from the study as they did not meet the research criteria.
• The data for another 4 participants were lost due to human error.

APA results section: Summarizing the data

It is important to note that you should provide a summary of your study’s results. However, you can create a supplemental archive for other researchers to access raw data. 2

Descriptive Statistics

Descriptive statistics are concise coefficients that summarize a specific data collection , such as a population sample or APA results section. APA results section can include descriptive statistics such as:

• Central tendency measures describe a data set by identifying the center of the data set. ( mode , median, mean )
• Measures of variability describe the score dispersion within a data set. ( standard deviation , range, variance , and interquartile range )
• Sample sizes
• Variables of interest, which are measured, changing quantities in experimental studies. Be sure to explain how you operationalized any variable of interest you use.
• 20 athletes in five trials were given 400 mg of a performance-enhancing substance to measure their speed (m/s ) and reaction time(s).
• After averaging each athlete’s speed and response time, the group’s averages were calculated.

The group that used the performance-enhancing drug had a higher speed (m/s) than the group that did not use the drug ( M = 4, SD=1.25 )

APA results section: Reporting the results

APA journal standards require all the appropriate hypothesis tests, confidence intervals, and effect size estimates to be reported in the APA results section.

Inferential statistics

Inferential statistics help researchers draw conclusions and make predictions based on the data.

When you are reporting the inferential statistics in the APA results section, use the following:

• Degrees of freedom
• Test statistic (includes the z-score, t-value, and f-ratio )
• Error term (if needed, though it is not included in correlations and non-parametric tests.)
• The exact p-value (unless . 001)

In keeping with the hypotheses, athletes who take performance-enhancing drugs have increased reaction times, and speeds, t (20) = 1s , p .001

Confidence intervals & effect sizes

A confidence interval can be described as a range of possible values for the mean derived from the sample data. It helps show the variability that is around point estimates. You should include confidence intervals any time you report estimates for population parameters.

Night guards consume an average of 600 mg of caffeine weekly, 93% CI [90, 200}

Effect size measures an experiment’s magnitude. It explains the research’s significance. Since effect size is an estimate, confidence intervals should be included.

Moderate amounts of performance-enhancing drugs increase speed significantly, Cohen’s d =1.4, 93% CI [0.92, 1.57]

Subgroup & exploratory analyses

Exploratory analysis tests a hypothesis, checks assumptions , and finds patterns and anomalies in data . If you find notable results, report them as exploratory, not confirming, to avoid overstating their value.

APA results section: Formatting numbers

Use figures, text, and tables to show numbers in APA results sections properly.

✓ For three or fewer numbers, use a sentence, a table for 4 and 20 numbers, and a figure for more than 20 .

✓ Number and title the APA tables and figures , as well as relevant notes. If you have already presented the data in a table, do not repeat it in a figure and vice versa.

✓ Statistics in your APA results section must be abbreviated, capitalized, and italicized.

✓ Use APA norms for reporting statistics and writing numbers.

✓ Look up these guidelines if you are unsure how to present certain symbols.

• ✓ Post a picture on Instagram
• ✓ Get the most likes on your picture
• ✓ Receive up to $300 cash back

APA results section: Don’t include these

Besides knowing what to include in an APA results section, it is just as important to know what not to have. Below is an outline of what you should exclude from an APA results section.

What should be included in the APA results section?

The APA results section should include details on the participants, descriptive statistics and inferential statistics , missing data , and the results of any exploratory analysis.

What tense should I use to write my results?

Write the APA results section in the past tense.

When should I include tables and figures?

Include tables and figures if you will discuss them in the body text of the APA results section.

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Writing up the results section of your dissertation

(Last updated: 12 May 2021)

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When asked why doing a dissertation can be such a headache, the typical student usually replies with one of two answers. Either, they simply don't like writing enormous volumes of text, or – and you may relate here – they categorically do not enjoy analysing data. "It's so long and boring!", the typical student wails.

Well, students wail, and we answer. We have put together this very comprehensive, very useful guide on how to write up the results section of your dissertation. To help you further, we've broken the information down into both quantitative and qualitative results, so you can focus on what applies to you most.

Writing up your quantitative results

Understanding the basics of your research.

First, you need to recall what you have assessed – or what your main variables are.

All quantitative research has at least one independent and one dependent variable, and, at this point, you should define them explicitly. An independent variable is one that you control to test its effects on the dependent variable. A dependent variable is thus your outcome variable.

Second, you need to determine if your variables were categorical or continuous.

A categorical variable is one with a fixed number of possible values, and a continuous variable is one where final scores have a wide range. Finally, you need to recall if you have used a so-called covariate or confounder variable. This is a variable that could have influenced the relationship between your independent and dependent variable, and that you controlled in order to accurately estimate the relationship between your main variables.

Let’s explain all this with an example. Suppose that your research was to assess whether height is associated with self-esteem. Here, participants’ height is an independent variable and self-esteem is a dependent variable. Because both height and scores on a measure of self-esteem can have a wide range, you have two continuous variables. You might have also wanted to see if the relationship between height and self-esteem exists after controlling for participants’ weight. In this case, weight is a confounding variable that you need to control for.

Here is another example. You might have assessed whether more females than males want to read a specific romantic novel. Here, your independent variable is gender and your dependent variable is the determination to read the book. Since gender has categories (male and female), this is a categorical variable. If you have assessed the determination to read the book on a scale from 1 to 10 (e.g. 1 = no determination at all to read the book, all the way to 10 = extremely strong determination to read it), then this is a continuous variable; however, if you have asked your participants to say whether they do or do not want to read the book, then this is a categorical variable (since there are two categories: “yes” and “no”).

Lastly, you might have wanted to see if the link between gender and the determination to read the book exists after controlling for participants’ current relationship status. Here, relationship status is your confounding variable.

We will return to these examples throughout this blog post. At this point, it is important to remember that outlining your research in this way helps you to write up your results section in the easiest way possible.

Let’s move on to the next step.

Outlining descriptive and frequencies statistics

In order to report descriptive and/or frequencies statistics, you need to outline all variables that you have used in your research and note whether those variables are continuous or categorical.

For continuous variables, you are using descriptive statistics and reporting the measures of central tendency (mean) and measures of variability or spread (standard deviation). For categorical variables, you are using frequencies statistics and reporting the number (or frequency) of participants per category and associated percentages. Both these statistics require you to make a table, and in both cases you also need to comment upon the statistics.

How does all of this look in practice? Recall the two examples that were outlined above. If you have assessed the association between participants’ height and self-esteem, while controlling for participants’ weight, then your research consists of three continuous variables. You need to make a table, as in TABLE 1 below, which identifies means and standard deviations for all these variables. When commenting upon the results, you can say:

Participants were on average 173.50 cm tall (SD = 5.81) and their mean weight was 65.31 kg (SD = 4.44). On average, participants had moderate levels of self-esteem (M = 5.55, SD = 2.67).

Note that, in this example, you are concluding that participants had moderate self-esteem levels if their self-esteem was assessed on a 1 to 10 scale. Since the value of 5 falls within the middle of this range, you are concluding that the mean value of self-esteem is moderate. If the mean value was higher (e.g., M = 8.33), you would conclude that participants’ self-esteem was, on average, high; and if the mean value was lower (e.g., M = 2.44), you would conclude that average self-esteem scores were low.

TABLE 1. Descriptive statistics for all variables used in research:

Let’s now return to our second research example and say that you want to report the degree to which males and females want to read a romantic novel, where this determination was assessed on a 1-10 (continuous) scale. This would look as shown in TABLE 2.

TABLE 2. Descriptive statistics for the determination to read the book, by gender:

We can see how to report frequencies statistics for different groups by referring to our second example about gender, determination to read a romantic novel, and participants’ relationship status.

Here, you have three categorical variables (if determination to read the novel was assessed by having participants reply with “yes” or “no”). Thus, you are not reporting means and standard deviations, but frequencies and percentages.

To put this another way, you are noting how many males versus females wanted to read the book and how many of them were in a relationship, as shown in TABLE 3. You can report these statistics in this way:

Twenty (40%) male participants wanted to read the book and 35 (70%) female participants wanted to read the book. Moreover, 22 (44%) males and 26 (52%) females indicated that they are currently in a relationship.

TABLE 3. Frequencies statistics for all variables used in research:

Reporting the results of a correlation analysis

The first of these is correlation, which you use when you want to establish if one or more (continuous, independent) variables relate to another (continuous, dependent) variable. For instance, you may want to see if participants’ height correlates with their self-esteem levels.

The first step here is to report whether your variables are normally distributed. You do this by looking at a histogram that describes your data. If the histogram has a bell-shaped curve (see purple graph below), your data is normally distributed and you need to rely on a Pearson correlation analysis. Here, you need to report the obtained r value (correlation coefficient) and p value (which needs to be lower than .05 in order to establish significance). If you find a correlation, you need to say something like:

The results of the Pearson correlation analysis revealed that there was a positive correlation between people’s height and their self-esteem levels ( r = .44, p < .001).

One final thing to note, which is important for all analyses, is that when your p value is indicated to be .000, you never report it by saying “ p = .000”, but by noting “ p p = .011”.

If your data is skewed rather than normally distributed (see red graphs), then you need to rely on a Spearman correlation analysis. Here, you report the results by saying:

Spearman correlation analysis revealed a positive relationship between people’s height and their self-esteem (r s = .44, p There has been a significant positive correlation between height and self-esteem after controlling for participants’ weight ( r = .39, p = .034).

You also need to make a table that will summarise your main results. If you didn’t use a covariate, you will have a fairly simple table, such as that shown in TABLE 4. If you have used a covariate, your table is slightly more complex, such as that shown in TABLE 5. Note that both tables use “-” to indicate correlations that have already been noted within the table. Also note how “*”, “**”, and “***” are used to annotate correlations that are significant at different levels.

TABLE 4. Correlations between all variables used in research:

Reporting the results of a regression

These are the specific points that you need to address in order to make sure that all assumptions have been met:

(1) for the assumption of no multicollinearity (i.e., a lack of high correlation between your independent variables), you need to establish that none of your Tolerance statistics are below .01 and none of the VIF statistics are above 10;

(2) for the assumption of no autocorrelation of residuals (i.e., a lack of correlation between the residuals of two observations), you need to look at this table and see whether your Durbin-Watson statistic falls within a desirable range, depending on your number of participants and the number of predictors (independent variables); and,

(3) for the assumptions of linearity (i.e., a linear relationship between independent and dependent variables) and homoscedasticity (i.e., a variance of error terms that should be similar across the independent variables), you need to look at the scatterplot of standardised residual on standardised predicted value and conclude that your graph doesn’t funnel out or curve.

All of this may sound quite complex. But in reality it is not: you just need to look at your results output to note the Tolerance and VIF values, Durbin-Watson value, and the scatterplot. Once you conclude that your assumptions have been met, you write something like:

Since none of the VIF values were below 0.1 and none of the Tolerance values were above 10, the assumption of no multicollinearity has been met. Durbin-Watson statistics fell within an expected range, thus indicating that the assumption of no autocorrelation of residuals has been met as well. Finally, the scatterplot of standardised residual on standardised predicted value did not funnel out or curve, and thus the assumptions of linearity and homoscedasticity have been met as well.

If your assumptions have not been met, you need to dig a bit deeper and understand what this means. A good idea would be to read the chapter on regression (and especially the part about assumptions) written by Andy Field. You can access his book here . This will help you understand all you need to know about the assumptions of a regression analysis, how to test them, and what to do if they have not been met.

Now let’s focus on reporting the results of the actual regression analysis. Let’s say that you wanted to see if participants’ height predicts their self-esteem, after controlling for participants’ weight. You have entered height and weight as predictors in the model and self-esteem as a dependent variable.

First, you need to report whether the model reached significance in predicting self-esteem scores. Look at the results of an ANOVA analysis in your output and note the F value, degrees of freedom for the model and for residuals, and significance level. These values are shown in PICTURE 2.

PICTURE 2. Results of ANOVA for regression:

PICTURE 3. Model summary for regression:

Significance value tells you if your predictor reached significance – such as whether participants’ height predicted self-esteem scores. You also need to comment upon the β value. This value represents the change in the outcome associated with a unit change in the predictor . Thus, if your β value is .351 for participants’ height (predictor/independent variable), then this means that for every increase in height by 1 cm, self-esteem increases by .35. You need to report the same thing for your other predictor – that is, participants’ weight.

Finally, since your model included both height and weight as predictors, and height acted as a significant predictor, you can conclude that participants’ height influences their self-esteem after controlling for weight.

PICTURE 4. Regression coefficients:

The model reached significance, meaning that it successfully predicted self-esteem scores (F (1,40) = 99.59, p The model explained 33.5% of variance in self-esteem scores. Participants’ self-esteem was predicted by their weight (β = .35, t = -8.13, p For every increase in weight by 1 kg, self-esteem decreased by 35. Participants’ self-esteem was also predicted by their height (β = .58, t = 17.80, p after controlling for their weight. For every increase in height by 1 cm, self-esteem increased by .58.

