An increasing number of psychological studies are devoted to the analysis of g-factor structures. One key purpose of applying g-factor models is to identify predictors or potential causes of the general and specific effects. Typically, researchers relate predictor variables directly to the general and specific factors using a classical mimic approach. However, this procedure bears some methodological challenges, which often lead to model misspecification and biased parameter estimates. We propose 2 possible modeling strategies to circumvent these problems: the multiconstruct bifactor and the residual approach. We illustrate both modeling approaches for the application of g-factor models to longitudinal and multitrait-multimethod data. Practical guidelines are provided for choosing an appropriate method in empirical applications, and the implications of this investigation for multimethod and longitudinal research are discussed.