This article examines 4 approaches for explaining shared method variance, each applied to a longitudinal trait-state-occasion (TSO) model. Many approaches have been developed to account for shared method variance in multitrait-multimethod (MTMM) data. Some of these MTMM approaches (correlated method, orthogonal method, correlated method minus one, correlated uniqueness) were therefore borrowed in these analyses such that their effectiveness could be evaluated in conjunction with a TSO model. To this end, datasets were generated according to 4 different covariance matrices (each created according to specifications of a model built with 1 of the 4 approaches) and each model was crossed with each type of data. Whereas the correlated method and correlated method minus one approaches encountered many difficulties in convergence, fit, or parameter estimates, the correlated uniqueness and orthogonal method approaches proved to be quite versatile. (Contains 5 figures, 3 tables, and 5 footnotes.)