Reporting the results of a chi-square analysis

For instance, you would do a chi-square analysis when you want to see whether gender (categorical independent variable with two levels: males/females) affects the determination to read a book, when this variable is measured with yes/no answers (categorical dependent variable with two levels: yes/no).

When reporting your results, you should first make a table as shown in TABLE 3 above. Then you need to report the results of a chi-square test, by noting the Pearson chi-square value, degrees of freedom, and significance value. You can see all these in your output.

Finally, you need to report the strength of the association, for which you need to look at the Phi and Cramer’s V values. When each of your variables has only two categories, as in the present example, Phi and Cramer’s V values are identical and it doesn’t matter which one you will report. However, when one of your variables has more than two categories, it is better to report the Cramer’s V value. You report these values by indicating the actual value and the associated significance level.

Note that Cramer’s V value can range from 0 to 1. The closer the value is to 1, the higher the strength of the association. You can report the results of the chi-square analysis in the following way:

There was a significant association between gender and the determination to read the romantic novel (x 2 (1) = 25.36, p Cramer’s V value was significant (Cramer’s V = .75, p and it indicated a high strength of the association.

Reporting the results of a t-test analysis

Recall that you have previously outlined descriptive statistics for these variables, where you have noted means and standard deviations for males’ and females’ scores on the determination to read the novel (see TABLE 2 above). Now you need to report the obtained t value, degrees of freedom, and significance level – all of which you can see in your results output.

You can say something like:

Males reported a lower determination to read the novel (M = 3.20, SD = .43) when compared to females (M = 6.33, SD = 1.36). The results of a t-test analysis revealed that this difference reached significance (t (54) = 4.47, p < .001).

Reporting the results of one-way ANOVA

In the t-test example, you had two conditions of a categorical independent variable, which corresponded to whether a participant was male or female. You would have three conditions of an independent variable when assessing whether relationship status (independent variable with three levels: single, in a relationship, and divorced) affects the determination to read a romantic novel (as assessed on a 1-10 scale).

Here, you would report the results in a similar manner to that of a t -test. You first report the means and standard deviations on the determination to read the book for all three groups of participants, by saying who had the highest and lowest mean. Then you report the results of the ANOVA test by reporting the F value, degrees of freedom (for within-subjects and between-subjects comparisons), and the significance value.

There are two things to note here. First, before reporting your results, you need to look at your output to see whether the so-called Levene’s test is significant. This test assesses the homogeneity of variance – the assumption being that all comparison groups should have the same variance. If the test is non-significant, the assumption has been met and you are reporting the standard F value.

However, if the test is significant, the assumption has been violated and you need to report instead the Welch statistic, associated degrees of freedom, and significance value (which you will see in your output; for example, see PICTURE 3 above).

The second thing to note is that ANOVA tells you only whether there were significant differences between groups – but if there are differences, it doesn’t tell you where these differences lie. For this, you need to conduct a post-hoc comparison (Tukey’s HSD test). The output will tell you which comparisons are significant.

You can report your results in the following manner:

Single participants were most determined to read the book (M = 7.11, SD = .45), followed by divorced participants (M = 5.11, SD = .55) and participants who are in a relationship (M = 4.95, SD = .44). ANOVA revealed significant between-groups differences (F (2,12) = 5.12, p = .004). Post-hoc comparisons revealed that significant differences occurred between participants who were single and in a relationship ( p = .003) and between single and divorced participants ( p = .004). There were no significant differences between divorced and in-a-relationship participants (p = .067).

Reporting the results of ANCOVA

For instance, you will use ANCOVA when you want to test whether relationship status (categorical independent variable with three levels: single, in a relationship, divorced) affects the determination to read a romantic novel (continuous dependent variable, assessed on a 1-10 scale) after controlling for participants’ general interest in books (continuous covariate, assessed on a 1-10 scale).

To report the results, you need to look at the “test of between-subjects effects” table in your output. You need to report the F values, degrees of freedom (for each variable and error), and significance values for both the covariate and the main independent variable. As with ANOVA, a significant ANCOVA doesn’t tell you where the differences lie. For this, you need to conduct planned contrasts and report the associated significance values for different comparisons.

You can report the results in the following manner:

The covariate, general interest in books, was significantly related to the determination to read the romantic novel (F (1,26) = 4.96, p There was also a significant effect of relationship status on the determination to read the romantic novel, after controlling for the effect of the general interest in books (F (2,26) = 4.14, p Planned contrasts revealed that being single significantly increased the determination to read the book when compared to being in a relationship (t (26) = 2.77, p = .004) and when compared to being divorced (t (26) = 1.89, p = .003).

Reporting the results of MANOVA

For instance, you would use MANOVA when testing whether male versus female participants (independent variable) show a different determination to read a romantic novel (dependent variable) and a determination to read a crime novel (dependent variable).

When reporting the results, you first need to notice whether the so-called Box’s test and Levene’s test are significant. These tests assess two assumptions: that there is an equality of covariance matrices (Box’s test) and that there is an equality of variances for each dependent variable (Levene’s test).

Both tests need to be non-significant in order to assess whether your assumptions are met. If the tests are significant, you need to dig deeper and understand what this means. Once again, you may find it helpful to read the chapter by Andy Field on MANOVA, which can be accessed here .

Following this, you need to report your descriptive statistics, as outlined previously. Here, you are reporting the means and standard deviations for each dependent variable, separately for each group of participants. Then you need to look at the results of “multivariate analyses”.

You will notice that you are presented with four statistic values and associated F and significance values. These are labelled as Pillai’s Trace, Wilks’ Lambda, Hotelling’s Trace, and Roy’s Largest Root. These statistics test whether your independent variable has an effect on the dependent variables. The most common practice is to report only the Pillai’s Trace. You report the results in the same manner as reporting ANOVA, by noting the F value, degrees of freedom (for hypothesis and error), and significance value.

However, you also need to report the statistic value of one of the four statistics mentioned above. You can label the Pillai’s Trace statistic with V, the Wilks’ Lambda statistic with A, the Hotelling’s Trace statistic with T, and Roy’s Largest Root statistic with Θ (but you need report only one of them).

Finally, you need to look at the results of the Tests of Between-Subjects Effects (which you will see in your output). These tests tell you how your independent variable affected each dependent variable separately. You report these results in exactly the same way as in ANOVA.

Here’s how you can report all results from MANOVA:

Males were less determined to read the romantic novel (M = 4.11, SD = .58) when compared to females (M = 7.11, SD = .43). Males were also more determined to read the crime novel (M = 8.12. SD = .55) than females (M = 5.22, SD = .49). Using Pillai’s Trace, there was a significant effect of gender on the determination to read the romantic and crime novel (V = 0.32, F (4,54) = 2.56, p = .004). Separate univariate ANOVAs on the outcome variables revealed that gender had a significant effect on both the determination to read the romantic novel (F (2,27) = 9.73, p = .003) and the determination to read the crime novel (F (2,27) = 5.23, p = .038).

Writing up your qualitative results

Before reporting the results of your qualitative research, you need to recall what type of research you have conducted. The most common types of qualitative research are interviews, observations, and focus groups – and your research is likely to fall into one of these types.

All three types of research are reported in a similar manner. Still, it may be useful if we focus on each of them separately.

Reporting the results of interviews

For example, let’s say that your qualitative research focused on young people’s reasons for smoking. You have asked your participants questions that explored why they started smoking, why they continue to smoke, and why they wish to quit smoking. Since your research was organised in this manner, you already have three major themes: (1) reasons for starting to smoke, (2) reasons for continuing to smoke, and (3) reasons for quitting smoking. You then explore particular reasons why your participants started to smoke, why they continue to smoke, and why they want to quit. Each reason that you identify will act as a subtheme.

When reporting the results, you should organise your text in subsections. Each section should refer to one theme. Then, within each section, you need to discuss the subthemes that you discovered in your data.

Let’s say that you found that young people started smoking because: (1) they thought smoking was cool, (2) they experienced peer pressure, (3) their parents modelled smoking behaviour, (4) they thought smoking reduces stress, and (5) they wanted to try something new. Now you have five subthemes within your “reasons for starting to smoke” theme. What you need to do now is to present the findings for each subtheme, while also reporting quotes that best describe your subtheme. You do that for each theme and subtheme.

It is also good practice to make a table that lists all your themes, subthemes, and associated quotes.

Here’s an example of how to report a quote within a text:

Several participants noted that they started smoking because they thought smoking was cool. One participant said: “I was only 15 at the time and I was looking at these older boys whom everybody considered as cool. I was shy and I always wanted to be more noticed. So I thought that, if I start smoking, I will be more like these older boys” (interview 1, male).

Reporting the results of observations

For instance, you might have noticed that the therapist finds it important to discuss: (1) the origin of the problem, (2) the lack of a patient’s medical difficulties, (3) the experience of stress, (4) the link of stress to the problem, and (5) the new understanding of the problem. You can consider these as themes in your observations.

Accordingly, you will want to report each theme separately. You do this by outlining your observation first (this can be a conversation or a behaviour that you observed), and then commenting upon it.

Here’s an example:

Therapist: Was there something that stressed you out during the last few months?

Patient: Yes, of course. I thought I would lose my job, but that passed. After that, I was breaking up with my girlfriend. But between those things, I was fine.

Therapist: And was there any difference in your symptoms while you were and while you were not stressed?

Patient: Hmmm. Actually yes. Now that I think of it, they were mostly present when I went through those periods.

Therapist: Could it be that stress intensifies your symptoms?

Patient: I never thought of it. I guess it seems logical. Is it?

In this example, the therapist has tried to make a connection between the patient’s symptoms and stress. She did not explicitly tell the patient “your symptoms are caused by stress”. Instead, she has guided him, through questions, to connect his symptoms to stress. This seems beneficial because the patient has arrived at the link between stress and symptoms himself.

Reporting the results of focus groups

As an example, let’s say that your focus group dealt with identifying reasons why some people prefer Coca-Cola over Schweppes, and vice versa. You have transcribed your focus group sessions and have extracted themes from the data. You have discovered a wide variety of reasons why people prefer one of the two drinks.

When reporting your results, you should have two sections: one listing reasons for favouring Coca-Cola, and the other listing reasons for favouring Schweppes. Within each section, you need to identify specific reasons for these preferences. You should connect these specific reasons to particular quotes.

Here’s an example of how this may look:

The first reason why some participants favoured Schweppes over Coca-Cola is that Schweppes is considered as less sweet. Several participants agreed on this notion. One said: “I don’t like Coca-Cola because it is simply too sweet. Schweppes has a much more bitter taste, and I don’t feel like I am getting stuffed with sugar” (participant 2, female). Another participant agreed by noting: “I completely agree with what she said. Because Coca-Cola is sweet, I feel like I have taken a candy, and this doesn’t refresh me. A glass of cold Schweppes is much more refreshing. I don’t feel like needing water after it” (participant 4, male).

In conclusion…

As we have seen, writing up qualitative results is easier than writing quantitative results. Yet, even reporting statistics is not that hard, especially if you have a good guide to help you.

Hopefully, this guide has reduced your worries and increased your confidence that you can write up the results section of your dissertation without too many difficulties.

How to correctly reference a dissertation

How to finish and format your dissertation, dissertation writing: publishing a dissertation.

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Writing your Dissertation:  Results and Discussion

When writing a dissertation or thesis, the results and discussion sections can be both the most interesting as well as the most challenging sections to write.

You may choose to write these sections separately, or combine them into a single chapter, depending on your university’s guidelines and your own preferences.

There are advantages to both approaches.

Writing the results and discussion as separate sections allows you to focus first on what results you obtained and set out clearly what happened in your experiments and/or investigations without worrying about their implications.This can focus your mind on what the results actually show and help you to sort them in your head.

However, many people find it easier to combine the results with their implications as the two are closely connected.

Check your university’s requirements carefully before combining the results and discussions sections as some specify that they must be kept separate.

Results Section

The Results section should set out your key experimental results, including any statistical analysis and whether or not the results of these are significant.

You should cover any literature supporting your interpretation of significance. It does not have to include everything you did, particularly for a doctorate dissertation. However, for an undergraduate or master's thesis, you will probably find that you need to include most of your work.

You should write your results section in the past tense: you are describing what you have done in the past.

Every result included MUST have a method set out in the methods section. Check back to make sure that you have included all the relevant methods.

Conversely, every method should also have some results given so, if you choose to exclude certain experiments from the results, make sure that you remove mention of the method as well.

If you are unsure whether to include certain results, go back to your research questions and decide whether the results are relevant to them. It doesn’t matter whether they are supportive or not, it’s about relevance. If they are relevant, you should include them.

Having decided what to include, next decide what order to use. You could choose chronological, which should follow the methods, or in order from most to least important in the answering of your research questions, or by research question and/or hypothesis.

