# 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 | 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 |