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Parsimony in model selection: Tools for assessing fit propensity.

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Area of Science:

  • Psychological modeling
  • Statistical theory
  • Quantitative psychology

Background:

  • Theories are often tested as statistical models, with model selection focusing on data fit.
  • Current methods for model comparison may overemphasize fit statistics, neglecting model parsimony.
  • Parsimony (Occam's Razor) is crucial but often simplified to parameter count, overlooking 'fit propensity'.

Purpose of the Study:

  • To introduce a novel toolkit for assessing parsimony in structural equation models.
  • To address the limitations of existing model selection criteria in capturing 'fit propensity'.
  • To provide researchers with methods to better understand and evaluate the parsimony of their statistical models.

Main Methods:

  • Development of an R package, 'ockhamSEM', built upon the 'lavaan' package.
  • Implementation of methods to quantify and analyze the 'fit propensity' of structural equation models.
  • Application of the toolkit to investigate the factor structure of the Rosenberg Self-Esteem Scale.

Main Results:

  • The 'ockhamSEM' package provides a practical tool for evaluating model parsimony beyond parameter counts.
  • Fit propensity analysis reveals nuances in model evaluation not captured by standard fit indices.
  • The study demonstrates the utility of fit propensity in assessing the theoretical implications of structural equation models.

Conclusions:

  • Evaluating 'fit propensity' is essential for a more rigorous assessment of theoretical models.
  • The 'ockhamSEM' toolkit enhances the application of parsimony principles in structural equation modeling.
  • This approach offers a more robust framework for theory testing and model selection in quantitative research.