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

Two-way repeated-measures ANOVA tests two factors when at least one of them is a within-subject (repeated) factor, plus their interaction.

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What it isWhen to use itPrepare your dataRun itAssumptionsRead the resultsEffect sizeAPA7 reportingMistakesAlternativesFAQ

What is Two-Way ANOVA with Repeated Measures?

Two-way repeated-measures ANOVA tests two factors when at least one of them is a within-subject (repeated) factor, plus their interaction.

When to use it

Use it when the same participants experience combinations of two factors, for example condition (control, treatment) measured at two or more times. Check whether both factors are within-subject, or one is between-subject (a mixed design).

Example question: Did the treatment change scores from pre to post more than the control condition did?

Preparing your data

Wide format: one row per participant with one numeric column for each combination of the repeated factors (for example control_pre, control_post, treatment_pre, treatment_post). A between-subject factor, if any, is an extra column.

Example dataset (simulated; first rows of 24): id, control_pre, control_post, treatment_pre, treatment_post (anxiety); wide format, both factors repeated.

idcontrol_precontrol_posttreatment_pretreatment_post
1.020.921.126.126.0
2.016.118.010.85.1
3.015.215.017.16.3
4.028.828.935.231.2
5.015.714.911.58.2
6.023.228.921.012.8

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 Two-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. Define which columns belong to which level of each factor and select the outcome.
  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 provide data in every repeated condition.
  • Differences are roughly normal; there are no extreme outliers.
  • Sphericity for any repeated factor with three or more levels (use Greenhouse–Geisser or Huynh–Feldt corrections).
  • Homogeneity of variance and covariance across groups in mixed designs.
  • Counterbalanced conditions to limit order effects.

Key numbers to look at

  • Main effects of both factors and the interaction, each with F, df and p (corrected if needed).
  • Partial η2 for each effect.
  • Mauchly's test and epsilon corrections.
  • Interaction plot and simple effects.

Effect size

Partial η2 ≈ .01 small, .06 medium, .14 large.

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

Reporting Two-Way ANOVA with Repeated Measures in APA7 style

Template

There was a significant condition × time interaction, F(1, XX) = XX.XX, p < .001, ηp2 = .XX. Simple effects showed that scores improved from pre to post in the treatment condition, p < .001, but not in the control condition, p = .XXX.

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.

A 2 (condition: control, treatment) × 2 (time: pre, post) repeated-measures ANOVA showed a condition × time interaction, F(1, 23) = 5.57, p = .027, ηp2 = .20. Simple effects (paired t-tests) showed that anxiety decreased from pre to post in the treatment condition, t(23) = −3.77, p = .001, but did not change significantly in the control condition, t(23) = −0.55, p = .587. With two levels per factor, sphericity is not an issue.
Effectdf1df2Fp
Condition1235.84.024
Time12311.59.002
Condition × time1235.57.027

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

  • Interpreting main effects before the interaction.
  • Ignoring sphericity for factors with three or more levels.
  • Dropping participants with missing cells without reporting how many.

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

Frequently asked questions

What is a mixed ANOVA?

A design with at least one between-subject factor (different people in each group) and at least one within-subject factor (the same people measured repeatedly).

Do I need sphericity if each factor has two levels?

No. Sphericity only matters for within-subject factors with three or more levels.

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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.