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Area of Science:

  • Psychometrics
  • Personality Psychology
  • Quantitative Psychology

Background:

  • Generalizability theory (G theory) is crucial for assessing score reliability.
  • Structural equation modeling (SEM) offers flexible frameworks for complex G theory designs.
  • The Big Five Inventory (BFI-2) provides a robust measure of personality, including multifaceted subdomains.

Purpose of the Study:

  • To demonstrate SEM applications for G theory-based univariate, multivariate, and bifactor models.
  • To analyze generalizability of multi-occasion BFI-2 data using these SEM designs.
  • To evaluate the utility of different G theory models for understanding personality score reliability.

Main Methods:

  • Utilized structural equation models (SEM) to implement G theory designs.
  • Analyzed multi-occasion data from the expanded Big Five Inventory (BFI-2).
  • Employed univariate, multivariate, and bifactor modeling approaches within SEM.

Main Results:

  • SEM effectively represents G theory designs for personality assessment.
  • Multivariate and bifactor models provide improved generalizability indices for composite scores.
  • Bifactor models enable detailed partitioning of score variance and evaluation of subscale contributions.

Conclusions:

  • Accounting for item and occasion effects is vital for accurate generalizability.
  • Multivariate and bifactor SEM designs offer advanced insights into personality score reliability and dimensionality.
  • The study provides practical guidelines and R code for implementing these advanced G theory analyses.