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Research Findings – Types Examples and Writing Guide
Table of Contents
Research findings refer to the results obtained from a study or investigation conducted through a systematic and scientific approach. These findings are the outcomes of the data analysis, interpretation, and evaluation carried out during the research process.
Types of Research Findings
There are two main types of research findings:
Qualitative research is an exploratory research method used to understand the complexities of human behavior and experiences. Qualitative findings are non-numerical and descriptive data that describe the meaning and interpretation of the data collected. Examples of qualitative findings include quotes from participants, themes that emerge from the data, and descriptions of experiences and phenomena.
Quantitative research is a research method that uses numerical data and statistical analysis to measure and quantify a phenomenon or behavior. Quantitative findings include numerical data such as mean, median, and mode, as well as statistical analyses such as t-tests, ANOVA, and regression analysis. These findings are often presented in tables, graphs, or charts.
Both qualitative and quantitative findings are important in research and can provide different insights into a research question or problem. Combining both types of findings can provide a more comprehensive understanding of a phenomenon and improve the validity and reliability of research results.
Parts of Research Findings
Research findings typically consist of several parts, including:
- Introduction: This section provides an overview of the research topic and the purpose of the study.
- Literature Review: This section summarizes previous research studies and findings that are relevant to the current study.
- Methodology : This section describes the research design, methods, and procedures used in the study, including details on the sample, data collection, and data analysis.
- Results : This section presents the findings of the study, including statistical analyses and data visualizations.
- Discussion : This section interprets the results and explains what they mean in relation to the research question(s) and hypotheses. It may also compare and contrast the current findings with previous research studies and explore any implications or limitations of the study.
- Conclusion : This section provides a summary of the key findings and the main conclusions of the study.
- Recommendations: This section suggests areas for further research and potential applications or implications of the study’s findings.
How to Write Research Findings
Writing research findings requires careful planning and attention to detail. Here are some general steps to follow when writing research findings:
- Organize your findings: Before you begin writing, it’s essential to organize your findings logically. Consider creating an outline or a flowchart that outlines the main points you want to make and how they relate to one another.
- Use clear and concise language : When presenting your findings, be sure to use clear and concise language that is easy to understand. Avoid using jargon or technical terms unless they are necessary to convey your meaning.
- Use visual aids : Visual aids such as tables, charts, and graphs can be helpful in presenting your findings. Be sure to label and title your visual aids clearly, and make sure they are easy to read.
- Use headings and subheadings: Using headings and subheadings can help organize your findings and make them easier to read. Make sure your headings and subheadings are clear and descriptive.
- Interpret your findings : When presenting your findings, it’s important to provide some interpretation of what the results mean. This can include discussing how your findings relate to the existing literature, identifying any limitations of your study, and suggesting areas for future research.
- Be precise and accurate : When presenting your findings, be sure to use precise and accurate language. Avoid making generalizations or overstatements and be careful not to misrepresent your data.
- Edit and revise: Once you have written your research findings, be sure to edit and revise them carefully. Check for grammar and spelling errors, make sure your formatting is consistent, and ensure that your writing is clear and concise.
Research Findings Example
Following is a Research Findings Example sample for students:
Title: The Effects of Exercise on Mental Health
Sample : 500 participants, both men and women, between the ages of 18-45.
Methodology : Participants were divided into two groups. The first group engaged in 30 minutes of moderate intensity exercise five times a week for eight weeks. The second group did not exercise during the study period. Participants in both groups completed a questionnaire that assessed their mental health before and after the study period.
Findings : The group that engaged in regular exercise reported a significant improvement in mental health compared to the control group. Specifically, they reported lower levels of anxiety and depression, improved mood, and increased self-esteem.
Conclusion : Regular exercise can have a positive impact on mental health and may be an effective intervention for individuals experiencing symptoms of anxiety or depression.
Applications of Research Findings
Research findings can be applied in various fields to improve processes, products, services, and outcomes. Here are some examples:
- Healthcare : Research findings in medicine and healthcare can be applied to improve patient outcomes, reduce morbidity and mortality rates, and develop new treatments for various diseases.
