From the `Analysis` menu choose `Compare repeated measures variables`, and choose the variables you want to compare, then hit OK.

To use several factors, click on `Factors...` and set the names and the levels of the factors, and click OK. Then you can select the variables for the factor level combinations.

• Only the cases where all variables are available are used.
• Variables to be compared have to have the same measurement levels.

In the `Display options...` dialog, the minimum and maximum of the y-axis, and (new in v2.4) the way factors are displayed can be set.

The following results will be calculated (see also the common elements of the results):

## Raw data

Result For interval variables For ordinal variables For nominal variables
Sample size Number of observed cases Number of observed cases Number of observed cases
Graphs of the raw data Plot with individual data Plot with individual data Mosaic plot

For individual data plots, values of a single case are connected.

## Sample properties

Result For interval variables For ordinal variables For nominal variables
Descriptive data Means, Standard deviations, Maximums, Upper quartiles, Medians, Lower quartiles, Minimums Maximums, Upper quartiles, Medians, Lower quartiles, Minimums Variation ratio, Contingency table (case count), Contingency table (percentage)
Standardized effect size For two groups Cohen’s d and eta-squared
Graphs of the data Box plot with individual data Box plot with individual data

For individual data plots, values of a single case are connected.

## Population properties

Result For interval variables For ordinal variables For nominal variables
Population parameters numerically Mean with 95% CI Median and its confidence interval Contingency table (confidence interval, multinomial proportions with Goodman method)
Standardized effect size For two groups, Hedges’ g and its confidence interval
Graphs of the population parameters Graph with mean and 95% CI of the mean
Hypothesis test for two variables and sensitivity power analyses If the data are normal (measured with Shapiro-Wilk test), then paired t-test and (new in v2.3) Bayesian paired t-test

Otherwise, paired Wilcoxon test (see CogStat specific details)

For paired t-test sensitivity power analyses
Paired Wilcoxon test (see CogStat specific details) If the variables are dichotomous, then McNemar’s test

Otherwise, no test is provided by CogStat
Hypothesis test for more than two variables If the data are normal (measured with Shapiro-Wilk test) repeated measures ANOVA
For ANOVA, sphericity is checked with Mauchly’s sphericity test. If sphericity is violated, Greenhouse-Geisser correction is applied.
If ANOVA is significant, Holm-Bonferroni corrected post-hoc tests are run.

For non-normal variables Friedman test
(New in v2.3) If the Friedman test is significant, post hoc Durbin-Conover test
Friedman test
(New in v2.3) If the Friedman test is significant, post hoc Durbin-Conover test
If the variables are dichotomous, then Cochran Q-test

Otherwise, no test is provided by CogStat
Hypothesis test for two factors Repeated measures ANOVA