You also need to consider how best to present your results: tables, figures, graphs, or text. Try to use a variety of different methods of presentation, and consider your reader: 20 pages of dense tables are hard to understand, as are five pages of graphs, but a single table and well-chosen graph that illustrate your overall findings will make things much clearer.

Make sure that each table and figure has a number and a title. Number tables and figures in separate lists, but consecutively by the order in which you mention them in the text. If you have more than about two or three, it’s often helpful to provide lists of tables and figures alongside the table of contents at the start of your dissertation.

Summarise your results in the text, drawing on the figures and tables to illustrate your points.

The text and figures should be complementary, not repeat the same information. You should refer to every table or figure in the text. Any that you don’t feel the need to refer to can safely be moved to an appendix, or even removed.

Make sure that you including information about the size and direction of any changes, including percentage change if appropriate. Statistical tests should include details of p values or confidence intervals and limits.

While you don’t need to include all your primary evidence in this section, you should as a matter of good practice make it available in an appendix, to which you should refer at the relevant point.

For example:

Details of all the interview participants can be found in Appendix A, with transcripts of each interview in Appendix B.

You will, almost inevitably, find that you need to include some slight discussion of your results during this section. This discussion should evaluate the quality of the results and their reliability, but not stray too far into discussion of how far your results support your hypothesis and/or answer your research questions, as that is for the discussion section.

See our pages: Analysing Qualitative Data and Simple Statistical Analysis for more information on analysing your results.

Discussion Section

This section has four purposes, it should:

• Interpret and explain your results
• Answer your research question
• Justify your approach
• Critically evaluate your study

The discussion section therefore needs to review your findings in the context of the literature and the existing knowledge about the subject.

You also need to demonstrate that you understand the limitations of your research and the implications of your findings for policy and practice. This section should be written in the present tense.

The Discussion section needs to follow from your results and relate back to your literature review . Make sure that everything you discuss is covered in the results section.

Some universities require a separate section on recommendations for policy and practice and/or for future research, while others allow you to include this in your discussion, so check the guidelines carefully.

Starting the Task

Most people are likely to write this section best by preparing an outline, setting out the broad thrust of the argument, and how your results support it.

You may find techniques like mind mapping are helpful in making a first outline; check out our page: Creative Thinking for some ideas about how to think through your ideas. You should start by referring back to your research questions, discuss your results, then set them into the context of the literature, and then into broader theory.

This is likely to be one of the longest sections of your dissertation, and it’s a good idea to break it down into chunks with sub-headings to help your reader to navigate through the detail.

Fleshing Out the Detail

Once you have your outline in front of you, you can start to map out how your results fit into the outline.

This will help you to see whether your results are over-focused in one area, which is why writing up your research as you go along can be a helpful process. For each theme or area, you should discuss how the results help to answer your research question, and whether the results are consistent with your expectations and the literature.

The Importance of Understanding Differences

If your results are controversial and/or unexpected, you should set them fully in context and explain why you think that you obtained them.

Your explanations may include issues such as a non-representative sample for convenience purposes, a response rate skewed towards those with a particular experience, or your own involvement as a participant for sociological research.

You do not need to be apologetic about these, because you made a choice about them, which you should have justified in the methodology section. However, you do need to evaluate your own results against others’ findings, especially if they are different. A full understanding of the limitations of your research is part of a good discussion section.

At this stage, you may want to revisit your literature review, unless you submitted it as a separate submission earlier, and revise it to draw out those studies which have proven more relevant.

Conclude by summarising the implications of your findings in brief, and explain why they are important for researchers and in practice, and provide some suggestions for further work.

You may also wish to make some recommendations for practice. As before, this may be a separate section, or included in your discussion.

The results and discussion, including conclusion and recommendations, are probably the most substantial sections of your dissertation. Once completed, you can begin to relax slightly: you are on to the last stages of writing!

Continue to: Dissertation: Conclusion and Extras Writing your Methodology

See also: Writing a Literature Review Writing a Research Proposal Academic Referencing What Is the Importance of Using a Plagiarism Checker to Check Your Thesis?

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• How to Write a Discussion Section | Tips & Examples

How to Write a Discussion Section | Tips & Examples

Published on August 21, 2022 by Shona McCombes . Revised on July 18, 2023.

The discussion section is where you delve into the meaning, importance, and relevance of your results .

It should focus on explaining and evaluating what you found, showing how it relates to your literature review and paper or dissertation topic , and making an argument in support of your overall conclusion. It should not be a second results section.

There are different ways to write this section, but you can focus your writing around these key elements:

• Summary : A brief recap of your key results
• Interpretations: What do your results mean?
• Implications: Why do your results matter?
• Limitations: What can’t your results tell us?
• Recommendations: Avenues for further studies or analyses

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Table of contents

What not to include in your discussion section, step 1: summarize your key findings, step 2: give your interpretations, step 3: discuss the implications, step 4: acknowledge the limitations, step 5: share your recommendations, discussion section example, other interesting articles, frequently asked questions about discussion sections.

There are a few common mistakes to avoid when writing the discussion section of your paper.

• Don’t introduce new results: You should only discuss the data that you have already reported in your results section .
• Don’t make inflated claims: Avoid overinterpretation and speculation that isn’t directly supported by your data.
• Don’t undermine your research: The discussion of limitations should aim to strengthen your credibility, not emphasize weaknesses or failures.

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Start this section by reiterating your research problem and concisely summarizing your major findings. To speed up the process you can use a summarizer to quickly get an overview of all important findings. Don’t just repeat all the data you have already reported—aim for a clear statement of the overall result that directly answers your main research question . This should be no more than one paragraph.

Many students struggle with the differences between a discussion section and a results section . The crux of the matter is that your results sections should present your results, and your discussion section should subjectively evaluate them. Try not to blend elements of these two sections, in order to keep your paper sharp.

• The results indicate that…
• The study demonstrates a correlation between…
• This analysis supports the theory that…
• The data suggest that…

The meaning of your results may seem obvious to you, but it’s important to spell out their significance for your reader, showing exactly how they answer your research question.

The form of your interpretations will depend on the type of research, but some typical approaches to interpreting the data include:

• Identifying correlations , patterns, and relationships among the data
• Discussing whether the results met your expectations or supported your hypotheses
• Contextualizing your findings within previous research and theory
• Explaining unexpected results and evaluating their significance
• Considering possible alternative explanations and making an argument for your position

You can organize your discussion around key themes, hypotheses, or research questions, following the same structure as your results section. Alternatively, you can also begin by highlighting the most significant or unexpected results.

• In line with the hypothesis…
• Contrary to the hypothesized association…
• The results contradict the claims of Smith (2022) that…
• The results might suggest that x . However, based on the findings of similar studies, a more plausible explanation is y .

As well as giving your own interpretations, make sure to relate your results back to the scholarly work that you surveyed in the literature review . The discussion should show how your findings fit with existing knowledge, what new insights they contribute, and what consequences they have for theory or practice.

Ask yourself these questions:

• Do your results support or challenge existing theories? If they support existing theories, what new information do they contribute? If they challenge existing theories, why do you think that is?
• Are there any practical implications?

Your overall aim is to show the reader exactly what your research has contributed, and why they should care.

• These results build on existing evidence of…
• The results do not fit with the theory that…
• The experiment provides a new insight into the relationship between…
• These results should be taken into account when considering how to…
• The data contribute a clearer understanding of…
• While previous research has focused on  x , these results demonstrate that y .

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Even the best research has its limitations. Acknowledging these is important to demonstrate your credibility. Limitations aren’t about listing your errors, but about providing an accurate picture of what can and cannot be concluded from your study.

Limitations might be due to your overall research design, specific methodological choices , or unanticipated obstacles that emerged during your research process.

Here are a few common possibilities:

• If your sample size was small or limited to a specific group of people, explain how generalizability is limited.
• If you encountered problems when gathering or analyzing data, explain how these influenced the results.
• If there are potential confounding variables that you were unable to control, acknowledge the effect these may have had.

After noting the limitations, you can reiterate why the results are nonetheless valid for the purpose of answering your research question.

• The generalizability of the results is limited by…
• The reliability of these data is impacted by…
• Due to the lack of data on x , the results cannot confirm…
• The methodological choices were constrained by…
• It is beyond the scope of this study to…

Based on the discussion of your results, you can make recommendations for practical implementation or further research. Sometimes, the recommendations are saved for the conclusion .

Suggestions for further research can lead directly from the limitations. Don’t just state that more studies should be done—give concrete ideas for how future work can build on areas that your own research was unable to address.

• Further research is needed to establish…
• Future studies should take into account…
• Avenues for future research include…

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

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In the discussion , you explore the meaning and relevance of your research results , explaining how they fit with existing research and theory. Discuss:

• Your  interpretations : what do the results tell us?
• The  implications : why do the results matter?
• The  limitation s : what can’t the results tell us?

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

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

In a thesis or dissertation, the discussion is an in-depth exploration of the results, going into detail about the meaning of your findings and citing relevant sources to put them in context.

The conclusion is more shorter and more general: it concisely answers your main research question and makes recommendations based on your overall findings.

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McCombes, S. (2023, July 18). How to Write a Discussion Section | Tips & Examples. Scribbr. Retrieved September 3, 2024, from https://www.scribbr.com/dissertation/discussion/

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Need editing and proofreading services, how to create the perfect thesis title page in 2024.

• Tags: Academic Writing , APA Style , Chicago Style , Formatting Guidelines , MLA Style

Create the perfect thesis title page with our practical guide! From important elements to include in a title page to formatting guidelines, we’ve covered everything. 

For those who wish to quickly create a title page, we’ve also mentioned popular title page generators. So let’s start by understanding the components of a thesis title page!  

Get a well-edited thesis with zero errors! Get started

What to include in a thesis title page? 

A thesis title page contains important details such as the thesis title, author’s name, institution’s name, submission date, department name, and degree program’s name. It’s important to refer to your university’s guidelines about the thesis title page. 

In many cases, the title page for a thesis can also include the following components:

• Student number: Often placed below the author’s name or in the bottom corner.
• Instructor’s name: Usually positioned in the middle or lower third of the page.
• Institution’s logo: Typically at the top of the page, either centered or in a corner.
• Location: If included, usually placed at the bottom of the page.
• Committee members: Names of other faculty serving on the thesis committee.
• Copyright statement: A declaration of the author’s rights to the work.
• Submission statement: A formal statement indicating the thesis is submitted in partial fulfillment of degree requirements.
• Disclaimer: If necessary, a statement about the confidentiality or restricted use of the thesis.
• Funding acknowledgment: Mention any grants or scholarships that supported the research.

Now let us see the formatting guidelines for a thesis title page. 

How to format a thesis title page? 

It’s essential to crosscheck the institution’s guidelines for the thesis title page format. There should be consistency and uniformity regarding the formatting of the margins, text alignment, and font size. 

Now let’s understand the basic formatting guidelines for creating a title page according to APA, MLA, and Chicago style guides. 

Guidelines for formatting an APA title page 

The format of an APA title page differs, depending on whether it’s required for a student paper or a professional paper. While a professional paper requires a running head (title mentioned on top of every document page), student papers don’t require it unless it’s specified by the university.

Here are some general guidelines for formatting an APA title page:

• Maintain a 1-inch margin on all sides. 
• Apply double spacing consistently. 
• Include the running head on the left. (for professional papers) 
• Add the page number on the right side. 
• Centrally align your thesis paper’s title. 
• Use fonts like Times New Roman, Calibri, Arial, Lucida Sans Unicode, Georgia, and Computer Modern. 

You can read the article “ How to Create an APA Title Page ” to know more. 

Guidelines for creating an MLA title page 

• Write the author’s name first, followed by the instructor’s name, course name, subject name, and title. 
• For a thesis involving multiple authors, list every author’s name on a separate line.
• Centrally align your paper’s title.
• Except for the paper’s title, align all important details to the left. 
• Use italics to format the title of media works, including books, movies, and TV series. 

To know in detail, you can read the article “ How to Create an MLA Title Page ”. 

Guidelines for creating a Chicago-style title page 

• Centrally align the title of your thesis paper. 
• After the title, write details such as the teacher’s name, course name, and date in 3 separate lines. Ensure that they are centrally aligned towards the bottom of the page. 
• Utilize the Times New Roman font (size- 12). 
• If there is a subtitle, end the title with a colon ( : ) and write the subtitle on the next line. 
• Omit the page number from the thesis paper’s title page. 

To know more about creating a title page in Chicago Style, you can read the article “ Chicago Title, Cover Page & Body | Paper Format Guidelines

Now, let’s see a thesis title page example to understand better! 

Thesis title page example 

If you want to see more templates for a thesis title page, we’ve got you covered. Explore the next section to know more! 

Thesis title page templates 

Access the following document to see various thesis title page templates! 

Thesis title page templates

Now, let’s quickly see five pointers to follow while creating a thesis paper title page. 

