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Repeated measures data are widely used in social and behavioral sciences, e.g., to investigate the trajectory of an underlying phenomenon over time. A variety of different mixed-effects models, a type of statistical modeling approach for repeated measures data, have been proposed and they differ mainly in two aspects: (1) the distributional assumption of the dependent variable and (2) the linearity of the model. Distinct combinations of these characteristics encompass a variety of modeling techniques. Although these models have been independently discussed in the literature, the most flexible framework – the generalized nonlinear mixed-effects model (GNLMEM) – can be used as a modeling umbrella to encompass these modeling options for repeated measures data. Therefore, the aim of this paper is to explicate on the different mixed-effects modeling techniques guided by the distributional assumption and model linearity choices using the GNLMEM as a general framework. Additionally, empirical examples are used to illustrate the versatility of this framework.

A primer on distributional assumptions and model linearity in repeated measures data analysis