Estimation of Latent Variable Scores with Multiple Group Item Response Models: Implications for Integrative Data Analysis

Pega Davoudzadeh, Kevin J. Grimm, Keith F. Widaman, Sarah L. Desmarais, Stephen Tueller, Danielle Rodgers, Richard A. Van Dorn

Research output: Contribution to journalArticle


Integrative data analysis (IDA) involves obtaining multiple datasets, scaling the data to a common metric, and jointly analyzing the data. The first step in IDA is to scale the multisample item-level data to a common metric, which is often done with multiple group item response models (MGM). With invariance constraints tested and imposed, the estimated latent variable scores from the MGM serve as an observed variable in subsequent analyses. This approach was used with empirical multiple group data and different latent variable estimates were obtained for individuals with the same response pattern from different studies. A Monte Carlo simulation study was then conducted to compare the accuracy of latent variable estimates from the MGM, a single-group item response model, and an MGM where group differences were ignored. Results suggest that these alternative approaches led to consistent and equally accurate latent variable estimates. Implications for IDA are discussed.

Original languageEnglish (US)
JournalStructural Equation Modeling
StateAccepted/In press - Jan 1 2020



  • Data integration
  • item response model
  • latent variable estimation
  • multi-sample
  • scaling

ASJC Scopus subject areas

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

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