Detecting Misspecification in Mean Structures for Growth Curve Models: Performance of Pseudo R 2s and Concordance Correlation Coefficients

Wei Wu, Stephen West

Research output: Contribution to journalArticle

3 Scopus citations

Abstract

This study examined the performance of 4 correlation-based fit indexes (marginal and conditional pseudo R 2s; average and conditional concordance correlations) in detecting misspecification in mean structures in growth curve models. Their performance was also compared to that of 4 traditional SEM fit indexes. We found that the marginal pseudo R 2 and average concordance correlation were able to detect misspecification in the marginal mean structure (average change trajectory). The conditional pseudo R 2 and concordance correlation could detect misspecification when it occurred in the conditional mean structure (individual change trajectory) or in both mean structures. Compared to the SEM fit indexes, the correlation-based fit indexes were more robust to sample size but were less robust to data properties such as magnitude of population mean and measurement error. Theoretical and practical implications of the results and directions for future research are discussed.

Original languageEnglish (US)
Pages (from-to)455-478
Number of pages24
JournalStructural Equation Modeling
Volume20
Issue number3
DOIs
StatePublished - Jul 1 2013

Keywords

  • concordance correlation
  • growth curve model
  • mean structure
  • model fit
  • pseudo R square

ASJC Scopus subject areas

  • Decision Sciences(all)
  • Modeling and Simulation
  • Sociology and Political Science
  • Economics, Econometrics and Finance(all)

Fingerprint Dive into the research topics of 'Detecting Misspecification in Mean Structures for Growth Curve Models: Performance of Pseudo R <sup>2</sup>s and Concordance Correlation Coefficients'. Together they form a unique fingerprint.

  • Cite this