This chapter describes the statistical methodologies that are used to answer the scientific questions posed. It focuses on the analysis of longitudinal count and binary data and examines two modeling strategies, the first being a population-average approach and the second are known as subject-specific approach. Longitudinal and repeated measures data are very frequent in almost all scientific fields, including agriculture, biology, medicine, epidemiology, geography, and demography. In the analysis of repeated measures or longitudinal data, the critical feature to recognize is that, since sets of measures are obtained from the same subjects, these measures are likely to be correlated and can rarely be considered as independent. Missing data is quite common for longitudinal and repeated measures data. One is to compare models based on measures of fit that are adjusted for the number of covariance parameters. The chapter illustrates techniques on repeated measures nonnormally distributed outcome variables.