Bonus section: Important pointers 

Following are 5 important pointers for creating a thesis title page: 

• Use proper capitalization- Capitalize the title correctly and make the first letter capital for any nouns , verbs , adjectives , and adverbs in the title. 
• Avoid unnecessary decorative elements- Stick to a clean professional layout and avoid any unnecessary graphics and borders. 
• Carefully proofread- Double-check the title page for any spelling or grammatical errors . You can also use English corrector tools to do this! 
• Use a template for reference- Many universities provide a sample template. It’s better to refer to the template to ensure the correct thesis title page format. 
• Get feedback- If you’re unsure about any element of a thesis title page, you can consult your instructor or advisor about this. 

You can also use title page generators like Ivy Panda, Writing-Services.org, and Custom-Writing.org to create a title page. However, it’s essential to ensure that you follow your institution’s guidelines. 

This concludes our guide about the title page of a thesis! Once your title page is ready, the next step is to edit your paper. As experts in editing and proofreading services , we’d love to perfect your paper. 

Here are some more useful resources for you: 

• 2024’s Top 10 Thesis Statement Generators (Free Included!)
• The 10 Best Essential Resources for Academic Research
• Academic Writing in 2024: 5 Key Dos & Don’ts + Examples
• Best ChatGPT Prompts for Academic Writing (100+ Prompts!)
• The Ethics of Academic Research

Frequently Asked Questions

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Democracy and Foreign Direct Investment in BRICS-TM Countries for Sustainable Development

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• Published: 05 September 2024

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• Ibrahim Cutcu   ORCID: orcid.org/0000-0002-8655-1553 1 &
• Ahmet Keser   ORCID: orcid.org/0000-0002-1064-7807 2  

The study aims to examine the long-term cointegration between the democracy index and foreign direct investment (FDI). The sample group chosen for this investigation comprises BRICS-TM (Brazil, Russia, India, China, South Africa, Turkey [Türkiye], and Mexico) countries due to their increasing strategic importance and potential growth in the global economy. Data from 1994 to 2018 were analyzed, with panel data analysis techniques employed to accommodate potential structural breaks. The level of democracy serves as the independent variable in the model, while FDI is the dependent variable. Inflation and income per capita are considered control variables due to their impact on FDI. The analysis revealed a long-term relationship with structural breaks among the model’s variables. Democratic progress and FDI demonstrate a correlated, balanced relationship over time in these countries. Therefore, governments and policymakers in emerging economies aiming to attract FDI should account for structural breaks and the correlation between democracy and FDI. Furthermore, the Kónya causality tests revealed a causality from democracy to FDI at a 1% significance level in Mexico, 5% in China, and 10% in Russia. From FDI to democracy (DEMOC), there is causality at a 5% significance level in Mexico and a 10% significance level in Russia. Thus, the findings suggest that supporting democratic development with macroeconomic indicators in BRICS-TM countries will positively impact foreign direct capital inflows.

Graphical Abstract

Avoid common mistakes on your manuscript.

Introduction

Economies and governments require capital infusion to augment their production and employment levels. Underdeveloped and developing nations, despite having an abundance of land and labor, grapple with capital deficiencies. Consequently, these countries often seek foreign direct investment (FDI) to address this capital shortfall. Even emerging market economies are not immune to this phenomenon, with challenges intensifying globally post-COVID-19 pandemic. Khan et al. ( 2023 ) highlighted the pivotal role of institutional quality and good governance in attracting FDI. The need for FDI has grown exponentially in an increasingly globalized world characterized by interdependence among states. Democracy and the democratic status of states emerge as critical indicators of institutional quality. Kilci and Yilanci ( 2022 ) posit that the prolonged pandemic triggered the third most significant recession since the Great Depression of 1929 and the Global Financial Crisis of 2008–2009. Consequently, the demand for FDI has surged, positioning foreign investment as the foremost resource for fostering sustainable economic development. In light of the provided frame, this study addresses the following research questions:

What factors attract foreign direct investment to a country?

Which factors positively impact FDI?

Reviewing the existing literature reveals that scholars from diverse disciplines address similar questions using political variables like political stability and democracy levels or economic variables such as economic stability and natural resources . However, the impact of democracy on FDI is often overlooked . For example, studies by Baghestani et al. ( 2019 ) and Gür ( 2020 ) investigated variables like oil prices, exchange rates, exports, imports, and the global innovation index but seldom considered democracy’s role in attracting FDI . Similarly, studies examining the relationship between democracy and FDI, like those by Yusuf et al. ( 2020 ) and Ahmed et al. ( 2021 ), generally excluded data from BRICS-TM countries.

Li and Resnick ( 2003 ) assert that the two paramount features of modern international political economy are the proliferation of democracy and increased economic globalization . It has become apparent that FDI inflow is a manifestation of high-level globalization and the diffusion of democracy. According to the United Nations Conferences on Trade and Development (UNCTAD), 2002 data between 1990 and 2000, three-quarters of the total international foreign direct capital was directed toward democratic and developed countries (Busse, 2003 ).

The conceptualization of democracy, within both theoretical and historical frameworks, has been marked by inherent challenges (Suny, 2017 ). Aliefendioğlu ( 2005 ) defines democracy as the amalgamation of the ancient Greek terms “Demos” and “Kratos,” centered on the principle of self-governance by the people. In essence, democracy encompasses the utilization of popular sovereignty by and for the citizenry (Keser et al., 2023 ). Haydaroğlu and Gülşah ( 2016 ) contend that the contemporary manifestation of democracies is rooted in representative democracy, wherein individuals exercise their sovereignty by selecting representatives to act on their behalf. The spread of liberal or representative democracy is believed to be a driving force behind this shift in economic structures. The relational intersection between FDI flow and democratic mechanisms needs to be investigated. At this point, Voicu and Peral ( 2014 ) argue that economic development and modernization operate as background factors that affect the development of support for democracy. Therefore, an opinion emerges that there is an inevitable intersection between FDI flow and democratic mechanism.

Despite the sustained attention from academia and the public, the detailed understanding of democracy’s effect on FDI remains limited (Li & Resnick, 2003 ). There is a noticeable gap in the literature concerning studies investigating the impact of democracy on FDI, specifically in BRICS-TM countries , which are emerging markets that attract significant FDI. Moreover, the absence of structural break panel cointegration tests in previous analyses accentuates these gaps, forming the primary motivation for this research . The study aims to fill these voids by empirically examining the relationship between democracy and FDI using data from the emerging markets of BRICS-TM countries. These countries require substantial foreign capital and are crucial for the stable development of the global economy since they are expected to become pivotal centers in the multipolar world system. The study differs from other publications, employing unique methods, such as structural break panel cointegration tests, to address these objectives.

Reducing costs, increasing employment-oriented production, and enhancing export capacity are paramount in global competition. If a country cannot achieve these advancements with its existing potential and dynamics, attracting foreign capital becomes imperative, necessitating the creation of multiple attraction points to entice foreign direct investments. Consequently, attracting foreign capital is significant in today’s globalized world. This study provides insights into this pressing issue in the contemporary global competitive landscape by analyzing the long-term relationship between democracy and foreign direct investment. Considering their prominence in the world economy due to recent economic growth and competitive structures, the selection of BRICS-TM countries as a sample group underscores the study’s importance. The study acknowledges the strategic importance and increasing power of BRICS-TM countries, especially China and India, which have consistently attracted significant foreign capital in recent years. Using panel data analysis techniques that incorporate structural breaks addresses a crucial gap in the literature, offering a more accurate analysis of the democracy-foreign direct investment relationship in the BRICS-TM sample group. However, data constraints related to model variables alongside the limitations of evaluating results within the framework of the chosen sample group are acknowledged later in the “ Discussion ” section.

Lastly, there appears to be a gap in the existing literature concerning studies that investigate the impact of democracy on FDI flow in BRICS-TM countries . The countries that attract more FDI than others raise the question of whether their democracy level empirically influences the amount of FDI. Moreover, upon examining the limited studies exploring the relationship between democracy and FDI, it is evident that none applied the structural break panel cointegration test in their analyses. These gaps collectively serve as the primary motivation for this research. Thus, the study aims to address these gaps in the existing literature and scrutinizes whether there is cointegration between the level of democracy and FDI in a country by utilizing sample group data from emerging markets of BRICS-TM countries. This selection is significant as these countries are among emerging economies with considerable developmental potential. In essence, this study aims to empirically unveil the relationship between democracy and FDI , a crucial requirement for developing economies striving to attract more foreign capital for sustainable development . Additionally, this study employs distinctive methods, such as the structural break panel cointegration test, to investigate the subject, further elaborated in the “ Research Method and Econometric Analysis ” section.

In global competition, the imperative to reduce costs, increase employment-oriented production, and enhance export capacity is paramount. Given a country’s potential and dynamics, if these enhancements prove elusive, the necessity arises to attract foreign capital and establish various attraction points to incentivize foreign direct investments. Therefore, attracting foreign direct investment (FDI) to a country holds tremendous significance in today’s globalized world. Before investing, foreign capital rigorously assesses the potential profit opportunities and scrutinizes various socio-economic indicators, especially democracy. For these reasons, by analyzing the long-term relationship between democracy and foreign direct investment in the BRICS-TM sample, this study incorporates analyses and inferences regarding this crucial challenge in today’s globally competitive environment.

Furthermore, it is anticipated that the strategic importance and influence of BRICS-TM countries will continue to escalate in the upcoming years. Notably, countries in the sample group, particularly China and India, have consistently attracted substantial foreign capital, and their economies exhibit ongoing growth. As evident from the graphical analysis in the study, China stands out as the world leader in attracting foreign direct investment. Considering the economic size of Russia and Brazil, the geo-strategic location of Türkiye, and the natural resource wealth of China, India, and Mexico, it is apparent that these countries are central attractions for foreign direct capital. Events with significant consequences on the global stage, such as economic crises, wars, earthquakes, and elections, can induce substantial fluctuations and structural breaks in national economies. Hence, using panel data analysis techniques that allow for structural breaks in the study fills a critical gap in the literature. This approach provides a more accurate analysis of the democracy-foreign direct investment relationship in the BRICS-TM sample group. The primary limitation in the study’s analysis is the constraint arising from the variables included in the model. Additionally, selecting the BRICS-TM sample group as the focus on developing countries can be considered another limitation, restricting the evaluation of results within this specific sample framework. The study anticipates that the policy recommendations derived from the analysis findings will guide policymakers, market players, and new researchers.

The article is organized into the following sections: (1) “ Introduction ” section: This section initially furnishes broad information concerning the subject matter, elucidating the lacunae in the existing literature and delineating the limitations of the study. (2) “ Theoretical Frame and Literature Review ” section: Subsequently, the second section delves into the examination of the theoretical framework, scrutinizing the prevailing status of the literature. (3) “ Research Method and Econometric Analysis ” section: The third segment comprehensively addresses the research methodology employed and expounds upon the econometric analysis conducted. (4) “ Results ” section: The ensuing fourth chapter presents the study’s findings and results. (5) “ Discussion ” section: These results and findings are then systematically expounded upon in the fifth chapter within the context of the current literature. (6) “ Conclusion ” section: Culminating the study is a concluding section encapsulating the critical insights derived, followed by policy recommendations.

Theoretical Frame and Literature Review

As previously indicated, scarce studies have delved into the correlation between democracy and foreign direct investment (FDI). A comprehensive examination of the existing literature reveals a notable dearth of research focused on BRICS-TM countries, with most of them overlooking “democracy” as a variable and/or the connection between “democracy and FDI.” Conversely, researchers investigating FDI predominantly explore its associations with other variables, such as “exports and imports.”

The Status of the Literature on BRICS-TM Countries and Democracy and Foreign Direct Investment

The following two tables summarize the status of the current literature on the issue and its findings. In Table  1 , the literature on BRICS and/or BRIC + S + T + M countries, as well as its variables, methods, and findings, is given. Then, in Table  2 , the studies researching the relationship between democracy and FDI, their methodology, sample groups, and findings are summarized.

As can be seen in Table  1 , BRICS-TM countries were very rarely studied, and almost all of these studies neglected “democracy” as a variable and/or the relation between “democracy and FDI.” Alternatively, the studies that did examine FDI researched its relation with other variables such as export and import. Unique methods, such as structural break panel cointegration tests, were applied to investigate the issue, and this method comprises the novel part of the study. The details can be seen under the “ Research Method and Econometric Analysis ” section.

In summary, the literature review provided in Table  1 covers the relationship between democracy, foreign direct investment (FDI), and various other economic variables, focusing on BRICS-TM countries. Below is an analysis of the essential findings and gaps identified in the literature:

By applying AI (ChatGPT) to the information provided in Table  1 (studies on BRIC + S + T + M countries), key findings are double-checked and summarized below:

Limited focus on BRICS-TM countries: The literature review notes a scarcity of studies on BRICS-TM countries, with a lack of attention to the “democracy” variable in the context of FDI.

Variable relationships explored: Various studies investigate the relationships between different economic variables and FDI, such as oil prices, exchange rates, gross domestic product (GDP), international tourism, economic output, carbon emissions, exports, imports, and innovation.

