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A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments
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Bayesian Evaluation of inequality-constrained Hypotheses in SEM Models using Mplus.

Rens van de Schoot1, Herbert Hoijtink2, Michael N Hallquist3

  • 1Department of Methods and Statistics, Utrecht University, The Netherlands Optentia research program, faculty of Humanities, North-West University, South Africa.

Structural Equation Modeling : a Multidisciplinary Journal
|December 24, 2013
PubMed
Summary
This summary is machine-generated.

This study presents a Bayesian approach for testing informative hypotheses in structural equation models (SEM). It allows researchers to compare inequality-constrained hypotheses with their complements, aiding behavioral and social science research.

Keywords:
Bayes factorInformative HypothesisMplusorder restricted inferencestructural equation modeling

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Area of Science:

  • Behavioral and social sciences
  • Statistical modeling

Background:

  • Researchers often have directional expectations about parameters in structural equation models (SEM).
  • Testing these informative hypotheses requires specialized statistical approaches.

Purpose of the Study:

  • To introduce and demonstrate a Bayesian approach for comparing inequality-constrained hypotheses against their complements within SEM.
  • To provide a practical method for researchers to test specific directional predictions.

Main Methods:

  • A Bayesian framework is employed to compare an informative hypothesis (inequality constraints) with its complement.
  • The utility of the method is illustrated with a practical example.
  • The influence of prior distribution specification on the results is examined.

Main Results:

  • The proposed Bayesian approach effectively compares inequality-constrained hypotheses in SEM.
  • The method's implementation is demonstrated using Mplus software.
  • Prior distribution choices can impact the comparison outcomes.

Conclusions:

  • The Bayesian approach offers a robust method for testing informative hypotheses in SEM.
  • This facilitates more nuanced hypothesis testing in behavioral and social sciences.
  • The approach is accessible for implementation via Mplus.