Investigating Factorial Invariance of Latent Variables Across Populations When Manifest Variables Are Missing Completely

Keith F. Widaman, Kevin J. Grimm, Dawnté R. Early, Richard W. Robins, Rand D. Conger

Research output: Contribution to journalArticle

11 Scopus citations

Abstract

Difficulties arise in multiple-group evaluations of factorial invariance if particular manifest variables are missing completely in certain groups. Ad hoc analytic alternatives can be used in such situations (e.g., deleting manifest variables), but some common approaches, such as multiple imputation, are not viable. At least 3 solutions to this problem are viable: analyzing differing sets of variables across groups, using pattern mixture approaches, and a new method using random number generation. The latter solution, proposed in this article, is to generate pseudo-random normal deviates for all observations for manifest variables that are missing completely in a given sample and then to specify multiple-group models in a way that respects the random nature of these values. An empirical example is presented in detail comparing the 3 approaches. The proposed solution can enable quantitative comparisons at the latent variable level between groups using programs that require the same number of manifest variables in each group.

Original languageEnglish (US)
Pages (from-to)384-408
Number of pages25
JournalStructural Equation Modeling
Volume20
Issue number3
DOIs
StatePublished - Jul 2013

Keywords

  • confirmatory factor analysis
  • factorial invariance
  • missing data
  • structural equation modeling

ASJC Scopus subject areas

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

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