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Advantages in Bayesian Approaches to Confirmatory Factor Analysis.

Diana Alvarez-Bartolo1, Cheuk Hei Cheng2,3, Roy Levy2,4

  • 1School of Nursing, Johns Hopkins University, Baltimore, MD, USA. dalvar16@jh.edu.

Prevention Science : the Official Journal of the Society for Prevention Research
|June 4, 2026
PubMed
Summary
This summary is machine-generated.

Bayesian methods offer advantages for prevention researchers evaluating interventions. These methods improve interpretation, handle uncertainty, avoid estimation issues, and allow flexible measurement invariance testing compared to frequentist approaches.

Keywords:
BayesFactor analysisPsychometricsStatistics

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Area of Science:

  • Psychometrics
  • Prevention Science
  • Statistical Modeling

Background:

  • Rigorous evaluation of preventive interventions requires establishing psychometric quality of measures.
  • Confirmatory Factor Analysis (CFA) is commonly used but faces challenges within the frequentist framework, such as sample size and measurement invariance (MI) requirements.
  • Bayesian methods offer potential advantages but are underutilized in prevention science.

Purpose of the Study:

  • To illustrate the advantages of Bayesian methods over frequentist approaches for Confirmatory Factor Analysis (CFA) in prevention research.
  • To demonstrate how Bayesian methods enhance result interpretation, uncertainty expression, and estimation.
  • To showcase flexible approaches to measurement invariance (MI) and the incorporation of prior information using Bayesian techniques.

Main Methods:

  • Application of Bayesian methods to Confirmatory Factor Analysis (CFA) using data from the Alabama Parenting Questionnaire (APQ).
  • Five illustrative examples demonstrating specific advantages of Bayesian approaches.
  • Comparison of Bayesian results with traditional frequentist (maximum likelihood) methods.

Main Results:

  • Bayesian methods provide clearer interpretation of results and better expression of uncertainty compared to frequentist approaches.
  • Bayesian analysis helps circumvent common estimation problems encountered in frequentist CFA.
  • Flexible examination of parameter measurement invariance (MI) is facilitated, alongside the integration of prior substantive information.

Conclusions:

  • Bayesian methods offer significant advantages for prevention scientists conducting psychometric evaluations and other statistical analyses.
  • Researchers are encouraged to consider adopting Bayesian methods for CFA to overcome limitations of frequentist approaches.
  • The illustrated examples highlight the practical benefits of Bayesian analysis for applied research in intervention science.