Please use this identifier to cite or link to this item:
Title: Evaluation of Model Fit in Latent Growth Model with Missing Data, Non-normality and Small Sample
Keywords: quantitative psychology, latent growth models, small samples, non-normality, missing data, model evaluation
Issue Date: 30-Dec-2013
Citation: LIM YONGHAO (2013-12-30). Evaluation of Model Fit in Latent Growth Model with Missing Data, Non-normality and Small Sample. ScholarBank@NUS Repository.
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.
Appears in Collections:Master's Theses (Open)

Show full item record
Files in This Item:
File Description SizeFormatAccess SettingsVersion 
LimYH.pdf1.09 MBAdobe PDF



Page view(s)

checked on May 18, 2019


checked on May 18, 2019

Google ScholarTM


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.