The question as to which structural equation model should be selected when multitrait-multimethod (MTMM) data are analyzed is of interest to many researchers. In the past, attempts to find a well-fitting model have often been data-driven and highly arbitrary. In the present article, the authors argue that the measurement design (type of methods used) should guide the choice of the statistical model to analyze the data. In this respect, the authors distinguish between (a) interchangeable methods, (b) structurally different methods, and (c) the combination of both kinds of methods. The authors present an appropriate model for each type of method. All models allow separating measurement error from trait influences and trait-specific method effects. With respect to "interchangeable methods," a multilevel confirmatory factor model is presented. For "structurally different methods," the correlated trait-correlated (method-1) model is recommended. Finally, the authors demonstrate how to appropriately analyze data from MTMM designs that simultaneously use "interchangeable" and "structurally different methods." All models are applied to empirical data to illustrate their proper use. Some implications and guidelines for modeling MTMM data are discussed. (Contains 5 figures, 7 tables, and 2 footnotes.)