Diverse methodologies: Researchers employ diverse methodologies, including directional analysis, panel ARDL cointegration, survey research, and panel cointegration, to analyze the relationships among variables.

Within this frame, a summary of the studies investigating the relationship between democracy and FDI or using similar variables is given in Table  2 .

As presented in Table  2 , none of the above studies analyzed the relationship among democracy, FDI, inflation , and GDP variables for BRICS-TM countries. In addition, none of the studies applied a structural break panel cointegration test in their analysis. All these gaps motivate the authors of this study to conduct such research.

Additionally, applying AI (ChatGPT) to the information provided in Table  2 , key findings from Table  2 are double-checked and summarized below (studies on the relationship between democracy and economics):

Limited studies on democracy and FDI in BRICS-TM: The literature highlights a gap in research, as none of the studies in Table  2 specifically analyze the relationship between democracy, FDI, inflation, and GDP variables in BRICS-TM countries.

Contradictory findings on democracy and economic growth: The studies in Table  2 present contradictory findings on the impact of democracy on economic growth. Some find a positive and significant effect, while others do not establish a significant relationship.

Methodological variety: Various methods, such as dynamic fixed effects, panel data regression analysis, panel cointegration, and causality analysis, are employed to explore the relationships between democracy, FDI, and economic growth.

Upon inspection of the limited studies, contradictory results emerge, even when employing data from diverse sample groups. An illustrative example is found in the work of Busse ( 2003 ), whose research can be summarized as follows:

Results from regression analysis between FDI and democracy reveal that analogous to studies by Rodrik ( 1996 ) and Harms and Ursprung ( 2002 ), multinational corporations (MNCs) exhibit a preference for countries where political rights and freedoms are legally and practically safeguarded.

Countries that enhance their democratic rights and freedoms tend to attract more FDI per capita than predicted (Busse, 2003 ).

Li and Resnick ( 2003 ) posited that investors typically favor regimes with advanced democracy and robust legal systems over states where their properties are at risk in dictatorial regimes. From this standpoint, one can infer that a significantly high level of democracy correlates with a markedly high level of FDI. In other words, property rights violations are diminished in developing countries with robust democracies, leading to increased FDI levels (Li & Resnick, 2003 ).

However, Haggard ( 1990 ) presents a contrary perspective, arguing that authoritarian regimes may appeal more to investors seeking to safeguard their economic assets and properties. An amalgamation of opposing views arises: investors from countries with underdeveloped democracies prefer collaboration with authoritarian regimes, whereas investors from developed nations lean toward familiar democratic regimes.

Despite the contradictory and complex findings from the limited number of studies on the potential relationship between democracy and FDI, it is contended that two influential factors contribute to investment flow toward countries with legally guaranteed and well-developed democratic rights. Firstly , as proposed by Spar ( 1999 ), a transition occurs from critical sectors like agriculture and raw materials to production and tertiary sectors in the flow and stock structure of FDI in developing countries. Secondly , there is a transformation in the interest and motivation of multinational enterprises toward developing countries based on sectoral development (Busse, 2003 ). This underscores the impact of democratic organizations established to secure democratic rights on FDI. In instances where poor democratic governance renders a country less appealing to foreign investors, the country faces a dilemma: choosing between the limited options of “loss of foreign capital” or “democratization” (Li & Resnick, 2003 ). Spar ( 1999 ) emphasizes that as the reliance on governments and their policies decreases, the need for a more democratic environment, a reliable and stable legal system, and appropriate market conditions becomes increasingly crucial for the overall well-being of the country’s economy.

Upon scrutinizing the most recent studies on the subject, a trend of contradictory findings becomes apparent. For instance, Yusuf et al. ( 2020 ) found that the democracy coefficient, as a variable signifying its impact on economic growth, lacks significance for West African countries in the short and long run. In contrast, Putra and Putri ( 2021 ) asserted that “democracy has a positive and significant effect on economic growth in 7 Asia Pacific countries.” Similar to Yusuf et al., in a panel data analysis encompassing the period from 1970 to 2014 and involving 115 developing countries, Lacroix et al. ( 2021 ) concluded that “democratic transitions do not affect foreign direct investment (FDI) inflows.”

A comprehensive review of existing empirical studies reveals a notable scarcity in the number of inquiries into the relationship between democracy and foreign direct investment (FDI) (Li & Resnick, 2003 ). Moreover, the available studies yield contradictory results on this matter. Addressing this issue, it is noteworthy that Oneal ( 1994 ) conducted one of the initial qualitative examinations on the impact of regime characteristics on FDI. Despite not identifying a statistically valid relationship between regime type and FDI flow, Oneal’s research is an early exploration of this intricate relationship.

Explorations into the connection between investor behavior and political regime characteristics, particularly in determining whether democratic or authoritarian features foster more foreign direct investment (FDI), have yielded divergent outcomes. Derbali et al. ( 2015 ) found a statistically significant relationship between FDI and democratic transformation. Through an econometric analysis encompassing a sample of 173 countries, with 44 undergoing democratic transformation between 1980 and 2010, the authors observed a substantial increase in FDI flow associated with democratic transitions.

Castro ( 2014 ) conducted a test examining the relationship between foreign direct investment (FDI) flow (the ratio of FDI flow to GDP) and indicators of “democracy” and “dictatorship” using a dynamic panel data model. Despite the analysis results failing to furnish evidence supporting a direct connection between FDI and democracy, the author emphasizes that this outcome does not negate the impact of political institutions on the flow of FDI. According to Mathur and Singh ( 2013 ), their study stands out as the inaugural examination focusing on the “importance given to economic freedom rather than political freedom” in the decision-making process of foreign investors. The authors concluded that contrary to conventional expectations, even democratic countries may attract less foreign direct investment (FDI) if they do not ensure guaranteed economic freedom. Malikane and Chitambara ( 2017 ) conducted a study exploring the relationship between democracy and foreign direct investment (FDI), employing data from eight South African countries from 1980–2014. The research findings indicate a direct and positive impact of FDI on economic growth due to the robust democratic institutions emerging as crucial catalysts in the respective sample countries.

Consequently, Malikane and Chitambara’s ( 2017 :92) study suggests that the influence of FDI on economic growth is contingent upon the level of democracy in the host country. Upon scrutinizing the studies above, a pattern of conflicting findings emerges concerning the relationship between the level of democracy and the influx of foreign direct investment (FDI) to a country . Studies commonly emphasize that the impact of democracy on FDI depends upon each country’s developmental stage. The prevalence of confusion, varying findings, and conflicting results underscores the significance of empirical analyses on this matter. A comprehensive examination of the overview identified gaps, and the need for new research is detailed under the subsequent subheading.

Overview of the Literature, Identified Gaps, and Requirements for New Research

After a detailed overview of the existing literature, the main features and gaps can be identified as follows:

Limited studies on democracy and FDI: The literature notes a scarcity of studies examining the relationship between democracy and FDI, and existing studies present conflicting results.

Context-dependent impact of democracy: Contradictory findings suggest that democracy’s impact on FDI may vary depending on a country’s development level.

Gap in BRICS-TM studies: The identified gap in the literature is the lack of research specifically addressing the relationship between democracy and FDI in BRICS-TM countries. The need for a structural break panel cointegration test is also emphasized.

Influence of political institutions: Some studies argue that solid democratic institutions positively influence FDI, while others suggest that economic freedom, rather than political freedom, may be more crucial for attracting FDI.

Requirements for new research: To fill the gap in the literature, new research should be conducted specifically targeting BRICS-TM countries.

Thus, when c onsidering the contradictory findings, future studies should explore the contextual factors influencing the relationship between democracy and FDI in different country settings. Conducting longitudinal analyses could provide insights into the dynamic relationship between democracy and FDI over time. Comparative studies between countries with different levels of democratic development can help in understanding the nuanced impact of democracy on FDI. Last but not least, given the emphasis on structural break panel cointegration tests, future research could incorporate these analytical tools for a more comprehensive understanding of the relationships under consideration.

Last but not least, Olorogun ( 2023 ) conducted research using data from sub-Saharan countries from 1978 to 2019 and found a “long-run covariance between sustainable economic development and foreign direct investment (FDI)” and a “significant level of causality between economic growth and financial development in the private sector, FDI, and export.” So, if a significant relationship can be found between democracy and foreign direct investment, the results may also provide a useful assessment for sustainable development.

In summary, while the literature review reveals valuable insights into the complex relationship between democracy, FDI, and economic variables, there is a clear need for more targeted research in the context of BRICS-TM countries by further exploration of the contextual factors influencing these relationships.

Research Method and Econometric Analysis

This section of the study delves into the analysis methods and interpretations of the relationship between democracy and foreign direct investment (FDI). The presentation encompasses the dataset and model specifications concerning the variables under scrutiny. Specifically, analyses were conducted utilizing econometric analysis programs, namely, EViews 12 , Gauss 23 , and StataMP 64 . The study culminated with interpreting findings and formulating policy recommendations based on the results obtained.

Data Set and Model

The study scrutinized the hypothesis to address the initial research inquiry, asserting a correlation between democracy and foreign direct investment (FDI). The research targeted BRICS-TM countries (Brazil, Russia, India, China, South Africa, Türkiye, Mexico) recognized for their increasing prominence in the global economy and anticipated growth in strategic significance. These seven emerging markets were chosen due to their demonstrated potential to attract FDI. The research covered annual data spanning 1994–2018 by employing panel data analysis techniques capable of accommodating structural breaks. Both democracy and foreign direct investments are susceptible to the influence of local and global dynamics, which can induce significant disruptions in the variables.

Consequently, the study utilized tests allowing for structural breaks to enhance the robustness of the analyses. The investigation aimed to uncover the long-term relationship between foreign direct investment and democracy , a critical indicator of economic development for emerging markets in recent years. The model developed for examining the relationship between democracy and foreign direct investment within the specified sample and data range is represented by Eq.  1 :

In the model, cross-section data is represented by i  = 1, 2, 3,…. N , while the time dimension is represented by t  = 1, 2, 3,….. T , and the error term is by ɛ.

The study’s model setup and variables were adapted from Yusuf et al. ( 2020 ), Putra and Putri ( 2021 ), and Lacroix et al. ( 2021 ) in the literature. Figure  1 shows the research design.

Research design

Table 3 shows the variables and data sources used in the model.

The study designated foreign direct investment (FDI), denoted as LNFDI, as the dependent variable. The independent variable was conceptualized as the democracy variable (DEMOC). To account for potential influencing factors, inflation (INF) and per capita income (PGDP) variables, known to impact FDI, were introduced into the model as control variables to draw upon insights from the existing literature. In the context of panel data analyses, selecting control variables involves consulting the literature to identify factors with substantial influence on the dependent variable. When examining factors impacting foreign direct investment (FDI), a frequently encountered category comprises various macroeconomic variables, among which inflation and per capita income are recurrently employed. Given the study’s sample composition—comprising the BRICS-TM countries—these two variables were incorporated into the model as control variables. This decision was motivated by their recurrent utilization in the literature and their direct relevance to foreign direct investments and production costs. Furthermore, the inclusion of these variables addressed a shared data constraint.

During the data collection phase, the study utilized indices reflecting “political rights” and “civil liberties,” which were acknowledged indicators of “democracy” in the literature. These indices, sourced from the Freedom House Database ( 2020 ), were incorporated into the analysis by calculating their means, which were then used as values for the democracy variable. This approach aligns with the practices of several researchers in the existing literature, such as Kebede and Takyi ( 2017 ), Doucoligaos and Ulubasoglu ( 2008 ), and Tavares and Wacziarg ( 2001 ), who have employed this index. The index operates on a scale from 1 to 7, where 1 represents the highest state of democracy and 7 corresponds to the lowest state. To facilitate analyses, calculations, and interpretation, the index values were scaled to ensure a range between 0 and 100.

Freedom House assesses the degree of democratic governance in 29 countries from Central Europe to Central Asia through its annual “Nations in Transit” report. The democracy score encompasses distinct ratings on various facets, including national and local governance, electoral processes, independent media, civil society, judicial framework and independence, and corruption. Most researchers (Dolunay et al., 2017 ; Martin et al., 2016 ; Osiewicz & Skrzypek, 2020 ; Steiner, 2016 ) frequently utilize the data provided by Freedom House in their studies. In addition to the independent variable of democracy (DEMOC), the model integrates control variables influencing FDI. Capitation (LNPGDP) and inflation (INF) variables were incorporated within this framework. A review of the existing literature reveals that factors affecting FDI, including inflation and per capita income, have been employed in models by researchers (Botric & Skuflic, 2005 ; Chakrabarti, 2001 ; Jadhav, 2012 ; Ranjan & Agraval, 2011 ; Vijayakumar et al., 2010 ).

