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On Effect Size Measures for Nested Measurement Models.

Tenko Raykov1, Christine DiStefano2, Lisa Calvocoressi3

  • 1Michigan State University, East Lansing, USA.

Educational and Psychological Measurement
|November 3, 2022
PubMed
Summary
This summary is machine-generated.

New effect size indices quantify differences between nested confirmatory factor analysis models. These measures assess the impact of parameter restrictions, independent of sample size, and are easily estimated.

Keywords:
confidence intervalconfirmatory factor analysiseffect sizenested modelparameter restrictionproportion explained variance

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Area of Science:

  • Psychometrics
  • Statistical Modeling

Background:

  • Confirmatory Factor Analysis (CFA) is widely used to model relationships between observed variables and latent constructs.
  • Evaluating the fit of nested CFA models is crucial for model comparison and refinement.
  • Existing methods for comparing nested models often rely on statistical significance, which can be influenced by sample size.

Purpose of the Study:

  • To introduce a novel class of effect size indices for comparing nested confirmatory factor analysis models.
  • To provide descriptive measures that quantify the impact of parameter restrictions on model fit.
  • To develop indices that are independent of sample size and statistical significance.

Main Methods:

  • The study discusses effect size indices derived from changes in explained variance proportions in manifest variables.
  • These indices are based on linear combinations of explained variance.
  • The methods allow for point and interval estimation using standard statistical software.

Main Results:

  • The proposed effect size indices quantify the degree of difference between nested CFA models.
  • These measures effectively capture the impact of parameter constraints on model fit.
  • The indices are demonstrated to be robust and interpretable, irrespective of statistical significance.

Conclusions:

  • The developed effect size indices offer a valuable tool for assessing model differences in confirmatory factor analysis.
  • These measures provide sample-size-independent quantification of the impact of parameter restrictions.
  • The indices enhance the interpretability and practical application of model comparisons in psychometric research.