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In multilevel modeling, an overall change function (e.g. 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. For example, in a study looking at income growth with age, individuals might be assumed to show linear improvement over time. However, the exact intercept and slope could be allowed to vary across individuals (i.e. defined as random coefficients).
Multilevel modeling with repeated measures employs the same statistical techniques as MLM with clustered data. In multilevel modeling for repeated measures data, the measurement occasions are nested within cases (e.g. individual or subject). Thus, [[Multilevel model#Level 1 Regression Equation|level-1]] units consist of the repeated measures for each subject, and the [[Multilevel model#Level 1 Regression Equation|level-2]] unit is the individual or subject. In addition to estimating overall parameter estimates, MLM allows for regression equations at the level of the individual. Thus, as a growth curve modeling technique, it allows for the estimation of inter-individual differences in intra-individual change over time by modeling the variances and covariances.<ref>{{cite journal|last=Curran|first=Patrick J.|coauthors=Obeidat, Khawla, Losardo, Diane|title=Twelve Frequently Asked Questions About Growth Curve Modeling|journal=Journal of Cognition and Development|date=NaN undefined NaN|volume=11|issue=2|pages=121–136|doi=10.1080/15248371003699969}}</ref> In other words, it allows for the testing of individual differences in patterns of responses over time (i.e. growth curves). This characteristic of multilevel modeling makes it preferable to other repeated measures statistical techniques such as repeated measures-analysis of variance ([[RM-ANOVA]]) for certain research questions.
==Assumptions==
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