This chapter gives a brief introduction to mixed models analyses in the context of repeated-measures data analyses. Repeated-measures data can take multiple forms. Two of the more common types of repeated-measures data are repeated-measures within a participant at a single time point; and repeated-measures within a participant across time in a longitudinal design. The chapter focuses on three specific benefits to the use of mixed models: the ability to correctly handle nested data, the ability to include participants with missing data, and the ability to specify and model multiple random effects for analyses. It considers the multilevel application of repeated measures as an extension of the more well known repeated measures Analysis of Covariance (ANCOVA). The illustration used in the chapter demonstrates the flexibility in modeling and the unique type of data achieved when treating time as a random effect.