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One-Way ANOVA with Repeated Measures: How to Run It Online and Report It in APA7

One-way repeated-measures ANOVA tests whether the means of the same participants differ across three or more conditions or time points.

Run One-Way ANOVA with Repeated Measures now (free)Jump to APA7 reportingDownload example data (CSV)
What it isWhen to use itPrepare your dataRun itAssumptionsRead the resultsEffect sizeAPA7 reportingMistakesAlternativesFAQ

What is One-Way ANOVA with Repeated Measures?

One-way repeated-measures ANOVA tests whether the means of the same participants differ across three or more conditions or time points.

When to use it

Use it for the same people measured several times (baseline, mid, end) or exposed to several conditions, and an outcome that is continuous.

Example question: Did stress change across baseline, week 4 and week 8?

Preparing your data

Wide format: one row per participant and one numeric column per condition or time point (for example t1, t2, t3).

Example dataset (simulated; first rows of 24): id, week0, week4, week8 (stress at three time points for the same participants); wide format.

idweek0week4week8
1.028.016.332.6
2.029.023.121.5
3.037.530.137.6
4.022.226.331.8
5.020.225.026.2
6.037.632.640.9

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

  1. Open the One-Way ANOVA with Repeated Measures tool. You do not need an account to run an analysis.
  2. Enter your data or upload your file, laid out as described in Preparing your data.
  3. Select your variables. Select all the columns that represent the repeated measurements of the same variable.
  4. Choose any options the tool offers (for example the confidence level, a post hoc test or the error bar type) and run the analysis.
  5. Read the results on the page. Check the assumptions first, then the test statistic, the p-value and the effect size.
  6. 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

  • The same participants appear in every condition; participants with missing data are usually dropped.
  • Differences between conditions are roughly normal.
  • Sphericity: the variances of the differences between all pairs of conditions are about equal (Mauchly's test). If violated, use the Greenhouse–Geisser correction (when ε is below about .75) or Huynh–Feldt (when above).
  • No strong order or practice effects, or conditions are counterbalanced.

Key numbers to look at

  • Mauchly's test of sphericity and the epsilon (ε) corrections.
  • F(df1, df2) with the corrected degrees of freedom when sphericity is violated.
  • Partial η2.
  • Pairwise comparisons with Bonferroni adjustment.

Effect size

Partial η2 ≈ .01 small, .06 medium, .14 large. Generalised η2 is also common in repeated-measures designs.

Benchmarks such as small, medium and large are conventions (Cohen, 1988), not rules. Interpret size in the context of your field.

Reporting One-Way ANOVA with Repeated Measures in APA7 style

Template

Mauchly's test indicated that sphericity was violated, χ²(2) = X.XX, p = .XXX, so Greenhouse–Geisser corrected degrees of freedom were used (ε = .XX). Stress changed significantly across time, F(X.XX, XX.XX) = XX.XX, p < .001, ηp2 = .XX. Bonferroni-adjusted comparisons showed that …

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.

Mauchly's test indicated that sphericity had been violated, χ2(2) = 11.47, p = .003; Greenhouse–Geisser ε = .71. Greenhouse–Geisser corrected degrees of freedom were therefore used. Stress changed significantly across time, F(1.42, 32.71) = 7.15, p = .006, ηp2 = .24. Bonferroni-adjusted pairwise comparisons: week 0 vs. week 4: adjusted p = .149; week 0 vs. week 8: adjusted p = .005; week 4 vs. week 8: adjusted p = .184.

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

  • Ignoring the sphericity test and corrections.
  • Treating repeated measurements as independent groups.
  • Running many pairwise tests without adjusting the p-values.

Not sure which analysis you need? Use the test chooser.

Frequently asked questions

What is sphericity?

The condition that the variability of the differences between each pair of conditions is the same. With only two conditions it is automatically satisfied.

Which correction should I use?

Greenhouse–Geisser is conservative and a safe default. Huynh–Feldt is less conservative and used when ε is high.

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References

American Psychological Association. (2020). Publication manual of the American Psychological Association (7th ed.). https://doi.org/10.1037/0000165-000

Cohen, J. (1988). Statistical power analysis for the behavioral sciences (2nd ed.). Lawrence Erlbaum Associates.

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.