- Education : Research findings in education can be used to develop effective teaching methods, improve learning outcomes, and design new educational programs.
- Technology : Research findings in technology can be applied to develop new products, improve existing products, and enhance user experiences.
- Business : Research findings in business can be applied to develop new strategies, improve operations, and increase profitability.
- Public Policy: Research findings can be used to inform public policy decisions on issues such as environmental protection, social welfare, and economic development.
- Social Sciences: Research findings in social sciences can be used to improve understanding of human behavior and social phenomena, inform public policy decisions, and develop interventions to address social issues.
- Agriculture: Research findings in agriculture can be applied to improve crop yields, develop new farming techniques, and enhance food security.
- Sports : Research findings in sports can be applied to improve athlete performance, reduce injuries, and develop new training programs.
When to use Research Findings
Research findings can be used in a variety of situations, depending on the context and the purpose. Here are some examples of when research findings may be useful:
- Decision-making : Research findings can be used to inform decisions in various fields, such as business, education, healthcare, and public policy. For example, a business may use market research findings to make decisions about new product development or marketing strategies.
- Problem-solving : Research findings can be used to solve problems or challenges in various fields, such as healthcare, engineering, and social sciences. For example, medical researchers may use findings from clinical trials to develop new treatments for diseases.
- Policy development : Research findings can be used to inform the development of policies in various fields, such as environmental protection, social welfare, and economic development. For example, policymakers may use research findings to develop policies aimed at reducing greenhouse gas emissions.
- Program evaluation: Research findings can be used to evaluate the effectiveness of programs or interventions in various fields, such as education, healthcare, and social services. For example, educational researchers may use findings from evaluations of educational programs to improve teaching and learning outcomes.
- Innovation: Research findings can be used to inspire or guide innovation in various fields, such as technology and engineering. For example, engineers may use research findings on materials science to develop new and innovative products.
Purpose of Research Findings
The purpose of research findings is to contribute to the knowledge and understanding of a particular topic or issue. Research findings are the result of a systematic and rigorous investigation of a research question or hypothesis, using appropriate research methods and techniques.
The main purposes of research findings are:
- To generate new knowledge : Research findings contribute to the body of knowledge on a particular topic, by adding new information, insights, and understanding to the existing knowledge base.
- To test hypotheses or theories : Research findings can be used to test hypotheses or theories that have been proposed in a particular field or discipline. This helps to determine the validity and reliability of the hypotheses or theories, and to refine or develop new ones.
- To inform practice: Research findings can be used to inform practice in various fields, such as healthcare, education, and business. By identifying best practices and evidence-based interventions, research findings can help practitioners to make informed decisions and improve outcomes.
- To identify gaps in knowledge: Research findings can help to identify gaps in knowledge and understanding of a particular topic, which can then be addressed by further research.
- To contribute to policy development: Research findings can be used to inform policy development in various fields, such as environmental protection, social welfare, and economic development. By providing evidence-based recommendations, research findings can help policymakers to develop effective policies that address societal challenges.
Characteristics of Research Findings
Research findings have several key characteristics that distinguish them from other types of information or knowledge. Here are some of the main characteristics of research findings:
- Objective : Research findings are based on a systematic and rigorous investigation of a research question or hypothesis, using appropriate research methods and techniques. As such, they are generally considered to be more objective and reliable than other types of information.
- Empirical : Research findings are based on empirical evidence, which means that they are derived from observations or measurements of the real world. This gives them a high degree of credibility and validity.
- Generalizable : Research findings are often intended to be generalizable to a larger population or context beyond the specific study. This means that the findings can be applied to other situations or populations with similar characteristics.
- Transparent : Research findings are typically reported in a transparent manner, with a clear description of the research methods and data analysis techniques used. This allows others to assess the credibility and reliability of the findings.
- Peer-reviewed: Research findings are often subject to a rigorous peer-review process, in which experts in the field review the research methods, data analysis, and conclusions of the study. This helps to ensure the validity and reliability of the findings.
- Reproducible : Research findings are often designed to be reproducible, meaning that other researchers can replicate the study using the same methods and obtain similar results. This helps to ensure the validity and reliability of the findings.
