Geiser (Multitrait-multimethod-multioccasion modeling, 2009) recently presented the Correlated State-Correlated (Methods-Minus-1) [CS-C(M−1)] model for analysing longitudinal multitrait-multimethod (MTMM) data. In the present article, the authors discuss the extension of the CS-C(M−1) model to a model that includes latent difference variables, called CS-C(M−1) change model. The CS-C(M−1) change model allows investigators to study inter-individual differences in intra-individual change over time, to separate true change from random measurement error, and to analyse change simultaneously for different methods. Change in a reference method can be contrasted with change in other methods to analyse convergent validity of change.