In research concerning model invariance across populations, researchers have discussed the limitations of the conventional chi-square difference test ([Delta] chi-square test). There have been some research efforts in using goodness-of-fit indexes (i.e., [Delta]goodness-of-fit indexes) for assessing multisample model invariance, and some specific recommendations have been made (Cheung & Rensvold, 2002). Because [Delta]goodness-of-fit indexes were designed to assess model fit in terms of covariance structure, it is not clear how they will perform when mean structure invariance is the research focus. This study extends the previous work (Cheung & Rensvold, 2002), and evaluates how [Delta]goodness-of-fit indexes perform in mean structure invariance analysis. By using a Monte Carlo simulation experiment, the performance of [Delta]goodness-of-fit indexes in detecting population mean structure difference is evaluated. The findings suggest that, in general, [Delta]goodness-of-fit indexes are so sensitive to model size that they are not generally useful in mean structure invariance analysis. (Contains 4 figures, 5 tables, and 1 footnote.)