Advantages of Research Findings
Research findings have many advantages, which make them valuable sources of knowledge and information. Here are some of the main advantages of research findings:
- Evidence-based: Research findings are based on empirical evidence, which means that they are grounded in data and observations from the real world. This makes them a reliable and credible source of information.
- Inform decision-making: Research findings can be used to inform decision-making in various fields, such as healthcare, education, and business. By identifying best practices and evidence-based interventions, research findings can help practitioners and policymakers to make informed decisions and improve outcomes.
- Identify gaps in knowledge: Research findings can help to identify gaps in knowledge and understanding of a particular topic, which can then be addressed by further research. This contributes to the ongoing development of knowledge in various fields.
- Improve outcomes : Research findings can be used to develop and implement evidence-based practices and interventions, which have been shown to improve outcomes in various fields, such as healthcare, education, and social services.
- Foster innovation: Research findings can inspire or guide innovation in various fields, such as technology and engineering. By providing new information and understanding of a particular topic, research findings can stimulate new ideas and approaches to problem-solving.
- Enhance credibility: Research findings are generally considered to be more credible and reliable than other types of information, as they are based on rigorous research methods and are subject to peer-review processes.
Limitations of Research Findings
While research findings have many advantages, they also have some limitations. Here are some of the main limitations of research findings:
- Limited scope: Research findings are typically based on a particular study or set of studies, which may have a limited scope or focus. This means that they may not be applicable to other contexts or populations.
- Potential for bias : Research findings can be influenced by various sources of bias, such as researcher bias, selection bias, or measurement bias. This can affect the validity and reliability of the findings.
- Ethical considerations: Research findings can raise ethical considerations, particularly in studies involving human subjects. Researchers must ensure that their studies are conducted in an ethical and responsible manner, with appropriate measures to protect the welfare and privacy of participants.
- Time and resource constraints : Research studies can be time-consuming and require significant resources, which can limit the number and scope of studies that are conducted. This can lead to gaps in knowledge or a lack of research on certain topics.
- Complexity: Some research findings can be complex and difficult to interpret, particularly in fields such as science or medicine. This can make it challenging for practitioners and policymakers to apply the findings to their work.
- Lack of generalizability : While research findings are intended to be generalizable to larger populations or contexts, there may be factors that limit their generalizability. For example, cultural or environmental factors may influence how a particular intervention or treatment works in different populations or contexts.
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Dissertations 5: findings, analysis and discussion: home.
The time has come to show and discuss the findings of your research. How to structure this part of your dissertation?
Dissertations can have different structures, as you can see in the dissertation structure guide.
Dissertations organised by sections
Many dissertations are organised by sections. In this case, we suggest three options. Note that, if within your course you have been instructed to use a specific structure, you should do that. Also note that sometimes there is considerable freedom on the structure, so you can come up with other structures too.
A) More common for scientific dissertations and quantitative methods:
- Results chapter
- Discussion chapter
- Literature review
if you write a scientific dissertation, or anyway using quantitative methods, you will have some objective results that you will present in the Results chapter. You will then interpret the results in the Discussion chapter.
B) More common for qualitative methods
- Analysis chapter. This can have more descriptive/thematic subheadings.
- Discussion chapter. This can have more descriptive/thematic subheadings.
- Case study of Company X (fashion brand) environmental strategies
- Successful elements
- Lessons learnt
- Criticisms of Company X environmental strategies
- Possible alternatives
C) More common for qualitative methods
- Analysis and discussion chapter. This can have more descriptive/thematic titles.
- Case study of Company X (fashion brand) environmental strategies
If your dissertation uses qualitative methods, it is harder to identify and report objective data. Instead, it may be more productive and meaningful to present the findings in the same sections where you also analyse, and possibly discuss, them. You will probably have different sections dealing with different themes. The different themes can be subheadings of the Analysis and Discussion (together or separate) chapter(s).
If the structure of your dissertation is thematic , you will have several chapters analysing and discussing the issues raised by your research. The chapters will have descriptive/thematic titles.
