Testing for factor loading differences in mixture simultaneous factor analysis: A Monte Carlo simulation-based perspective

Elena Geminiani*, Eva Ceulemans, Kim De Roover

*Corresponding author for this work

Research output: Contribution to journalArticleScientificpeer-review

Abstract

Factor analysis is ubiquitously applied in behavioral sciences for capturing covariances of observed variables by latent variables (factors). When factor-analyzing data from many groups of subjects, mixture simultaneous factor analysis (MSFA) determines which groups have the same factor model by clustering them based on their factor loadings, factor (co)variances and residual variances. Two Monte Carlo simulations are performed to investigate the power and type I error of Wald tests for factor loading differences in MSFA, as affected by characteristics of the data (sample size in terms of number and size of groups), factor models (item communality levels, sizes and types of loading differences) and clustering (cluster size, classification error and uncertainty). The results were better in case of equal cluster sizes, strongly overdetermined factors, high communalities, and larger primary loading differences.

Original languageEnglish
Pages (from-to)391-409
JournalStructural Equation Modeling
Volume28
Issue number3
DOIs
Publication statusPublished - 2021

Keywords

  • Factor analysis
  • hypothesis testing
  • mixture clustering
  • multiple testing
  • MEASUREMENT INVARIANCE
  • SAMPLE-SIZE
  • UNIQUENESS CONSTRAINTS
  • LATENT-VARIABLES
  • LIKELIHOOD RATIO
  • MODEL
  • PERSONALITY
  • POWER
  • COMMUNALITY
  • NUMBER

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