Repeated measure analysis involves a statistical test called analysis of variance. There are different types of ‘within subject’ designs. The true ‘within subject’ design in this repeated measure analysis is a design in which each subject is measured under each treatment condition. The repeated measures design in this repeated measure analysis is a design in which each subject is measured at two or more points with respect to time. The profile analysis design in this repeated measure analysis is that which involves the comparison of the scores of the different tests that are comparably scaled.
The matched subjects design in this repeated measure analysis is that type of within subject design where instead of having the participation of the subject in all the levels, different subjects who are matched on a prior basis with the relevant variables are compared.
This repeated measure analysis is applicable to those research situations that utilize these within subject designs. The repeated measure analysis is applicable to the within subject designs like repeated measure design, profile analysis, matched subject design, etc.
Some parts of the theory and the method are similar to that of the independent measures of ANOVA. The difference is that a second phase is included, which involves the removal of the variability due to the individual differences from the error term.
The repeated measure design mechanically removes the individual differences from the between treatments variability as the same subjects are being used in every condition. Further, in this design, individual differences are removed from the denominator of the F-test.
The result obtained is a test statistic that is similar to the independent measures’ result, except that in the result, all the individual differences are removed.
The common technique for measuring an effect size in this type of repeated measure analysis is to compute the percentage of variance that has been explained by the treatment effects. The percentage to be computed for the determination of the effect size is identified as eta square. The researcher should keep in mind that before computing eta square for the determination of the effect size it is necessary to eliminate the individual differences between the subjects.
One of the major assumptions of this type of repeated measure analysis is that of sphericity. If this assumption of sphericity is violated, then the value of F statistic will come out with severely biased results. In other words, if the assumption of sphericity is violated, then the researcher might end up committing Type I error.
There are options available for the researcher to override this violation of assumptions while performing this type of repeated measure analysis. The researcher can do an adjusted degree of freedom test or use the Green house Geisser method to overcome the effects of violation.
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