They’re common in linguistics and psychology. There is a repeated measures design that occurs in specific experimental studies. Repeated measures across people and items What to use instead: A mixed model can incorporate multiple levels.Ħ. In all these cases, the repeated measures ANOVA can account for the repeats over time, but not the clustering. Patients measured over time are also clustered into medical centers. Streams may be measured over time, but are also clustered into watersheds. If the subjects themselves are not only measured multiple times, but also clustered into some other groups, you’ve got a three-level model.įor example, you may have students measured over time, but students are also clustered within classrooms. What to use instead: A marginal or mixed model can incorporate time-varying covariates. In some studies, the important predictor variables are measured on each repeat, right along with the response.īecause of that wide-data format, there’s no way to specify that each measurement of the covariate variable should only predict the corresponding response. What to use instead: A marginal or mixed model can treat time as a truly continuous effect. There are contrasts that allow you to order the categories and simulate a trend over time, but they’re not truly treating time as continuous. This is theoretically valid and reasonable, but repeated measures ANOVA can only account for categorical repeats. In other words, you want to treat the within-subjects effect of time as a continuous, quantitative variable. (Or equivalently, the amount of space if the repeats are say, along a transect).
In others, the amount of time that has passed between repeats is important. There is a qualitative difference among the repeats. In some repeated measures studies, each repeat occurs under a different experimental condition. What to use instead: A mixed model can handle unequal repeats. But there is no way to turn off that comparison. Here they don’t-they’re really interchangeable. Second, the ANOVA will compare the responses to each other, assuming that each one represents a different condition. If some have missing data in the last few responses, they’ll get dropped. 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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