### Using **GLM Repeated Measures** Test in Research

This easy tutorial will show you **how to run the GLM Repeated Measures test in SPSS**, and how to interpret the result.

The** GLM Repeated Measures** procedure provides an analysis of variance when the same measurement is made several times on each subject or case. If between-subjects factors are specified, they divide the population into groups. Using this general linear model procedure, you can test null hypotheses about the effects of both the between-subjects factors and the within-subjects factors. You can investigate interactions between factors as well as the effects of individual factors. In addition, the effects of constant covariates and covariate interactions with the between-subjects factors can be included. (Source)

**GLM Repeated Measures** is an ANOVA with repeated measures. We use ANOVA with repeated measures when we want to determine whether there is a difference between continuous variables measured multiple times in the same group of participants. For example, we want to determine whether exercise has an effect on weight loss in a group of 20 participants. Therefore, we will measure the weight of those participants before exercise, after 3 months of exercise, and after 6 months of exercise and we will compare the mean values of each group using an ANOVA with repeated measures.

### Assumptions of the **GLM Repeated Measures** :

- continuous dependent variable
- categorical independent variable with two or more related groups
- normal data distribution
- no significant outliers
- equal variances of the differences between all combinations of related groups (sphericity).

### An Example in SPSS: The GLM Repeated Measures

Null hypothesis:

There is no effect of the intervention on the dependent variable.

Alternative hypothesis:

There is an effect of the intervention on the dependent variable.

We collected data about the Math test score from 51 students before training, after one-month training, and after 2 months of training. We wanted to examine whether there is an effect of training on Math test scores. Therefore, we will use ANOVA with repeated measures. We have a continuous dependent variable (Math test score) and categorical independent variable with three groups (before, after 1 month, after 2 months).

**Methods.** Type I, Type II, Type III, and Type IV sums of squares can be used to evaluate different hypotheses. Type III is the default.

**Statistics.** Post hoc range tests and multiple comparisons (for between-subjects factors): least significant difference, Bonferroni, Sidak, Scheffé, Ryan-Einot-Gabriel-Welsch multiple *F*, Ryan-Einot-Gabriel-Welsch multiple ranges, Student-Newman-Keuls, Tukey’s honestly significant difference, Tukey’s *b*, Duncan, Hochberg’s GT2, Gabriel, Waller Duncan* t-test*, Dunnett (one-sided and two-sided), Tamhane’s T2, Dunnett’s T3, Games-Howell, and Dunnett’s *C*. Descriptive statistics: observed means, standard deviations, and counts for all of the dependent variables in all cells; the Levene test for homogeneity of variance; Box’s *M*; and Mauchly’s test of sphericity.

This easy tutorial will show you how to **run the GLM Repeated Measures test in SPSS**, and how to interpret the result.