In the literature, various variables such as “trade openness, level of human capital, unemployment rates, government supports, tax costs,” which are believed to influence foreign capital, are employed as control variables in models. On the other hand, in some research, the impact of institutional quality, such as democracy and governance, on environmental quality is studied. Within this frame, Shahbaz et al. ( 2023 ) found that “institutional quality variables impacted environmental quality differently. In this sense, it is detrimental for policymakers to consider concerted measures to decrease institutional vulnerabilities and reduce the level of the informal economy.” However, in this study, inflation and per capita income variables were chosen due to their prominence as the most frequently used variables in the literature (detailed in the “ Theoretical Frame and Literature Review ” section) and their comprehensive impact on foreign direct capital in terms of macroeconomics.

Furthermore, a shared data problem is evident in all variables from 1994 to 2018 for the BRICS-TM country sample group, particularly in variables other than the control variables in the model. Nevertheless, these issues have yet to be encountered as inflation and per capita income variables are comprehensive and fall within general macroeconomic data. Additionally, including many control variables in the model might obscure the significance of the effect on the dependent variable in hypothesis tests examining the relationship between democracy and foreign direct investment. Consequently, real GDP data, rather than nominal, were utilized in the analysis, and the logarithm of the data was represented as LNGDP.

As explored earlier, foreign investors prioritize economic freedom over political freedom when making investment decisions (Mathur & Singh, 2013 ). In this context, the assurance of economic liberty and the legal protection of property rights may be linked to the level of democracy, particularly in developed countries. This condition explains why the relevant variables should be incorporated into the model and tested. The logarithm of FDI (LNFDI) and per capita income (LNPGDP) variables were employed in the analyses. The rationale behind the logarithmic transformation lies in its capacity to facilitate the interpretation of analysis results and standardize variables on a specific scale. Additionally, taking logarithms of series does not result in information loss in data; it also aids in mitigating autocorrelation issues and allows the series to exhibit a normal distribution.

Econometric Method

The primary motivation behind the conducted study is to investigate the impact of the variable “democracy” on foreign direct investments through newly developed panel data analysis tests that allow for structural breaks, which are not commonly used in political science. In this regard, the study aims to be one of the pioneering works testing the relationship between variables related to political science and economics with an interdisciplinary perspective through innovative empirical studies. The methodological framework of this study, which analyzes the relationship between democracy and FDI through annual data from the 1994–2018 periods using panel data analysis and causality test, is outlined below:

Graphical representation of variables and analysis of descriptive statistics,

CD lm1 (Breusch & Pagan, 1980 ), CD lm1 , and LM adj tests (Pesaran et al., 2008 ) were used in the analysis to find the presence of cross-section dependence of variables.

Panel LM test (Im, Lee, & Tieslau, 2010 ) determined whether variables in the model have a unit root.

Delta test (Pesaran & Yamagata, 2008 ) was used to determine the homogeneity or heterogeneity of variables.

Cointegration test with multiple structural breaks (Westerlund & Edgerton, 2008 ) was conducted to determine the presence of cointegration between variables.

Kónya’s causality test (Kónya, 2006 ) was conducted to investigate the existence of causal relationships between variables.

In terms of methodology, the study aims to address a significant gap in the literature on democracy. Given the chosen sample group and the specified period, it becomes evident that structural changes must be considered in the analysis because the variables of democracy and foreign direct investment are particularly susceptible to global developments, leading to substantial shifts in the markets. A literature review indicates a preference for general country-based time series analyses over new-generation tests, with classical panel data analyses commonly employed for the selected country group. In summary, an examination of the literature reveals that studies on this issue predominantly rely on first- and second-generation linear panel data analysis techniques. Therefore, incorporating unit root and cointegration tests is crucial in significantly contributing to the literature, particularly by acknowledging and addressing structural breaks in the study. Additionally, it aligns with the theoretical framework that variables such as democracy and foreign direct capital investments, susceptible to the influence of global developments, are prone to structural changes. Consequently, employing panel data analysis techniques with structural breaks gains significance and enhances the motivation and scientific robustness of the study, mainly when a substantial data range is available.

The study focuses on the BRICS-TM countries: Brazil, Russia, India, China, South Africa, Türkiye Footnote 1 (Turkey), and Mexico . These nations have gained prominence in the global economy, and their strategic significance is anticipated to grow. The selection of this sample group is based on their demonstrated high performance and potential to attract substantial foreign direct investment globally. The study’s unique contribution lies in its examination of the impact of the democracy variable on foreign direct investments within this specific country group, employing innovative techniques not commonly found in the existing literature. Furthermore, the potential increase in foreign direct investment within these countries is expected to influence national and per capita incomes positively. The continuous enhancement of economic well-being and the rising accumulation of foreign direct investments could position these countries as new focal points of attraction in the medium and long term, fortifying their appealing characteristics.

Descriptive Statistics and Graphical Analysis of Variables

Graphical analyses provide valuable insights into the changes and fluctuations of variables over the years in econometric studies. The visual representation and interpretations of the study variables are presented in Fig.  2 .

Graphical representation of variables

The graphical analysis reveals the trend and volatility of FDI over the study period (1994–2018). Peaks and troughs may indicate significant events or economic shifts influencing FDI.

Democracy index: The graphical representation illustrates the changes in the democracy index across the selected countries. Distinct patterns or shifts may be observed, indicating periods of democratic development or regression.

Inflation (INF): The inflation variable is depicted graphically, highlighting its trajectory over the analyzed years. Fluctuations in inflation rates may correlate with economic events impacting FDI.

Per capita income (PGDP): The per capita income variable is visually presented, demonstrating its variations and trends. Per capita income changes can influence countries’ attractiveness for foreign investments.

These graphical analyses serve as a foundation for understanding the dynamics of the variables under investigation and provide a visual context for further econometric interpretations.

So Fig.  2 provides a comprehensive overview of the variables examined in the study. The following key observations can be made:

Foreign direct investment (FDI): China stands out as the leader in attracting the highest FDI among the BRICS-TM countries. South Africa exhibits the lowest FDI levels in the sample group.

Democracy index: China also holds the highest score in the democracy index, indicating its position as the most democratic among the selected countries. South Africa, on the other hand, has the lowest democracy index score.

Per capita income (PGDP): Russia demonstrates the highest per capita income among the countries, suggesting a relatively higher economic well-being. India, conversely, has the lowest per capita income in the sample group.

Inflation (INF): Russia and Türkiye experience the highest inflation rates, while other countries exhibit fluctuating patterns at lower and similar levels.

Table 4 provides a detailed overview of the descriptive statistics for the variables under consideration. The following key statistics offer insights into the central tendencies and variations within the sample group.

The analysis of the basic descriptive statistics in Table  4 yields several noteworthy findings:

Kurtosis values: The INF variable stands out with a kurtosis value exceeding 3, indicating a sharp peak and heavy tails in its distribution. All other variables exhibit kurtosis values below 3, suggesting relatively normal distributions without excessively heavy tails.

Skewness values: LNFDI and LNPGDP variables display negative skewness values, suggesting a longer left tail in their distributions. DEMOC and INF variables exhibit positive skewness values, indicating longer right tails in their distributions.

Jarque–Bera test: The Jarque–Bera test results indicate that the variables are statistically significant and deviate from a normal distribution. This departure from normality suggests that certain factors or events influence the distributions of the variables.

These findings provide insights into the shapes and characteristics of the variable distributions. As indicated by skewness and kurtosis values, the deviations from normality suggest that the variables may be subject to specific influences or events, contributing to their non-normal distributions. Researchers should consider these distributional characteristics when interpreting the results and drawing conclusions from the dataset.

Cross-section Dependence Test

The escalating interdependence among countries in global economies has rendered them susceptible to the impact of positive or negative developments in one nation affecting others. This phenomenon directly results from the deepening global integration associated with globalization. Consequently, econometric studies must incorporate cross-section dependence tests to gauge the extent of interaction between nations. Such tests aim to quantify how a shock in one country reverberates across borders, influencing other countries of the global economic landscape.

Studies addressing cross-section dependency (Andrews, 2005 ; Pesaran, 2006 ; Phillips & Sul, 2003 ) emphasize that failing to account for cross-section analysis may lead to biased and inconsistent results. Thus, all analyses should consider cross-sectional dependence in relevant studies (Breusch & Pagan, 1980 ; Pesaran, 2004 ).

The tests used to determine cross-section dependence were as follows:

When the time dimension is greater than the cross-section dimension ( T  >  N ), analyses were conducted using Breusch and Pagan’s ( 1980 ) CD lm1 test.

In cases when the time dimension is equal to the cross-section dimension ( T  =  N ), the CD lm2 test (Pesaran, 2004 ) was used to conduct analyses.

In cases when the time dimension was smaller than the cross-section dimension ( T  <  N ), analyses were conducted by CD lm test (Pesaran, 2004 ).

In cases when the time dimension is both smaller and greater than the cross-section dimension, analyses were conducted (LM adj ) test (Pesaran et al., 2008 ).

This study’s analysis focuses on the relationship between democracy and FDI across BRICS-TM countries, involving seven countries. With annual data spanning 1994–2018, the cross-section dimension is denoted by N  = 7 and the time dimension by T  = 25. Given that T  >  N , the study utilized the CD lm1 test (Breusch & Pagan, 1980 ) and CD lm1 and LM adj tests (Pesaran et al., 2008 ).

Given that T  >  N for the countries and time dimension, the decision-making is informed by the results of the CD lm1 and LM adj tests. Notably, LM adj test results were prioritized, considering the potential bias in cross-section dependency tests associated with the CD lm1 test. The findings of the cross-section dependence tests are presented in Table  5 .

Upon reviewing Table  5 , it is evident that the probability values for all variables are less than 0.01. Consequently, based on the LM adj test results, the null hypothesis stating “there is no dependence between sections” is rejected, while the alternative hypothesis suggesting “cross-section dependence between sections” is accepted.

The outcomes of the tests align with the characteristics of the contemporary global landscape, where any impactful event or development in one of the BRICS-TM countries has reverberations across others. Whether positive or negative, changes in one BRICS-TM nation can influence others, particularly in areas related to foreign direct investment (FDI) and democracy. As a result, policymakers in these countries should craft their future strategies with a keen awareness of this interconnectedness and the potential spillover effects on FDI and democracy. Indeed, the obtained result is consistent with theoretical expectations. The observed interdependence and influential power of the BRICS-TM country group align with the current dynamics of the globalized world. Their growing significance in the world economy and their strategic importance reinforces the decision that developments within these countries have substantial implications beyond their borders. This outcome urges the need for a nuanced approach to respond to the interconnected nature of these nations in the contemporary global landscape.

Panel Unit Root Test

In the initial phase of the econometric analysis, the stationarity of the variables in the models was determined through unit root analyses to address the spurious regression problem. Accurate results cannot be obtained when a unit root is present in a series of variables (Granger & Newbold, 1974 ). In panel data analysis, the primary consideration in stationarity tests is whether the countries are independent of each other or not. Unit root tests in panel data analysis comprise first- and second-generation tests, each with distinct characteristics. The first generation of unit root tests is further divided based on the homogeneity and heterogeneity assumptions of the countries. Some authors conducted tests under the homogeneity assumption (Breitung, 2005 ; Hadri, 2000 ; Levin et al., 2002 ), while some others pursued their analysis under the heterogeneity assumption (Choi, 2001 ; Im et al., 2003 ; Maddala & Wu, 1999 ).

Additionally, second-generation tests incorporate cross-section dependency into their analyses, whereas first-generation tests do not account for it. Given the dynamics of the global world, the use of second-generation tests in the literature is deemed more beneficial, as it is more realistic to assume that other countries will be affected by a shock experienced by one of the countries in the panel. Panel unit root tests have gained broader acceptance in time series analysis due to their ability to provide more meaningful results than standard stationarity tests. In recent years, there has been a preference for tests that allow for structural breaks, especially in series sensitive to economic variations such as foreign trade, exchange rates, and foreign capital. Hence, this study utilized panel unit root tests that consider structural breaks to assess the stationarity of variables susceptible to cyclical fluctuations, including democracy, inflation, per capita income, and FDI. Conducting stationarity tests without accounting for structural breaks can yield misleading results, making panel LM unit root tests with structural breaks the method of choice for this study.

The panel LM test (Im, Lee, & Tieslau, 2010 ) examines series in models with a level and trend, considering single and two breaks. In this study, analyses with a single break were preferred due to the shortness of the specified time interval and the events expected to cause breaks in the given period. The LM test statistics were employed to assess the hypothesis of “there is a unit root” (ϕ i  = 0). Compared to others, a distinctive feature of this test is its allowance for different breaking times for different countries. Moreover, it permits a structural break under both zero and alternative hypotheses, providing an additional advantage. The asymptotic distribution of the test follows the standard normal distribution, and it remains unaffected by the presence of a structural break. Table 6 presents the stationarity analysis results of the series for seven countries based on the model allowing breaks in level.

The analysis of Table  6  yields the following observations:

In unit root models allowing for a constant break, it is evident that all variables in the panel become stationary when their differences are calculated. In other words, since the series are stationary for the entire panel at the I(1) level, the necessary conditions for cointegration tests are met. The cointegration test indicates that global and local developments in countries cause structural breaks when considering these break dates.