- Background on the conflict in Yemen (2004-present day)
- Classification of the conflict in international law
- International law violations
- Options for enforcement of international law
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- Last Updated: Aug 4, 2023 2:17 PM
- URL: https://libguides.westminster.ac.uk/c.php?g=696975
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How To Write The Results/Findings Chapter
For quantitative studies (dissertations & theses).
By: Derek Jansen (MBA). Expert Reviewed By: Kerryn Warren (PhD) | July 2021
So, you’ve completed your quantitative data analysis and it’s time to report on your findings. But where do you start? In this post, we’ll walk you through the results chapter (also called the findings or analysis chapter), step by step, so that you can craft this section of your dissertation or thesis with confidence. If you’re looking for information regarding the results chapter for qualitative studies, you can find that here .
Overview: Quantitative Results Chapter
- What exactly the results/findings/analysis chapter is
- What you need to include in your results chapter
- How to structure your results chapter
- A few tips and tricks for writing top-notch chapter
What exactly is the results chapter?
The results chapter (also referred to as the findings or analysis chapter) is one of the most important chapters of your dissertation or thesis because it shows the reader what you’ve found in terms of the quantitative data you’ve collected. It presents the data using a clear text narrative, supported by tables, graphs and charts. In doing so, it also highlights any potential issues (such as outliers or unusual findings) you’ve come across.
But how’s that different from the discussion chapter?
Well, in the results chapter, you only present your statistical findings. Only the numbers, so to speak – no more, no less. Contrasted to this, in the discussion chapter , you interpret your findings and link them to prior research (i.e. your literature review), as well as your research objectives and research questions . In other words, the results chapter presents and describes the data, while the discussion chapter interprets the data.
Let’s look at an example.
In your results chapter, you may have a plot that shows how respondents to a survey responded: the numbers of respondents per category, for instance. You may also state whether this supports a hypothesis by using a p-value from a statistical test. But it is only in the discussion chapter where you will say why this is relevant or how it compares with the literature or the broader picture. So, in your results chapter, make sure that you don’t present anything other than the hard facts – this is not the place for subjectivity.
It’s worth mentioning that some universities prefer you to combine the results and discussion chapters. Even so, it is good practice to separate the results and discussion elements within the chapter, as this ensures your findings are fully described. Typically, though, the results and discussion chapters are split up in quantitative studies. If you’re unsure, chat with your research supervisor or chair to find out what their preference is.
What should you include in the results chapter?
Following your analysis, it’s likely you’ll have far more data than are necessary to include in your chapter. In all likelihood, you’ll have a mountain of SPSS or R output data, and it’s your job to decide what’s most relevant. You’ll need to cut through the noise and focus on the data that matters.
This doesn’t mean that those analyses were a waste of time – on the contrary, those analyses ensure that you have a good understanding of your dataset and how to interpret it. However, that doesn’t mean your reader or examiner needs to see the 165 histograms you created! Relevance is key.
How do I decide what’s relevant?
At this point, it can be difficult to strike a balance between what is and isn’t important. But the most important thing is to ensure your results reflect and align with the purpose of your study . So, you need to revisit your research aims, objectives and research questions and use these as a litmus test for relevance. Make sure that you refer back to these constantly when writing up your chapter so that you stay on track.
As a general guide, your results chapter will typically include the following:
- Some demographic data about your sample
- Reliability tests (if you used measurement scales)
- Descriptive statistics
- Inferential statistics (if your research objectives and questions require these)
- Hypothesis tests (again, if your research objectives and questions require these)
We’ll discuss each of these points in more detail in the next section.
Importantly, your results chapter needs to lay the foundation for your discussion chapter . This means that, in your results chapter, you need to include all the data that you will use as the basis for your interpretation in the discussion chapter.
For example, if you plan to highlight the strong relationship between Variable X and Variable Y in your discussion chapter, you need to present the respective analysis in your results chapter – perhaps a correlation or regression analysis.
Need a helping hand?
How do I write the results chapter?
There are multiple steps involved in writing up the results chapter for your quantitative research. The exact number of steps applicable to you will vary from study to study and will depend on the nature of the research aims, objectives and research questions . However, we’ll outline the generic steps below.
