Incorporating measurement nonequivalence in a cross-study latent growth curve analysis

David B. Flora, Patrick J. Curran, Andrea M. Hussong, Michael C. Edwards

Research output: Contribution to journalArticlepeer-review

33 Scopus citations


A large literature emphasizes the importance of testing for measurement equivalence in scales that may be used as observed variables in structural equation modeling applications. When the same construct is measured across more than one developmental period, as in a longitudinal study, it can be especially critical to establish measurement equivalence, or invariance, across the developmental periods. Similarly, when data from more than one study are combined into a single analysis, it is again important to assess measurement equivalence across the data sources. Yet, how to incorporate nonequivalence when it is discovered is not well described for applied researchers. Here, we present an item response theory approach that can be used to create scale scores from measures while explicitly accounting for nonequivalence. We demonstrate these methods in the context of a latent curve analysis in which data from two separate studies are combined to estimate a single longitudinal model spanning several developmental periods.

Original languageEnglish (US)
Pages (from-to)676-704
Number of pages29
JournalStructural Equation Modeling
Issue number4
StatePublished - Oct 2008
Externally publishedYes

ASJC Scopus subject areas

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


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