Compare repeated measures variables
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 | Spaghetti plot showing individual data | Spaghetti plot showing 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 showing quartiles with spaghetti plot showing individual data | Box plot showing quartiles with spaghetti plot showing 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 |