Step 1 – Revisit your research questions
The first step in writing your results chapter is to revisit your research objectives and research questions . These will be (or at least, should be!) the driving force behind your results and discussion chapters, so you need to review them and then ask yourself which statistical analyses and tests (from your mountain of data) would specifically help you address these . For each research objective and research question, list the specific piece (or pieces) of analysis that address it.
At this stage, it’s also useful to think about the key points that you want to raise in your discussion chapter and note these down so that you have a clear reminder of which data points and analyses you want to highlight in the results chapter. Again, list your points and then list the specific piece of analysis that addresses each point.
Next, you should draw up a rough outline of how you plan to structure your chapter . Which analyses and statistical tests will you present and in what order? We’ll discuss the “standard structure” in more detail later, but it’s worth mentioning now that it’s always useful to draw up a rough outline before you start writing (this advice applies to any chapter).
Step 2 – Craft an overview introduction
As with all chapters in your dissertation or thesis, you should start your quantitative results chapter by providing a brief overview of what you’ll do in the chapter and why . For example, you’d explain that you will start by presenting demographic data to understand the representativeness of the sample, before moving onto X, Y and Z.
This section shouldn’t be lengthy – a paragraph or two maximum. Also, it’s a good idea to weave the research questions into this section so that there’s a golden thread that runs through the document.
Step 3 – Present the sample demographic data
The first set of data that you’ll present is an overview of the sample demographics – in other words, the demographics of your respondents.
- What age range are they?
- How is gender distributed?
- How is ethnicity distributed?
- What areas do the participants live in?
The purpose of this is to assess how representative the sample is of the broader population. This is important for the sake of the generalisability of the results. If your sample is not representative of the population, you will not be able to generalise your findings. This is not necessarily the end of the world, but it is a limitation you’ll need to acknowledge.
Of course, to make this representativeness assessment, you’ll need to have a clear view of the demographics of the population. So, make sure that you design your survey to capture the correct demographic information that you will compare your sample to.
But what if I’m not interested in generalisability?
Well, even if your purpose is not necessarily to extrapolate your findings to the broader population, understanding your sample will allow you to interpret your findings appropriately, considering who responded. In other words, it will help you contextualise your findings . For example, if 80% of your sample was aged over 65, this may be a significant contextual factor to consider when interpreting the data. Therefore, it’s important to understand and present the demographic data.
Step 4 – Review composite measures and the data “shape”.
Before you undertake any statistical analysis, you’ll need to do some checks to ensure that your data are suitable for the analysis methods and techniques you plan to use. If you try to analyse data that doesn’t meet the assumptions of a specific statistical technique, your results will be largely meaningless. Therefore, you may need to show that the methods and techniques you’ll use are “allowed”.
Most commonly, there are two areas you need to pay attention to:
#1: Composite measures
The first is when you have multiple scale-based measures that combine to capture one construct – this is called a composite measure . For example, you may have four Likert scale-based measures that (should) all measure the same thing, but in different ways. In other words, in a survey, these four scales should all receive similar ratings. This is called “ internal consistency ”.
Internal consistency is not guaranteed though (especially if you developed the measures yourself), so you need to assess the reliability of each composite measure using a test. Typically, Cronbach’s Alpha is a common test used to assess internal consistency – i.e., to show that the items you’re combining are more or less saying the same thing. A high alpha score means that your measure is internally consistent. A low alpha score means you may need to consider scrapping one or more of the measures.
#2: Data shape
The second matter that you should address early on in your results chapter is data shape. In other words, you need to assess whether the data in your set are symmetrical (i.e. normally distributed) or not, as this will directly impact what type of analyses you can use. For many common inferential tests such as T-tests or ANOVAs (we’ll discuss these a bit later), your data needs to be normally distributed. If it’s not, you’ll need to adjust your strategy and use alternative tests.
To assess the shape of the data, you’ll usually assess a variety of descriptive statistics (such as the mean, median and skewness), which is what we’ll look at next.
Step 5 – Present the descriptive statistics
Now that you’ve laid the foundation by discussing the representativeness of your sample, as well as the reliability of your measures and the shape of your data, you can get started with the actual statistical analysis. The first step is to present the descriptive statistics for your variables.
