Longitudinal data analysis assumes that scales meet the assumption of longitudinal measurement invariance (i.e., that scales function equivalently across measurement occasions). This simulation study examines the impact of violations to the assumption of longitudinal measurement invariance on growth models and whether modeling the invariance violations improves the outcomes of interest. The four conditions were varied in the study: percent of non-invariant items, magnitude of invariance violation, type of invariance violation, and test length. Six latent growth models (first- and second-order) were estimated to examine the impact of invariance violations under varying degrees of model misspecification. The results suggest that the proportion of non-invariant items and the size of intercept invariance violations have the most significant impact on results. In addition, modeling the partial measurement invariance did not improve growth model parameter recovery. Ultimately, researchers should use extreme caution when estimating growth models when measurement invariance violations are present as it may lead to spurious conclusions about change over time. [The dissertation citations contained here are published with the permission of ProQuest LLC. Further reproduction is prohibited without permission. Copies of dissertations may be obtained by Telephone (800) 1-800-521-0600. Web page: http://www.proquest.com.bibliotheek.ehb.be/en-US/products/dissertations/individuals.shtml.]