GLM Repeated Measure

GLM repeated measure is a statistical technique that takes a dependent, or criterion variable, measured as correlated, non-independent data.  Commonly used when measuring the effect of a treatment at different time points.  The independent variables may be categorical or continuous.  GLM repeated measure can be used to test the main effects within and between the subjects, interaction effects between factors, covariate effects and effects of interactions between covariates and between subject factors.  GLM repeated measures in SPSS is done by selecting “general linear model” from the “analyze” menu.  From general linear model, select “repeated measures” and then preform “GLM repeated measures.”

Questions Answered:

  • What effect does an intervention program have on middle school students in regards to the various socioeconomic status levels?
  • Are there differences in attitudes by gender on the same group of people measured at three different time points?

Key Terms:

  • The within-subject factor: the basic factor for the repeated measurements.  This factor in GLM repeated measure carries certain levels.  The within-subjects factor will have as many levels as there are repetitions (e.g., time 1, time 2, time 3, etc.).
  • Covariates: quantitative independent variables.
  • Bartlett’s test of sphericity test: This will indicate if the factor model is inappropriate or not by determining whether or not the correlation matrix is an identity matrix.
  • Levene’s test: tests whether the variability is homogeneous or not.
  • Residual Plot: this shows the difference between the calculated and measured values of the dependent variables.  A plot with excessive different trends will usually indicate an inappropriate model.

Assumptions:

  • Data is matched across a similar characteristic and is non-independent.
  • The dependent variables should be assumed to follow a multivariate normal distribution.
  • It should be assumed that the effects in should be fixed effects.
  • The homogeneity of the variance is assumed, this can be tested with the help of Levene’s test.

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