A Monte Carlo Study was conducted to evaluate six models commonly used to evaluate change. The results revealed specific problems with each. Analysis of covariance and analysis of variance of residualized gain scores appeared to substantially and consistently overestimate the change effects. Multiple factor analysis of variance models utilizing pretest and post-test scores yielded invalidly low F ratios. The analysis of variance of difference scores and the multiple factor analysis of variance using repeated measures were the only models which adequately controlled for pre-treatment differences; however, they appeared to be robust only when the error level is 50% or more. This places serious doubt regarding published findings, and theories based upon change score analyses. When collecting data which have an error level less than 50% (which is true in most situations), a change score analysis is entirely inadvisable until an alternative procedure is developed. (Author/CTM)