On a country basis, the following conclusions can be drawn from Table  6 :

For the series whose differences are calculated, the FDI variable is stationary at the level value in Russia and India, while the same variable is stationary in India and Türkiye.

The per capita income variable is stationary at a level value only in Türkiye. However, the same variable is stationary in Brazil, India, and Türkiye for the series whose differences are computed.

The inflation variable is stationary at the level value in South Africa and Mexico. However, the same variable is stationary for the series whose differences are computed in Brazil, Russia, and China.

The democracy variable is stationary at the level value in Brazil, South Africa, and Türkiye. However, the variable is stationary in Brazil, Türkiye, and Mexico for the series whose differences are computed.

Table 7 shows the stationarity analysis results of seven countries based on the model that allows breaks in level and trend.

The results in Table  7 can be analyzed based on the following points:

General panel evaluation: Foreign direct investment (FDI) and per capita income variables are stationary at the level values when the panel is considered whole. Taking the difference of these variables increases the degree of stationarity. Inflation and democracy variables, among the other variables in the model, are stationary in the series when the difference is taken. However, they exhibit unit root characteristics at the level values. Overall, all series are stationary at the I(1) level with structural breaks for the entire panel. This suggests that the necessary conditions for the cointegration test are met. The dates of structural breaks indicate that social, political, and economic developments may have caused these breaks in the BRICS-TM countries included in the sample . These findings imply that significant events and changes in the socio-political and economic landscape of the BRICS-TM countries likely influence the structural breaks in the series.

Results from Table  7 can be interpreted on a country-specific basis as follows:

Brazil: FDI and per capita income are stationary at the level value. Inflation is stationary at the level, while democracy is stationary at the difference.

Russia: FDI and per capita income are stationary at the level value. Inflation is stationary at the level, while democracy is stationary at the difference.

India: FDI is stationary at the level value. Per capita income is stationary at the level, while inflation and democracy are stationary at the difference.

China: FDI is stationary at the difference. Per capita income is stationary at the level, while inflation and democracy are stationary at the difference.

South Africa: FDI is stationary at the level value. Per capita income is stationary at the level, while inflation and democracy are stationary at the difference.

Türkiye: FDI is stationary at the level value, per capita income is stationary at the level, and inflation and democracy are stationary at the difference.

Mexico: FDI is stationary at the difference. Per capita income is stationary at the level, while inflation and democracy are stationary at the difference.

These country-specific findings indicate variations in the stationarity characteristics of the variables, highlighting the importance of considering individual country dynamics in the analysis. The results of the panel unit root tests, both with and without structural breaks, provide insights into the stationarity of the variables. The interpretation suggests that a shock to one of the countries included in the model can lead to permanent effects that do not dissipate immediately. As confirmed by the tests, the non-stationarity of the series establishes the necessary condition for cointegration tests.

Moreover, when the same tests are conducted by taking the first-order differences of all series to achieve stationarity, it is observed that the variables become stationary at the I(1) level. This indicates that the variables are integrated in the first order, aligning with theoretical expectations. The I(1) characteristic implies that the variables exhibit a tendency to return to equilibrium after a shock, supporting the notion of long-run relationships among the variables.

Homogeneity Test of Cointegration Coefficients

The homogeneity of coefficients plays a crucial role in determining the relationship between variables in panel data studies. It helps organize subsequent tests used in the analysis. The homogeneity test examines whether the change in one country is affected at the same level by other countries. Coefficients are expected to be homogeneous in models for countries with similar economic structures, while they may be heterogeneous for countries with different economic structures. Pesaran and Yamagata ( 2008 ) developed the delta test based on Swamy ( 1970 ) to determine whether the slope parameters of cross-sections are homogeneous. The null hypothesis for this test is “slope coefficients are homogeneous.” Homogeneity, in the context of panel data analysis, implies that the coefficients of the slopes are the same for all units or entities within the panel. On the other hand, heterogeneity indicates that, at least in one of the entities, the slope coefficients differ from those in the rest of the panel. Testing for homogeneity helps assess whether the relationship between variables is consistent across all units or if there are significant variations.

As seen in Table  8 , the delta homogeneity test was performed to determine whether the slope coefficients of the model differ between units.

The delta test results indicate that the slope coefficients vary between units in the long term, given that the probability values for both test statistics are smaller than 0.05, as presented in Table  8 . This result suggests that the variables exhibit heterogeneity, implying that the relationships between variables are inconsistent across all units over the long term. The obtained result aligns with expectations and is consistent with the theory, indicating that the countries within the BRICS-TM sample exhibit different structures, and the coefficients are heterogeneous. This result suggests that the relationship between variables varies across these countries, emphasizing the sample group’s diverse economic characteristics and behaviors.

Panel Cointegration Test with Structural Break

Different methods are employed to determine the existence of long-term cointegration among the model’s variables. One set of methods is first-generation tests, which do not require cross-section dependence. The second set includes second-generation tests that consider cross-section dependence but do not incorporate structural breaks (Koç & Sarica, 2016 ). To obtain realistic and unbiased results, it is crucial to conduct tests that take structural breaks into account in cointegration analyses. Therefore, the panel cointegration test-PCWE (Westerlund & Edgerton, 2008 ) was employed, given that the series is stationary at the I(1) level.

PCWE was developed based on unit root tests that utilize Lagrange multiplier (LM) statistics, obtained from multiple repetitions (bootstrap). The merits of this test can be succinctly summarized as follows (Koç & Sarica, 2016 ; Göçer, 2013 ):

It takes into account cross-section dependency and structural breaks.

It accommodates heteroscedasticity and autocorrelation.

It identifies breaks at different dates for each country in terms of both constants and slopes.

Potential inherent problems in the model can be addressed with fully adjusted least squares estimators.

This test is effective in yielding reliable results even with small sample sizes.

This study opted for PCWE tests, given their robust characteristics. Additionally, considering the limited number of countries in the sample and the anticipation of few structural breaks in the specified period, the PCWE test was the preferred choice. As depicted in Table  9 , the determination of statistically significant cointegration between variables is made based on the significance levels of the probability values.

As indicated in Table  9 , cointegration is observed at a 5% significance level in the regime change model and a 1% significance level in the model without a break. The presence of cointegration suggests a long-term relationship between the variables of democracy and FDI in BRICS-TM. In simpler terms, democratic developments and FDI are correlated over the long run, indicating a balanced relationship between them. Future researchers may explore the direction of these variables across different samples. This study specifically tested the existence of a long-term relationship between FDI and democracy, and the inclusion of structural breaks was found to be significant. Governments and decision-makers, particularly in developing countries like BRICS-TM, should consider the relationship between democracy and FDI by taking structural breaks into account to attract foreign investment effectively. Therefore, it is emphasized that “any development related to democracy has the potential to influence FDI, and considering this factor is beneficial in the formulation and implementation of socio-economic policies.” No cointegration is observed in the “change at level” model. Indeed, the obtained results align with the study’s hypothesis. Considering the periods of structural breaks in the countries within the sample, it becomes evident that a long-term relationship exists between the variables incorporated into the model. This issue underscores the importance of considering not only the overall relationship between democracy and FDI but also the specific historical contexts and transitions in individual countries that might contribute to this relationship.

Regarding structural breaks in countries in the sample within the scope of cointegration in the regime change model, local and global developments, in general, cause breaks. The reasons for structural break dates in the sample countries are given in Table  10 .

The following items can be aligned with the breaking dates provided in Table  10 :

A recovery in macroeconomics and positive expectations toward agreements with the IMF became prominent after Russia’s transition economies in 1996.

2000 in Brazil is known as the period when the rapid growth trend started after passing the targeted inflation after the 1999 Russian Crisis.

Membership of China in the International Trade Union was evaluated as an essential development in the global economy in 2001.

Experiencing the biggest crisis in history in Türkiye in 2002 and starting a dominant single-party regime were remarkable developments.

The 2005 Election results in Mexico and the hurricane disasters, including an 8.7-magnitude earthquake, created significant socio-economic problems that year.

The ANC party’s coming to power alone in South Africa in 2009 was commented on as a consistent process for the national and regional economy; this situation also removed a series of uncertainties.

The devaluation experienced in India in 2016 has created a significant break.

Of course, the impact of such structural breaks should be considered. Toguç et al. ( 2023 ) argued that “differentiating these short-term and long-term effects has implications for risk management and policymaking.” Since structural break increases risks and uncertainty, foreign capital prefers to invest in other destinations.

Kónya’s Causality Test

This test (Kónya, 2006 ) investigates the existence of causality between variables using the seemingly unrelated regression (SUR) estimator (Zellner, 1962 ). One advantage of this test is that the causality test can be applied separately to the countries that make up the heterogeneous panel. Another important advantage is that it is unnecessary to apply unit root and cointegration tests, as country-specific critical values are produced. According to the test results, if the Wald statistics calculated for each country are greater than the critical values at the chosen significance level, the null hypothesis of “no causality between the variables” is rejected. In other words, a Wald statistic greater than the critical value indicates that there is causality between the variables.

The Kónya causality test results provided in Table  11 revealed a causality from democracy (DEMOC) to FDI at a 1% significance level in Mexico, 5% in China, and 10% in Russia. In addition, from FDI to democracy (DEMOC), there is causality at a 5% significance level in Mexico and a 10% significance level in Russia.

According to the results in Table  12 for the causality between foreign direct investment (FDI) and PGDP, the Kónya causality tests revealed a one-way causality from PGDP to FDI at a 10% significance level in Mexico.

According to the results provided in Table  13 for the causality between foreign direct investment (FDI) and inflation (INF), the results of the Kónya causality tests revealed a one-way causality from inflation to FDI at a 10% significance level in Türkiye and, conversely, a one-way causality from FDI to inflation at a 10% significance level in South Africa.

The study investigated the nexus between democracy and foreign direct investment (FDI) using annual data from a sample of seven countries within emerging markets from 1994–2019. According to cross-section dependence test results, all variables’ probability values were less than 0.01, indicating significant cross-section dependence. The rejection of the null hypothesis, stating “there is no dependence between sections” in favor of the alternative hypothesis suggesting “there is cross-section dependence between sections,” aligns with the contemporary global landscape. In today’s interconnected world, any impactful event or development in one of the BRICS-TM countries has reverberations across others, particularly in areas related to FDI and democracy. These findings underscore the imperative for governments and policymakers in these countries to craft future strategies with a keen awareness of this interconnectedness and the potential spillover effects on FDI and democracy.

Furthermore, the outcomes of the panel unit root test indicate that all variables in the panel become stationary at the I(1) level when their differences are calculated, meeting the necessary conditions for cointegration tests. This result suggests that global and local developments in countries cause structural breaks when considering these break dates. Variations in stationarity characteristics of variables were observed on a country basis, highlighting the importance of considering individual country dynamics in the analysis.

The delta homogeneity test results suggest that the variables exhibit heterogeneity, implying that the relationships between variables are inconsistent across all units over the long term. This aligns with expectations and emphasizes the diverse economic characteristics and behaviors within the sample group of BRICS-TM countries.

The Westerlund-Edgerton cointegration test results reveal significant cointegration between variables, observed at a 1% significance level in the model without a break and a 5% level in the regime change model. This result signifies a sustained relationship between FDI and democracy in BRICS-TM countries over the long term. Future researchers may explore the direction of these variables across different samples, while governments and decision-makers should consider this relationship, particularly in developing countries, to attract foreign investment effectively.

Kónya’s causality test results also provided significant causality between some of the variables in some countries within the sample group. Firstly, there is a causality from democracy (DEMOC) to FDI in Mexico (1% significance level), in China (5% significance level), and in Russia (10% significance level). Secondly, there is also a significant causality from FDI to democracy (DEMOC) in Mexico (5% significance level) and in Russia (10% significance level). Thirdly, a one-way causality could only be found from PGDP to FDI in Mexico (10% significance level). Fourthly, there is also a one-way causality from inflation to FDI in Türkiye (10% significance level) and a one-way causality from FDI to inflation in South Africa (10% significance level). Thus, Kónya’s causality test results supported the hypothesis of the research with significant results.

In conclusion, the empirical findings establish a statistically significant and robust relationship between the level of democracy and the flow of FDI in BRICS-TM countries. These findings underscore the intertwined nature of political and economic dynamics within these nations and highlight the importance of considering both aspects in policy formulation and decision-making processes.

The relationship between the democracy level and foreign direct investment (FDI) of BRICS-TM countries is an area that requires further exploration. Subsequently, comparing the findings of this study with those of previous research reveals its significance. While earlier studies predominantly concentrated on the preferences of host countries in attracting foreign investment, few delved into the factors influencing foreign investors’ choices. A notable exception is by Li and Resnick ( 2003 ), who highlighted the pivotal question of “Why do companies invest in foreign countries?” and proposed a theory positing that “democratic institutions impact FDI flow in both positive and negative ways” (Li & Resnick, 2003 :176). Their conclusions from data analysis of 53 developing countries spanning 1982–1995 align with the current study’s outcomes. Specifically, they found that (1) advancements in democracy lead to heightened property rights protection, fostering increased FDI inflows, and, (2) conversely, democratic improvements in underdeveloped nations result in diminished FDI flows. These findings correspond with our study, given that the sampled countries are a mix of developing and developed nations, mirroring the first scenario described by Li and Resnick.