For scaled data, this usually includes statistics such as:
- The mean – this is simply the mathematical average of a range of numbers.
- The median – this is the midpoint in a range of numbers when the numbers are arranged in order.
- The mode – this is the most commonly repeated number in the data set.
- Standard deviation – this metric indicates how dispersed a range of numbers is. In other words, how close all the numbers are to the mean (the average).
- Skewness – this indicates how symmetrical a range of numbers is. In other words, do they tend to cluster into a smooth bell curve shape in the middle of the graph (this is called a normal or parametric distribution), or do they lean to the left or right (this is called a non-normal or non-parametric distribution).
- Kurtosis – this metric indicates whether the data are heavily or lightly-tailed, relative to the normal distribution. In other words, how peaked or flat the distribution is.
A large table that indicates all the above for multiple variables can be a very effective way to present your data economically. You can also use colour coding to help make the data more easily digestible.
For categorical data, where you show the percentage of people who chose or fit into a category, for instance, you can either just plain describe the percentages or numbers of people who responded to something or use graphs and charts (such as bar graphs and pie charts) to present your data in this section of the chapter.
When using figures, make sure that you label them simply and clearly , so that your reader can easily understand them. There’s nothing more frustrating than a graph that’s missing axis labels! Keep in mind that although you’ll be presenting charts and graphs, your text content needs to present a clear narrative that can stand on its own. In other words, don’t rely purely on your figures and tables to convey your key points: highlight the crucial trends and values in the text. Figures and tables should complement the writing, not carry it .
Depending on your research aims, objectives and research questions, you may stop your analysis at this point (i.e. descriptive statistics). However, if your study requires inferential statistics, then it’s time to deep dive into those .
Step 6 – Present the inferential statistics
Inferential statistics are used to make generalisations about a population , whereas descriptive statistics focus purely on the sample . Inferential statistical techniques, broadly speaking, can be broken down into two groups .
First, there are those that compare measurements between groups , such as t-tests (which measure differences between two groups) and ANOVAs (which measure differences between multiple groups). Second, there are techniques that assess the relationships between variables , such as correlation analysis and regression analysis. Within each of these, some tests can be used for normally distributed (parametric) data and some tests are designed specifically for use on non-parametric data.
There are a seemingly endless number of tests that you can use to crunch your data, so it’s easy to run down a rabbit hole and end up with piles of test data. Ultimately, the most important thing is to make sure that you adopt the tests and techniques that allow you to achieve your research objectives and answer your research questions .
In this section of the results chapter, you should try to make use of figures and visual components as effectively as possible. For example, if you present a correlation table, use colour coding to highlight the significance of the correlation values, or scatterplots to visually demonstrate what the trend is. The easier you make it for your reader to digest your findings, the more effectively you’ll be able to make your arguments in the next chapter.
Step 7 – Test your hypotheses
If your study requires it, the next stage is hypothesis testing. A hypothesis is a statement , often indicating a difference between groups or relationship between variables, that can be supported or rejected by a statistical test. However, not all studies will involve hypotheses (again, it depends on the research objectives), so don’t feel like you “must” present and test hypotheses just because you’re undertaking quantitative research.
The basic process for hypothesis testing is as follows:
- Specify your null hypothesis (for example, “The chemical psilocybin has no effect on time perception).
- Specify your alternative hypothesis (e.g., “The chemical psilocybin has an effect on time perception)
- Set your significance level (this is usually 0.05)
- Calculate your statistics and find your p-value (e.g., p=0.01)
- Draw your conclusions (e.g., “The chemical psilocybin does have an effect on time perception”)
Finally, if the aim of your study is to develop and test a conceptual framework , this is the time to present it, following the testing of your hypotheses. While you don’t need to develop or discuss these findings further in the results chapter, indicating whether the tests (and their p-values) support or reject the hypotheses is crucial.
Step 8 – Provide a chapter summary
To wrap up your results chapter and transition to the discussion chapter, you should provide a brief summary of the key findings . “Brief” is the keyword here – much like the chapter introduction, this shouldn’t be lengthy – a paragraph or two maximum. Highlight the findings most relevant to your research objectives and research questions, and wrap it up.
