Model Fit Indices for Random Effects Models: Translating Model Fit From Latent Growth Curve Models

• In Event: Advances in Multilevel Modeling

Abstract

Latent growth curve (LGC) and random effects models (REMs) are analytically and empirically equivalent for intrinsically linear models and used interchangeably for intrinsically nonlinear models. However, while LGC provides overall model fit indices, REM does not. Overall model fit indices are useful because they evaluate how well a specified model fits data. This paper proposes to translate model fit concepts from LGC to REM to help researchers compute overall model fit indices, including the model chi-square (χ^2), comparative fit index (CFI), root mean squared error of approximation (RMSEA), and standardized root mean squared residual (SRMR). Three empirical examples with different underlying growth processes (intrinsically linear and nonlinear) are used as illustrations to show the computation of these indices for REMs.

Authors

• Ziwei Zhang, University of Minnesota
  Presenting Author

• Corissa Taylor Rohloff, University of Minnesota
  Non-Presenting Author

• Nidhi Kohli, University of Minnesota
  Non-Presenting Author