Evaluation of Model Fit in Latent Growth Model with Missing Data, Non-normality and Small Sample

Abstract

Evaluating latent growth models of psychological data that is collected repeatedly is challenging because of small samples, non-normal and missing data. These conditions increase the likelihood of non-convergence, improper solutions, inflated Type 1 error rates, low statistical power and biased parameter estimates and standard errors. Various methods have been developed to handle non-normality and missing data but there has been less development in methods to handle small samples. In this thesis, 2 approaches to handle small samples ? 1) corrections to test statistics and 2) increasing the number of timepoints ? were investigated in simulation studies under a variety of sample sizes, non-normality and missing data. Type 1 error rates and statistical power of the corrections were comparable to the uncorrected test statistics under a wide range of conditions and were only superior when sample sizes are relatively large, data are normal and when the number of timepoints is large. Increasing number of timepoints also reduces the improper solutions and biased parameter estimates.