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Although common in the educational and developmental areas, multilevel models are not often utilized in the analysis of data from experimental designs. This article illustrates how multilevel models can be useful with two examples from experimental designs with repeated measurements not involving time. One example demonstrates how to properly examine independent variables for experimental stimuli or individuals that are categorical, continuous, or semicontinuous in the presence of missing data. The second example demonstrates how response times and error rates can be modeled simultaneously within a multivariate model in order to examine speed–accuracy trade-offs at the experimental-condition and individual levels, as well as to examine differences in the magnitude of effects across outcomes. SPSS and SAS syntax for the examples are available electronically.