Correcting Model Fit Criteria for Small Sample Latent Growth Models With Incomplete Data

Daniel McNeish, Jeffrey R. Harring

Research output: Contribution to journalArticlepeer-review

21 Scopus citations


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.

Original languageEnglish (US)
Pages (from-to)990-1018
Number of pages29
JournalEducational and Psychological Measurement
Issue number6
StatePublished - Dec 1 2017
Externally publishedYes


  • FIML
  • correction
  • dropout
  • full information maximum likelihood
  • latent growth model
  • missing data
  • small sample

ASJC Scopus subject areas

  • Education
  • Developmental and Educational Psychology
  • Applied Psychology
  • Applied Mathematics


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