Some final thoughts, tips and tricks
Now that you’ve got the essentials down, here are a few tips and tricks to make your quantitative results chapter shine:
- When writing your results chapter, report your findings in the past tense . You’re talking about what you’ve found in your data, not what you are currently looking for or trying to find.
- Structure your results chapter systematically and sequentially . If you had two experiments where findings from the one generated inputs into the other, report on them in order.
- Make your own tables and graphs rather than copying and pasting them from statistical analysis programmes like SPSS. Check out the DataIsBeautiful reddit for some inspiration.
- Once you’re done writing, review your work to make sure that you have provided enough information to answer your research questions , but also that you didn’t include superfluous information.
If you’ve got any questions about writing up the quantitative results chapter, please leave a comment below. If you’d like 1-on-1 assistance with your quantitative analysis and discussion, check out our hands-on coaching service , or book a free consultation with a friendly coach.
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Reporting Research Results in APA Style | Tips & Examples
Published on December 21, 2020 by Pritha Bhandari . Revised on July 9, 2022.
The results section of a quantitative research paper is where you summarize your data and report the findings of any relevant statistical analyses.
The APA manual provides rigorous guidelines for what to report in quantitative research papers in the fields of psychology, education, and other social sciences.
Use these standards to answer your research questions and report your data analyses in a complete and transparent way.
Table of contents
What goes in your results section, introduce your data, summarize your data, report statistical results, presenting numbers effectively, what doesn’t belong in your results section, frequently asked questions about results in apa.
In APA style, the results section includes preliminary information about the participants and data, descriptive and inferential statistics, and the results of any exploratory analyses.
Include these in your results section:
- Participant flow and recruitment period. Report the number of participants at every stage of the study, as well as the dates when recruitment took place.
- Missing data . Identify the proportion of data that wasn’t included in your final analysis and state the reasons.
- Any adverse events. Make sure to report any unexpected events or side effects (for clinical studies).
- Descriptive statistics . Summarize the primary and secondary outcomes of the study.
- Inferential statistics , including confidence intervals and effect sizes. Address the primary and secondary research questions by reporting the detailed results of your main analyses.
- Results of subgroup or exploratory analyses, if applicable. Place detailed results in supplementary materials.
Write up the results in the past tense because you’re describing the outcomes of a completed research study.
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Before diving into your research findings, first describe the flow of participants at every stage of your study and whether any data were excluded from the final analysis.
Participant flow and recruitment period
It’s necessary to report any attrition, which is the decline in participants at every sequential stage of a study. That’s because an uneven number of participants across groups sometimes threatens internal validity and makes it difficult to compare groups. Be sure to also state all reasons for attrition.
If your study has multiple stages (e.g., pre-test, intervention, and post-test) and groups (e.g., experimental and control groups), a flow chart is the best way to report the number of participants in each group per stage and reasons for attrition.
Also report the dates for when you recruited participants or performed follow-up sessions.
Another key issue is the completeness of your dataset. It’s necessary to report both the amount and reasons for data that was missing or excluded.
Data can become unusable due to equipment malfunctions, improper storage, unexpected events, participant ineligibility, and so on. For each case, state the reason why the data were unusable.
Some data points may be removed from the final analysis because they are outliers—but you must be able to justify how you decided what to exclude.
If you applied any techniques for overcoming or compensating for lost data, report those as well.
For clinical studies, report all events with serious consequences or any side effects that occured.
Descriptive statistics summarize your data for the reader. Present descriptive statistics for each primary, secondary, and subgroup analysis.
Don’t provide formulas or citations for commonly used statistics (e.g., standard deviation) – but do provide them for new or rare equations.
The exact descriptive statistics that you report depends on the types of data in your study. Categorical variables can be reported using proportions, while quantitative data can be reported using means and standard deviations . For a large set of numbers, a table is the most effective presentation format.
Include sample sizes (overall and for each group) as well as appropriate measures of central tendency and variability for the outcomes in your results section. For every point estimate , add a clearly labelled measure of variability as well.
Be sure to note how you combined data to come up with variables of interest. For every variable of interest, explain how you operationalized it.
According to APA journal standards, it’s necessary to report all relevant hypothesis tests performed, estimates of effect sizes, and confidence intervals.
