For example, in a study looking at income growth with age, individuals might be assumed to show linear improvement over time.
Multilevel modeling with repeated measures employs the same statistical techniques as MLM with clustered data.
[2] In other words, it allows the testing of individual differences in patterns of responses over time (i.e. growth curves).
However, one point to note is that time-related predictors must be explicitly entered into the model to evaluate trend analyses and to obtain an overall test of the repeated measure.
However, there are circumstances in which either MLM or SEM are preferable:[4][6] The distinction between multilevel modeling and latent growth curve analysis has become less defined.
In long form, each subject’s data is represented in several rows – one for every “time” point (observation of the dependent variable).