Date of Award
Fall 2021
Document Type
Open Access Dissertation
Department
Psychology
First Advisor
Christine DiStefano
Abstract
This dissertation informed researchers about the performance of different levelspecific and target-specific model fit indices in Multilevel Latent Growth Model (MLGM) using unbalanced design and different trajectories. As the use of MLGMs is a relatively new field, this study helped further the field by informing researchers interested in using various specific model fit indices.
We evaluated the descriptive information of the various model fit indices under various simulation conditions and the extent to which the fit indices could be influenced by different design factors, based on simulated data with different conditions derived from a correctly specified MLGM. Our simulation design factors included three levels of number of groups (50, 100, and 200), three levels of unbalanced GS (5/15, 10/20, and 25/75), and three trajectories (accelerating, decelerating, and linear).
Based on the results, we made recommendations for practical and theoretical research about fit indices. CFI- and TFI-related fit indices performed well in the MLGM could be trustworthy to use to evaluate model fit under similar conditions found in applied settings. However, RMSEA-related fit indices, SRMR-related fit indices, and chisquare-related fit indices varied by the factors included in this study and should be used with caution for evaluating model fit in the MLGM. The use of these fit indices appears to be particularly problematic when dealing with unbalanced design and small sample sizes.
Rights
© 2021, Fan Pan
Recommended Citation
Pan, F.(2021). Evaluating Fit Indices in a Multilevel Latent Growth Model With Unbalanced Design: A Monte Carlo Study. (Doctoral dissertation). Retrieved from https://scholarcommons.sc.edu/etd/6794