Derbali et al. ( 2015 ) concluded in a similar vein in their study, examining a massive dataset spanning from 1980 to 2010 with 173 countries, 44 of which underwent democratic transformation. Their observation that “variables related to human development and individual freedom initiate the democratic transformation process, contrary to the social heterogeneity variable” aligns with the results of the present study when interpreted in reverse. This scenario prompts a chicken-and-egg question: Does the level of democracy positively influence the flow of FDI, or does FDI flow positively impact the level of democracy? The authors tackled this issue in the second stage of their analysis and determined that democratic transformation leads to a substantial increase in FDI inflows. Our findings corroborate this perspective with evidence from a different sample group of countries.

Malikane and Chitambara ( 2017 ) concluded in their study analyzing the relationship between FDI, democracy, and economic growth in eight South African countries from 1980 to 2014 that the FDI variable exhibits a direct and positive impact on economic development, explicitly implicating that strong democratic institutions serve as notable drivers of economic growth. Their findings suggest that the effect of FDI on economic growth is contingent on the level of democracy in the host country. In another study on developing countries, Khan et al. ( 2023 ) found that specific determinants of good governance, such as control of corruption, political stability, and voice and accountability, significantly attract FDI inflows. However, other determinants, including government effectiveness, regulatory quality, political system, and institutional quality, significantly reduce FDI inflows. On the contrary, they found that in Asian countries, all institutional quality indicators except control of corruption have a significant and positive effect on FDI inflows (Khan et al., 2023 ). The significant relationships identified between these phenomena across various indicators for developing and Asian countries align with the findings of our study.

Developed and developing nations actively engage in concerted efforts to attract foreign capital investments in the contemporary global economic landscape. Foreign direct investments (FDIs) stand out as a pivotal form of investment that significantly influences a country’s growth and development trajectory. The inflow of direct foreign capital brings multifaceted contributions to a nation’s economy, encompassing vital aspects such as capital infusion, technological advancement, elevated management standards, expanded foreign trade opportunities, employment generation, sectoral discipline, access to skilled labor, and risk mitigation.

In addition to all these, foreign direct investment (FDI) holds significant importance not only in the general context of sustainability but also specifically in sustainable development. To better understand this close relationship between sustainable development and FDI, first briefly examine the concept of sustainability. Simply put, sustainability entails maintaining a favorable condition through methods that cause no harm yet are supportable, legally and scientifically verifiable, defendable, and implementable (Ratiu, 2013 ). From a developmental perspective, it signifies maintaining continuity without losing control. According to Menger ( 2010 ), sustainability can be defined as the ability to grow and survive independently. The author emphasizes that the concept of sustainability is closely related to “creativity” and “cultural vitality,” as well as being an “internally growing” and “self-sustaining” trend with innovative effects that also attract different social strata.

Within the context of all these existing barriers and dilemmas, managing the process of reducing the negative aspects while increasing and offering the positives to people must be handled with care. This intricate process, termed sustainable development, is like the search for the cosmos in chaos as it aims to balance the economic, environmental, and social dimensions of both local urban areas and regional and national areas, and even the global sphere, especially with climate change becoming one of the main negative impacts on the environmental dimension. Gazibey et al. ( 2014 ) also noted that, while some problem areas, such as “poverty reduction” are mainly related to the economic and somewhat to the social dimensions of sustainability, other issues like “climate change” and “reduction of carbon footprint” are more related to the environmental dimension. An in-depth examination reveals that many problems, which may initially seem related to a single dimension, are intertwined with multiple dimensions. Thus, while attracting foreign direct investment to a country may seem primarily related to the economic dimension at first glance, it is closely linked to environmental and social dimensions.

In its most straightforward approach, meeting and satisfying the basic needs of individuals will subsequently prioritize higher-level needs. This, in turn, will support sustainable development in all three dimensions. Thus, while foreign capital invested in a country may initially support economic sustainability, its contribution to the socio-economic levels of individuals will lay the groundwork primarily for social and educational improvement in the medium and long term, secondarily for environmental enhancement to result in a more livable environment. For example, Xu et al. ( 2024 ) argued that “China is currently exploring a sustainable development mode of collaborative governance.” In a good level of governance, all social partners expected to be affected by the possible policies are included in the decision-making process. This process is related to and supports the participation dimension of democracy. So, as the pieces of a chain, a good level of democracy supports the level of governance, and governance supports the accumulation of FDI and economic performance. Consequently, these favorable conditions might pave the way for sustainable development. Another study (Olorogun, 2023 ) found a long-run relationship between financial development in the private sector and economic growth in sub-Saharan Africa, with the data spanning from 1978 to 2019. According to the results of the author’s research, there is a long-run covariance between sustainable economic development and foreign direct investment (FDI) and a significant level of causality between economic growth and financial development in the private sector, FDI, and export.

Indeed, sustainability resembles a ball resting on a three-legged stool: Any absence or imbalance in one of this tripod’s economic, social, or environmental legs will cause the ball to fall. In other words, sustainable development requires addressing all three dimensions in a balanced manner.

This idea brings us to the focus of this research: The level of democracy and the FDI variable and the relationship between these variables essentially concerns all three dimensions. In countries with a higher level of democracy, the possibility of developing policies that consider citizens’ demands and preferences is higher than in countries with lower levels of democracy. Conversely, in countries with lower levels of democracy , the likelihood of prioritizing the preferences and gains of specific individuals or groups over issues such as sustainability, environmental protection, and social welfare is higher. Consequently, this situation will negatively affect both the potential level of FDI attracted to the less developed country and, ultimately, the sustainable development momentum.

To sum up, numerous factors play a crucial role in shaping decisions related to foreign direct investments. Particularly in underdeveloped and developing countries, where domestic capital accumulation might be insufficient, the preference for attracting direct foreign capital investments emerges as a strategic choice over external borrowing. This strategic approach is driven by fostering economic development and sustainable growth while leveraging the benefits associated with foreign capital inflows.

The empirical evidence on the relationship between democracy and the level of foreign direct investment (FDI) often presents conflicting results, influenced by variations in study periods and sample compositions. Notably, these disparities can be traced back to the differing development levels of countries under scrutiny.

Reviewing previous studies reveals a recurring pattern wherein developed countries exhibit a positive and significant correlation between democracy and FDI. Conversely, in underdeveloped or developing nations, a negative relationship tends to prevail between these two variables. This disparity hinges on the distinct behavior of capital owners seeking to invest in already developed countries, where business transactions are grounded in established legal frameworks, property rights, and the rule of law. In contrast, underdeveloped and developing countries often witness capital owners engaging in potentially illicit and unethical business dealings with high risks and potential returns.

These arrangements are frequently based on different interests and assurances with individuals and groups in positions of power. In essence, the ease of resource acquisition, processing, and exportation in underdeveloped countries becomes contingent upon the presence of authoritarian regimes. Such relationships of interest with authoritarian regimes provide investment security for global investors. However, these regimes—keen on preserving these relationships—are disinclined to have their dealings exposed, which in turn leads to increased pressure on their citizens. The resulting mutualistic relationship transforms into a lucrative exploitation process.

When the outcomes of the panel data analysis incorporating structural breaks were examined, it was found that all variables demonstrated significance at the 1% level. The cross-sectional dependency analysis results indicated a significant cross-sectional relationship between the variables. In the panel unit root test, it was observed that the variables in the model exhibited unit roots at the level, but their differences rendered all variables stationary. The delta homogeneity test findings suggested that the variables lacked homogeneity. Furthermore, the results of the panel cointegration test with structural breaks affirmed a long-term relationship, with significance levels of 1% in the model without breaks and 5% in the regime change model. Lastly, the reached bidirectional and one-directional causality between FDI and democracy and other economic variables like inflation and PGDP in the sample group countries require policymakers to focus on each variable carefully especially on the level of democracy if they aim to reach a high level of FDI.

In conclusion, the findings of this study suggest the presence of a long-term relationship between democracy and FDI also supported by causality in some countries within the sample, as revealed through the analysis of data from BRICS-TM countries within emerging markets spanning the period 1994–2018. The significance of this relationship is particularly evident when considering the impact of structural breaks. It is emphasized that governments and policymakers in emerging markets (including those in BRICS-TM), which aim to bolster their economy’s resilience against various shocks, should not only consider structural breaks but also recognize the intricate connection between democracy and FDI. The study underscores that developments in democracy have the potential to influence FDI, emphasizing the importance of factoring this relationship into the formulation and execution of socio-economic policies. Lastly, using panel tests with a structural break, a method uncommonly employed in the empirical analysis of the democracy variable, may contribute as an additional dimension to the existing literature in this field.

In analyzing the relationship between democracy and foreign direct investment, the findings suggest a long-term relationship in all models except for the level change model. These results highlight the significance of democratic developments in the BRICS-TM countries influencing the inflow of foreign direct capital. Therefore, policymakers in emerging markets, particularly within BRICS-TM countries, are encouraged to prioritize democracy and foster democratic developments to attract foreign direct investments. Additionally, given the impact of global and local developments leading to structural breaks, it becomes crucial for these policymakers to closely monitor and interpret international and global events that may affect the resilience of their national economies, both negatively and positively. By doing so, emerging markets can enhance their resilience against various shocks, enabling policymakers to adeptly prepare their economies, private sectors, and stock markets for potential global risks.

Opting for direct foreign capital investments over external debt or short-term investments is a more rational approach for developing countries to accumulate capital for their overall development. As many countries seek to address the scarcity of capital, the understanding of the contributions of foreign capital to development improves, while global competition intensifies to attract foreign capital. Therefore, policymakers should focus on enhancing macroeconomic indicators such as inflation and national income and fostering democratic development, a fundamental trust factor for foreign capital. Demographic and institutional factors also affect the global or social fiscal pressure (Nuță & Nuță, 2020 ). Thus, as an institutional factor, positive developments at the level of democracy are fundamental in attracting foreign capital.

It is crucial for developing countries to prioritize and keep pace with indicators that foreign capital considers significant. Global companies prioritize countries they can trust, where investments can swiftly yield profits due to potential risks. The foundation of democracy in developing nations starts in the family and education realms. Proper education on the importance and necessity of democracy in the curriculum contributes to long-term awareness of democracy. Developing effective education policies within families can address intra-family democracy, fostering a culture of democracy throughout the country.

The reasons listed up to this point reiterate that attracting foreign direct investments to a country is of utmost critical importance for supporting sustainable development in all aspects of the nation. As discussed in the discussion section, while sustainability may appear to be solely related to the economic dimension at first glance, an increase in foreign direct investment toward a country has the potential to indirectly and positively impact the social and environmental dimensions of sustainability as well. When considering that the level of democracy also has a similar effect on the level of FDI, it should be expected that the level of democracy in a country is strongly correlated with the issue of sustainable development.

In conclusion, new researchers interested in this subject are recommended to conduct analyses on different country groups. Updating established models and testing hypotheses using various socio-economic indicators and analysis methods can further contribute to the literature.

Data Availability

The data set is uploaded to the system as a supplementary file and also uploaded to Figshare with the https://doi.org/10.6084/m9.figshare.21701966 .

Turkey’s name changed to Türkiye: According to the United Nations (UN)-Türkiye, the country’s name has been officially changed to Türkiye at the UN upon a letter received on June 1 from the Turkish Foreign Ministry (UN-Türkiye. (2022)). Turkey’s name changed to Türkiye, URL: https://turkiye.un.org/en/184798-turkeys-name-changed-turkiye , Accessed on: 02.07.2022.

Abbreviations

Brazil, Russia, India, China, South Africa, Türkiye, Mexico

The Democracy Index variable

Ecological footprint

• Foreign direct investment

Gross domestic product

Logarithm of foreign direct investment

Logarithm of per capita income

Multinational corporations

Per capita income

Political institutions

Regression coefficient value

World Development Indicators

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Practice Points/Highlights

1. From 1994 to 2018, there was significant cointegration between democracy and foreign direct investment (FDI) in BRICS-TM countries among the emerging markets.

2. Democratic developments and FDI move together in the long run and have a balanced relationship between them in Emerging Market Economies.

3. Policymakers in BRICS-TM countries need to develop democracy awareness and ensure democratic developments to attract foreign direct investment to secure a resilient economy in these emerging economies

4. Governments and decision-makers in emerging economies, such as BRICS-TM, who want to attract FDI need to consider the structural breaks and the relationship between democracy and FDI .

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Cutcu, I., Keser, A. Democracy and Foreign Direct Investment in BRICS-TM Countries for Sustainable Development. J Knowl Econ (2024). https://doi.org/10.1007/s13132-024-02205-3

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