When reporting statistical results, you should first address primary research questions before moving onto secondary research questions and any exploratory or subgroup analyses.
Present the results of tests in the order that you performed them—report the outcomes of main tests before post-hoc tests, for example. Don’t leave out any relevant results, even if they don’t support your hypothesis.
For each statistical test performed, first restate the hypothesis , then state whether your hypothesis was supported and provide the outcomes that led you to that conclusion.
Report the following for each hypothesis test:
- the test statistic value,
- the degrees of freedom ,
- the exact p- value (unless it is less than 0.001),
- the magnitude and direction of the effect.
When reporting complex data analyses, such as factor analysis or multivariate analysis, present the models estimated in detail, and state the statistical software used. Make sure to report any violations of statistical assumptions or problems with estimation.
Effect sizes and confidence intervals
For each hypothesis test performed, you should present confidence intervals and estimates of effect sizes .
Confidence intervals are useful for showing the variability around point estimates. They should be included whenever you report population parameter estimates.
Effect sizes indicate how impactful the outcomes of a study are. But since they are estimates, it’s recommended that you also provide confidence intervals of effect sizes.
Subgroup or exploratory analyses
Briefly report the results of any other planned or exploratory analyses you performed. These may include subgroup analyses as well.
Subgroup analyses come with a high chance of false positive results, because performing a large number of comparison or correlation tests increases the chances of finding significant results.
If you find significant results in these analyses, make sure to appropriately report them as exploratory (rather than confirmatory) results to avoid overstating their importance.
While these analyses can be reported in less detail in the main text, you can provide the full analyses in supplementary materials.
To effectively present numbers, use a mix of text, tables , and figures where appropriate:
- To present three or fewer numbers, try a sentence ,
- To present between 4 and 20 numbers, try a table ,
- To present more than 20 numbers, try a figure .
Since these are general guidelines, use your own judgment and feedback from others for effective presentation of numbers.
Tables and figures should be numbered and have titles, along with relevant notes. Make sure to present data only once throughout the paper and refer to any tables and figures in the text.
Formatting statistics and numbers
It’s important to follow capitalization , italicization, and abbreviation rules when referring to statistics in your paper. There are specific format guidelines for reporting statistics in APA , as well as general rules about writing numbers .
If you are unsure of how to present specific symbols, look up the detailed APA guidelines or other papers in your field.
It’s important to provide a complete picture of your data analyses and outcomes in a concise way. For that reason, raw data and any interpretations of your results are not included in the results section.
It’s rarely appropriate to include raw data in your results section. Instead, you should always save the raw data securely and make them available and accessible to any other researchers who request them.
Making scientific research available to others is a key part of academic integrity and open science.
Interpretation or discussion of results
This belongs in your discussion section. Your results section is where you objectively report all relevant findings and leave them open for interpretation by readers.
While you should state whether the findings of statistical tests lend support to your hypotheses, refrain from forming conclusions to your research questions in the results section.
Explanation of how statistics tests work
For the sake of concise writing, you can safely assume that readers of your paper have professional knowledge of how statistical inferences work.
In an APA results section , you should generally report the following:
- Participant flow and recruitment period.
- Missing data and any adverse events.
- Descriptive statistics about your samples.
- Inferential statistics , including confidence intervals and effect sizes.
- Results of any subgroup or exploratory analyses, if applicable.
According to the APA guidelines, you should report enough detail on inferential statistics so that your readers understand your analyses.
- the test statistic value
- the degrees of freedom
- the exact p value (unless it is less than 0.001)
- the magnitude and direction of the effect
You should also present confidence intervals and estimates of effect sizes where relevant.
In APA style, statistics can be presented in the main text or as tables or figures . To decide how to present numbers, you can follow APA guidelines:
- To present three or fewer numbers, try a sentence,
- To present between 4 and 20 numbers, try a table,
- To present more than 20 numbers, try a figure.
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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Bhandari, P. (2022, July 09). Reporting Research Results in APA Style | Tips & Examples. Scribbr. Retrieved January 2, 2024, from https://www.scribbr.com/apa-style/results-section/
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