The Journal of Experimental Education, 2011, 79, 361–381 Copyright C  Taylor & Francis Group, LLC ISSN: 0022-0973 print /1940-0683 online DOI: 10.1080/00220973.2010.509369 MEASUREMENT, STATISTICS, AND RESEARCH DESIGN Detecting Growth Shape Misspecifications in Latent Growth Models: An Evaluation of Fit Indexes Walter L. Leite University of Florida Laura M. Stapleton University of Maryland, Baltimore County In this study, the authors compared the likelihood ratio test and fit indexes for detection of misspecifications of growth shape in latent growth models through a simulation study and a graphical analysis. They found that the likelihood ratio test, MFI, and root mean square error of approximation performed best for detecting model misspecification when a linear model was fit to scores presenting nonlinear growth trajectories, in terms of being sensitive to severity of misspecification, and providing stable results with different types of nonlinearity and sample sizes. Keywords: latent growth models, longitudinal analysis, misspecification of growth shape, model selection, Monte Carlo simulation, nonlinear growth trajectory, sensitivity of fit indexes LATENT GROWTH MODELS HAVE been increasingly popular methods used to analyze longitudinal data. With this model, a researcher is able to hypothesize the trajectory of growth (e.g., linear, quadratic) of the outcome being studied and Address correspondence to Walter L. Leite, School of Human Development and Organizational Studies in Education, University of Florida, 1215 Norman Hall, Gainesville, FL 32611, USA. E-mail: walter.leite@coe.ufl.edu