How to Write Results Chapter APA 7th Edition?

Published by at February 6th, 2022 , Revised On February 22, 2022

In a quantitative research paper, the results/findings chapter is where you summarize your data and interpret the results of any applicable statistical analysis.

In the disciplines like psychology, education, and other social sciences, the APA handbook offers strict criteria for what to report in quantitative research reports.

Use these guidelines to respond to your research questions and provide your data analysis in a clear and concise manner.

What to Write in Result chapter?

The results chapter of an APA paper is supposed to present basic information about the research participants and the data, descriptive and inferential statistics, as well as the results of the exploratory analyses.

The chapter should include:

  • Flow of participants and recruiting period: At each step of the study, provide the number of participants and the dates when they were recruited.
  • Data is missing: Determine the percentage of data that was not included in the final analysis and explain the reason.
  • Any negative outcomes: Make a note of any unusual occurrences or adverse outcomes.
  • Descriptive statistics: Summary of the primary and secondary study outcomes.
  • Report the Statistical Result: Report the comprehensive results of the key analysis to address the primary and secondary research questions.

Since you are reporting the results of a completed research project, write the results in the past tense.

Find here: Apa referencing style articles.

Introduction of Your Research Participants

The first step of the results chapter is to introduce the participants in your study. Introduce them at each stage of your study and explain the reason if any data is excluded from the study.

Flow of Participants and Recruiting Period:

Every attenuation – i.e. a decrease in the number of participants at any stage of a study – must be reported. The reason for this is that an unbalanced number of participants in each group could potentially affect internal validity and make group comparisons difficult. Make a list of all factors for dropout.

Example: 55 (20 %) of the 275 people who participated in the first screening survey were rejected because they did not meet the requirements of the study.

The remaining 220 participants were asked to complete an online survey about the study in exchange for research hours. However, another 12 people did not complete it and out from the research, bringing the total number of participants to 208.

When Data is Missing:

Another important consideration is the integrity of the dataset. It is mandatory to specify the volume of missing or omitted data, as well as the reasons for it. Data may be rendered invalid due to instrument/tool failure, improper storage, unforeseen events, ineligibility of participants and other factors. Provide an explanation of why the data was invalid or useless in each situation.

Example: The data taken from 10 study participants was marked invalid because they answered the questions incorrectly. For another two participants, data were lost due to instrument failure.

Negative Outcome:

In case you are conducting clinical research, any negative event or outcome, must be covered and explained.

Data Summary:

Descriptive statistics describe your data. Provide descriptive statistics for each primary, secondary and subgroup analysis.

Descriptive Data:

The nature of the data in your study will determine the specific descriptive statistics you provide. Proportions can be used for categorical variables, while means and standard deviations can be used to describe quantitative data. A table is the most effective form of presentation for a large set of numbers. In the results chapter, include sample size and appropriate measures of central tendency and variability of results. Include a clearly labelled measure of variability for each point estimate.

Report the Statistical Results:

All relevant hypothesis tests, estimated effect sizes and the confidence intervals must be reported in accordance with the APA journal guidelines. Before moving on to secondary research questions and exploratory or subgroup analyses, address the primary research questions first when providing statistical data. Present the results of the tests in the order in which you conducted them – for example, give the results of the main tests before the post-hoc tests. Even if the results do not support your theory, do not omit them.

Presentation of the Numbers:

Use a combination of sentences, tables, and figures present the numbers:

  • Try using a sentence to present three or fewer numbers:
  • Try a table to present the number if they are more than 3 and 20.
  • Try a figure to present the numbers 20 forward.

Because these are only suggestions, you should rely on your own judgement and feedback from supervisor to display data effectively. Tables and figures should be numbered and titled, and appropriate remarks should be included. Make sure you only provide data once throughout the document and to make use of any tables and figures that are included in the text.

Format of statistical Data:

If you refer to statistics in your dissertation, be sure to follow the guidelines for capitalization, italics and abbreviations. In the APA, there are specific criteria for presenting statistics as well as general standards for writing figures. Consult the full APA rules or comparable papers in your subject if you are unsure how to present certain symbols.

What Should Not Be in Your Results Chapter?

It is very important to give a clear presentation of your data analysis and results in a succinct way. Therefore, raw data and interpretations of your results should not be included in the results section.

FAQs

The results chapter or section simply and objectively sums up whatever you have discovered, without explaining why these results were discovered. In qualitative research, results and conversation are sometimes integrated. In quantitative research, however, it is important to separate the objective data from your perception of the data.

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