Comparison of different estimation methods in growth curve models for categorical data: A simulation study

This simulation study aims at investigating the performance of maximum likelihood and weighted least-square estimation approaches in growth curve models with categorical data. The goodness-of-fit indices were compared with a number of scenarios (different trajectories, sample sizes, replications, and number of categories). The results show that when the number of categories and replications are small, using weighted least-square estimating methods leads to better goodness-of-fit indices. However, when the number of categories and replications are large, both maximum likelihood and weighted least squares in estimating methods will result in similar fit indices.