This causes two problems.įirst, you will have a different number of response variables for each individual. Repeated measures ANOVA treats each response as a different variable. You measure a response each time some occurence happens. This is common in observed data, where the number of repeats is uncontrollable. Unbalanced number of repeats across individualsĪ related problem is imbalance in the number of repeated responses from each individual. So you may lose the measurement with missing data, but not all other responses from the same subject.Ģ. What to use instead: Marginal and mixed models treat each occasion as a different observation of the same variable. Because it uses listwise deletion, if one measurement is missing, the entire case gets dropped. The problem is that repeated measures ANOVA treats each measurement as a separate variable. One of the biggest problems with traditional repeated measures ANOVA is missing data on the response variable. Let’s go through seven of these and what the options are instead.
There are a few specific design and data situations that will eliminate repeated measures ANOVA as a reasonable approach. These data gymnastics mean you’re throwing away good information and under-accounting for true variation among repetitions. Sometimes trying to fit a data set into a repeated measures ANOVA requires too much data gymnastics. It works very well in certain designs.īut it’s limited in what it can do. Repeated measures ANOVA is the approach most of us learned in stats classes for repeated measures and longitudinal data.
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