Correcting Model Fit Criteria for Small Sample Latent Growth Models With Incomplete Data. (December 2017)

• Record Type: Journal Article
• Authors: McNeish, Daniel; Harring, Jeffrey R.
• Is Part Of: Educational and psychological measurement. Volume 77:Number 6(2017:Dec.)
• Page Start: 990
• Page End: 1018
• Publication Date: 2017-12
• Subjects: latent growth model -- small sample -- missing data -- dropout -- full information maximum likelihood -- FIML -- correction; Educational tests and measurements -- Periodicals; Psychological tests -- Periodicals; 151.205
• DOI: 10.1177/0013164416661824
• Languages: English
• ISSNs: 0013-1644

Abstract

To date, small sample problems with latent growth models (LGMs) have not received the amount of attention in the literature as related mixed-effect models (MEMs). Although many models can be interchangeably framed as a LGM or a MEM, LGMs uniquely provide criteria to assess global data–model fit. However, previous studies have demonstrated poor small sample performance of these global data–model fit criteria and three post hoc small sample corrections have been proposed and shown to perform well with complete data. However, these corrections use sample size in their computation—whose value is unclear when missing data are accommodated with full information maximum likelihood, as is common with LGMs. A simulation is provided to demonstrate the inadequacy of these small sample corrections in the near ubiquitous situation in growth modeling where data are incomplete. Then, a missing data correction for the small sample correction equations is proposed and shown through a simulation study to perform well in various conditions found in practice. An applied developmental psychology example is then provided to demonstrate how disregarding missing data in small sample correction equations can greatly affect assessment of global data–model fit.