A Friedman's 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 Wilcoxon Test.
It is considered a non-parametric test and is suitable for non-parametric data. To check if your data is parametric, please check out the dedicated guide: Parametric or Not Guide (PDF)
If your data is parametric you should consider using a One-Way Repeated-Measures ANOVA
Click Analyze > Nonparametric Tests > Legagy Dialogs > K Related Samples
Within the "Tests for Several Related Samples" Window, move your measurements into the "Test Variables" box.
Click "Statistics". Within the "Several Related Samples: Statisitcs", select 'Descriptives' and 'Quartiles'.
Select Continue à OK
SPSS will generate three tables, to correctly report this test we need two, the Descriptive Statistics and the Test Statistics:
This table shows 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). For non-parametric tests the most appropriate average would be the Median. You can get this out using the descriptive analysis in SPSS.
This table shows the results of the statistics test including the sample size (N), the Chi-Square statistic (Chi-Square), the degrees of freedom (df), and the p-value/significance (Asymp. Sig.).
Samples were evaluated across three times (Week 1, Week 2, Week 3) using a Friedman's analysis of variance. Differences across the weeks were significant, χ2(2) = 22.76, p < .001.
In addition, if your ANOVA is significant you must also report your post-hoc results:
A series of Wilcoxon tests would be an appropriate way to compare the paired groups (weeks), with a Bonferroni's correction applied to compensate for the multiple comparrisons. Giving the medians her would be useful to help interpretation of differences.
If you need help running the post-hoc test check our Wilcoxon Test guide.