A solution to modeling multilevel confirmatory factor analysis with data obtained from complex survey sampling to avoid conflated parameter estimates

Jiun Yu Wu*, John J.H. Lin, Mei Wen Nian, Yi Cheng Hsiao

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

6 Citations (Scopus)

Abstract

The issue of equality in the between-and within-level structures in Multilevel Confirmatory Factor Analysis (MCFA) models has been influential for obtaining unbiased parameter estimates and statistical inferences. A commonly seen condition is the inequality of factor loadings under equal level-varying structures. With mathematical investigation and Monte Carlo simulation, this study compared the robustness of five statistical models including two model-based (a true and a mis-specified models), one design-based, and two maximum models (two models where the full rank of variance-covariance matrix is estimated in between level and within level, respectively) in analyzing complex survey measurement data with level-varying factor loadings. The empirical data of 120 3rd graders' (from 40 classrooms) perceived Harter competence scale were modeled using MCFA and the parameter estimates were used as true parameters to perform the Monte Carlo simulation study. Results showed maximum models was robust to unequal factor loadings while the design-based and the miss-specified model-based approaches produced conflated results and spurious statistical inferences. We recommend the use of maximum models if researchers have limited information about the pattern of factor loadings and measurement structures. Measurement models are key components of Structural Equation Modeling (SEM); therefore, the findings can be generalized to multilevel SEM and CFA models. Mplus codes are provided for maximum models and other analytical models.

Original languageEnglish
Article number1464
JournalFrontiers in Psychology
Volume8
Issue numberSEP
DOIs
Publication statusPublished - 2017 Sep 22
Externally publishedYes

Keywords

  • Complex survey sampling
  • Design-based approach
  • Level-varying factor loadings
  • Maximum model
  • Measurement
  • Model-based approach
  • Multilevel confirmatory factor analysis

ASJC Scopus subject areas

  • Psychology(all)

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