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Repeated measure analysis is used when all members of a random sample are measured under a number of different conditions. As the sample is exposed to each condition in turn, the measurement of the dependent variable is repeated. Using a standard ANOVA in this case is not appropriate because it fails to model the correlation between the repeated measures.

One should be clear about the difference between a **repeated measures design** and a **simple multivariate design.** For both, sample members are measured on several occasions, or trials, but in the repeated measures design, each trial represents the measurement of the same characteristic under a different condition.

In SAS **PROC GLM** is used to carry out repeated measure analysis.

The basic syntax for PROC GLM in SAS is −

PROC GLM DATA = dataset; CLASS variable; MODEL variables = group / NOUNI; REPEATED TRIAL n;

Following is the description of the parameters used −

**dataset**is the name of the dataset.**CLASS**gives the variables the variable used as classification variable.**MODEL**defines the model to be fit using certain variables form the dataset.**REPEATED**defines the number of repeated measures of each group to test the hypothesis.

Consider the example below in which we have two groups of people subjected to test of effect of a drug. The reaction time of each person is recorded for each of the four drug types tested. Here 5 trials are done for each group of people to see the strength of correlation between the effect of the four drug types.

DATA temp; INPUT person group $ r1 r2 r3 r4; CARDS; 1 A 2 1 6 5 2 A 5 4 11 9 3 A 6 14 12 10 4 A 2 4 5 8 5 A 0 5 10 9 6 B 9 11 16 13 7 B 12 4 13 14 8 B 15 9 13 8 9 B 6 8 12 5 10 B 5 7 11 9 ; RUN; PROC PRINT DATA = temp ; RUN; PROC GLM DATA = temp; CLASS group; MODEL r1-r4 = group / NOUNI ; REPEATED trial 5; RUN;

When the above code is executed, we get the following result −

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