We present a 3-step approach to defining latent growth components. In the first step, a measurement model with at least 2 indicators for each time point is formulated to identify measurement error variances and obtain latent variables that are purged from measurement error. In the second step, we use contrast matrices to define the latent growth components representing the constructs of substantive interest. The corresponding matrix of structural coefficients is then computed by inverting the contrast matrix. In the third and last step, the first 2 steps are integrated into a structural equation model. The particular strength of this approach is that it permits construction of latent growth components in such a way that they represent interesting contrasts from a substantive point of view. This is illustrated using data of cancer patients obtained from 3 fatigue scales of the multidimensional fatigue inventory measured at 4 time points. (Contains 2 tables, 1 footnote and 5 figures.)