Frequency Analysis: How to Run It Online and Report It in APA7
A frequency analysis counts how often each category of a variable occurs and shows the counts as percentages.
What is Frequency Analysis?
A frequency analysis counts how often each category of a variable occurs and shows the counts as percentages.
When to use it
Use it for categorical variables such as gender, education level, group membership or answers on a Likert item, to describe your sample and to check category sizes before a chi-squared test.
Example question: How many participants were in each gender and education category?
Preparing your data
One row per participant and one column per categorical variable. Each cell holds one category label (for example Female, Male) or a numeric code with a consistent meaning. Spell each label identically everywhere, because "Female" and "female " are counted as different categories.
Example dataset (simulated; first rows of 120): id, gender (Female, Male, Non-binary), education (four levels).
| id | gender | education |
|---|---|---|
| 1 | Female | High school |
| 2 | Male | Bachelor's |
| 3 | Female | Master's |
| 4 | Female | Bachelor's |
| 5 | Female | High school |
| 6 | Male | Doctorate |
Download the full example CSV to practise. If the tool asks for a different layout, follow the instructions on its page.
How to run it on StatFas
- Open the Frequency Analysis tool. You do not need an account to run an analysis.
- Enter your data or upload your file, laid out as described in Preparing your data.
- Select your variables. Select the categorical variable(s) you want to count.
- Choose any options the tool offers (for example the confidence level, a post hoc test or the error bar type) and run the analysis.
- Read the results on the page. Check the assumptions first, then the test statistic, the p-value and the effect size.
- To save your results, log in or create a free account. Every analysis stays free; the account is only needed to download.
Assumptions and checks
- Categories must be mutually exclusive: each person falls into one category per variable.
- Merge or flag very small categories (for example fewer than 5 cases) before testing, because they make later tests unstable.
- Report the number of missing responses separately instead of hiding them in the percentages.
- Decide whether percentages use all participants or only valid responses, and say which.
Key numbers to look at
- Frequency: the count in each category.
- Percent of all cases and valid percent of non-missing cases.
- Cumulative percent, useful only for ordered categories such as education level.
- Mode: the most common category.
Effect size
Frequencies have no effect size. Associations between two categorical variables are measured with Cramér's V in the chi-squared test.
Benchmarks such as small, medium and large are conventions (Cohen, 1988), not rules. Interpret size in the context of your field.
Reporting Frequency Analysis in APA7 style
Template
For many variables, use an APA table with columns n and %.
Worked example (simulated data)
The numbers below were computed from the example CSV above, so you can reproduce them. They illustrate the format and are not real findings.
| Gender | n | % |
|---|---|---|
| Female | 66 | 55.0 |
| Male | 52 | 43.3 |
| Non-binary | 2 | 1.7 |
APA7 reminders: italicise statistical symbols (M, SD, t, F, p, r), report exact p-values to two or three decimals (use p < .001 for very small values), omit the leading zero for values that cannot exceed 1 (.45, not 0.45), and put degrees of freedom in parentheses. See the APA7 cheat sheet.
Common mistakes
- Using the mean of a nominal code (for example reporting a mean gender of 1.4).
- Giving percentages without the counts, or counts without the total.
- Leaving inconsistent spellings in the data, which splits one category into several.
Related analyses and alternatives
- Chi-squared Test: To test whether two categorical variables are associated.
- Binomial Test: To test one two-category variable against an expected proportion.
- Pie Chart: To show category shares as a chart.
Not sure which analysis you need? Use the test chooser.
Frequently asked questions
How many decimals should percentages have?
One decimal is the usual choice (for example 55.0%). Use whole numbers only for very large samples.
Can I use frequencies for numeric data?
Yes, for discrete values such as the number of children or a 1–5 rating. For continuous data such as height, group the values into bins or use descriptive statistics instead.
References
American Psychological Association. (2020). Publication manual of the American Psychological Association (7th ed.). https://doi.org/10.1037/0000165-000
This guide is for learning and for preparing results for your own reports. You remain responsible for choosing an appropriate analysis, checking its assumptions and interpreting the results. Follow the formatting rules of your journal or institution where they differ from APA7.