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On the explaining-away phenomenon in multivariate latent variable models.

Peter van Rijn1, Frank Rijmen

  • 1ETS Global, Amsterdam, The Netherlands.

The British Journal of Mathematical and Statistical Psychology
|December 4, 2014
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Summary
This summary is machine-generated.

The explaining-away phenomenon in latent variable models, common in psychological and educational research, can create unexpected dependencies between unobserved factors when they influence the same data. This study explores its impact on various models.

Keywords:
Explaining-away phenomenonconditional dependencefactor analysisitem response theorylatent variables

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Area of Science:

  • Psychometrics
  • Statistical Modeling
  • Educational Measurement

Background:

  • Probabilistic models in psychology and education frequently utilize latent variables.
  • Examples include factor analysis, item response theory, and latent class models.

Purpose of the Study:

  • To discuss the 'explaining-away' phenomenon within latent variable models.
  • To illustrate its implications for understanding conditional dependencies between latent variables.

Main Methods:

  • Theoretical discussion of the explaining-away phenomenon.
  • Empirical analysis using both simulated and real-world data examples.
  • Examination across various established latent variable models.

Main Results:

  • The explaining-away phenomenon can induce counterintuitive conditional dependencies among latent variables.
  • These effects are observable when multiple latent variables relate to the same observed variable.
  • The study demonstrates these implications across diverse latent variable frameworks.

Conclusions:

  • Understanding the explaining-away phenomenon is crucial for accurate interpretation of latent variable models.
  • It highlights the complex interplay between unobserved constructs and observed data.
  • The findings have implications for psychological and educational measurement practices.