Thus, as a growth curve modeling technique, it allows the estimation of inter-individual differences in intra-individual change over time by modeling the variances and covariances. In addition to estimating overall parameter estimates, MLM allows regression equations at the level of the individual. Thus, level-1 units consist of the repeated measures for each subject, and the level-2 unit is the individual or subject. In multilevel modeling for repeated measures data, the measurement occasions are nested within cases (e.g. Multilevel modeling with repeated measures employs the same statistical techniques as MLM with clustered data. However, the exact intercept and slope could be allowed to vary across individuals (i.e. For example, in a study looking at income growth with age, individuals might be assumed to show linear improvement over time. linear, quadratic, cubic etc.) is fitted to the whole sample and, just as in multilevel modeling for clustered data, the slope and intercept may be allowed to vary. In multilevel modeling, an overall change function (e.g. growth curve modeling for longitudinal designs) however, it may also be used for repeated measures data in which time is not a factor. Multilevel modeling for repeated measures data is most often discussed in the context of modeling change over time (i.e. One application of multilevel modeling (MLM) is the analysis of repeated measures data.
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