Introduction
A One-Way Repeated-Measures ANOVA compares the difference between more than two related groups, such as comparing the difference between three time points. If your data only has two groups such as a pre/post-test you should consider the Paired-Samples t-Test.
It is considered a parametric test and is only suitable for parametric data. To check if your data is parametric, please check out the dedicated guide here: Parametric or Not Guide
If your data is non-parametric you should consider using Freidman's ANOVA
Test Procedure
Step 1: Click Analyze > General Linear Model > Repeated Measures
Step 2: Within the "Repeated-Measures Define Factors" Window, create a name for your repeate-measures factor and specify the number of levels. Click Add. Click Define.
Step 3: Within the "Repeated Measures" Window, select the dependent variables you are analysing and move them into the "Within-Subjects Variables" box.
Step 4: Click "EM Means". Within the "Repeated Measures: Estimated Marginal Means" window select your within-subjects factor and move it to the "Dispaly Means for" window. Select "Compare Main Effects"
Step 5: Click "Options". Within the "Repeated Measures: Options" window select "Descriptive Statistics", "Estimates of effect size", "Homogeneity tests", and "Observed power".
Step 6: Select Continue à OK
Results
SPSS will generate multiple tables, to correctly report this test we need two, the Test of Between Subject Effects, and the Estimates/Descriptive Statistics.
Test of Between Subject Effects
This table shows the test results including the F-statistic (F), the degrees of freedom (df), the two-tailed significance or p-value (Sig), and the effect size (Partial Eta-Squared).
Estimates/Descriptive Statistics
Either of these tables show the descriptive statistics: the sample size of each group (N), the mean of each group (Mean), and the standard deviation of each group (Std. Deviation), best practice is to report them all.
Reporting the Results in APA Formatting
Samples were evaluated at across three-time points( Week 1, Week 2, Week3). A One-Way Between-Subjects ANOVA indicated there was a significant effect for test score, F(1,156) = 8241.26, p < .001, n2p = .981.
On average, Week 3 (Mean = 6.48, SD = 1.72) values were higher than Group B (Mean = 6.03, SD = 1.37), but lower than Group C (Mean = 5.57, SD = 1.12).
Post hoc comparisons were conducted using the Bonferroni method.
The difference between Week 1 and Week 2, -0.46 95% CI [-0.73,-0.18], was considered statistically significant (p = .001).
The difference between Week 2 and Week 3, -0.46 95% CI [-0.76,-0.13], was considered statistically significant (p = .001).
The difference between Week 1 and Week 3, -0.92 95% CI [-1.26,-0.58], was considered statistically significant (p < .001).
If your ANOVA is significant you must report your Post-Hoc results, these are indicated in green.
