A two-way (factorial) ANOVA tests the effect of two categorical independent variables on one continuous outcome, and crucially whether they interact. The write-up is longer than a one-way ANOVA because you report two main effects plus the interaction, each with its own F statistic and effect size. This guide gives you the exact APA 7 format, a worked example you can copy, and the mistakes reviewers catch.
What you need before you write a single word
An APA 7 write-up of a two-way ANOVA needs, for each of the three effects (Factor A, Factor B, and the A by B interaction):
- The F value.
- Two degrees of freedom (effect df and error df).
- The exact p value.
- An effect size, usually partial eta squared.
You also need the cell means and standard deviations (each combination of the two factors), best shown in a table.
The APA 7 format template
Report each effect in running text. The interaction is the most important, so lead with whether it was significant:
A two-way ANOVA showed a significant main effect of [Factor A], F(df, df_error) = X.XX, p = .XXX, partial η² = .XX, and of [Factor B], F(df, df_error) = X.XX, p = .XXX, partial η² = .XX. The [Factor A] by [Factor B] interaction was [significant/not significant], F(df, df_error) = X.XX, p = .XXX, partial η² = .XX.
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Formatting rules reviewers actually check:
- Report both degrees of freedom for every F: effect df first, then error df.
- No leading zero on p or partial eta squared.
- Round F to two decimals. If p is below .001, write p < .001.
- If the interaction is significant, interpret it (often with simple-effects tests), because it qualifies the main effects.
A worked example
Suppose you tested the effect of teaching method (lecture vs flipped) and study time (low vs high) on exam scores, with 20 students per cell.
- Main effect of method: F(1, 76) = 9.84, p = .002, partial η² = .11
- Main effect of study time: F(1, 76) = 18.30, p < .001, partial η² = .19
- Interaction: F(1, 76) = 5.12, p = .027, partial η² = .06
Written up in APA 7, that becomes:
A two-way ANOVA showed a significant main effect of teaching method, F(1, 76) = 9.84, p = .002, partial η² = .11, and of study time, F(1, 76) = 18.30, p < .001, partial η² = .19. These were qualified by a significant method by study-time interaction, F(1, 76) = 5.12, p = .027, partial η² = .06. Simple-effects tests showed that the flipped classroom advantage was larger for high-study-time students than for low-study-time students.
Now do it with your numbers
Paste the table straight out of your SPSS output window. You’ll get the APA 7 sentence, the effect size with its confidence interval, and a check that your p-value actually matches your test statistic.
No account. Your dataset never leaves your computer — only the summary numbers you paste are sent. If we can’t read your table we keep a copy of it so we can add support for it; nothing we keep identifies you.
The APA 7 cell-means table
A two-way design needs a table of cell means and SDs. APA tables use horizontal rules only:
| Method | Study time | M | SD |
|---|---|---|---|
| Lecture | Low | 68.20 | 8.90 |
| Lecture | High | 74.10 | 8.20 |
| Flipped | Low | 71.50 | 8.60 |
| Flipped | High | 83.40 | 7.80 |
Note. Exam scores range from 0 to 100. n = 20 per cell.
Reporting a non-significant result
Use the same statistics; change the wording, and do not call it a trend:
The method × study-time interaction was not significant, F(1, 76) = 0.84, p = .362, partial η² = .01, so the two main effects can be interpreted on their own.
Where to find these numbers in SPSS
- Tests of Between-Subjects Effects: one row per main effect and for the interaction, each with df, F, Sig. and (if "Estimates of effect size" was ticked) Partial Eta Squared. The Error row gives the second df.
- Descriptive Statistics or Estimated Marginal Means: the cell means for the text and table.
Mistakes reviewers catch
- Ignoring the interaction. The interaction is often the whole point of a factorial design. Always report it, and if it is significant, interpret the main effects in light of it.
- Reporting only one degree of freedom. Every F needs both, effect and error: F(1, 76).
- Using eta squared instead of partial eta squared. In factorial designs, partial eta squared is the conventional effect size.
- No cell means. The reader needs the mean for each combination of factors, not just the F statistics.
- Writing p = .000. Report p < .001.
- A leading zero on p or partial eta squared. APA drops it.
Before you report: did the model's assumptions hold?
A two-way ANOVA assumes:
- Independence of observations.
- Approximate normality of the outcome within each cell.
- Homogeneity of variance across cells (Levene's test).
Roughly equal cell sizes make the test more robust to violations. For a single factor, see our guide on reporting a one-way ANOVA.
Let KyroStat do the write-up for you
Formatting a factorial ANOVA, with two main effects, an interaction, partial eta squared, and a cell-means table, is where errors creep in. KyroStat runs the two-way ANOVA on your data, tests the interaction and simple effects, checks the assumptions, and hands you the finished APA 7 sentences, the cell-means table, the plot, and the underlying Python or R code. Upload your spreadsheet, and the report is done in seconds.
Frequently asked questions
What makes a two-way ANOVA different from a one-way ANOVA? A one-way ANOVA has one factor. A two-way ANOVA has two factors and also tests their interaction, whether the effect of one factor depends on the level of the other.
Which effect size should I report? Partial eta squared for each main effect and the interaction, reported without a leading zero.
What if the interaction is significant? Interpret the main effects cautiously and run simple-effects tests to describe how one factor's effect changes across levels of the other.
My p value shows as .000. What do I write? Report p < .001. A p value is never exactly zero.
Which related guides might I need? Often these: mixed ANOVA, post hoc tests and APA table format.