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Model Fit and Item Factor Analysis: Overfactoring, Underfactoring, and a Program to Guide Interpretation.

D Angus Clark1, Ryan P Bowles1

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|April 24, 2018
PubMed
Summary
This summary is machine-generated.

Model fit statistics commonly used in exploratory item factor analysis (IFA) may be unreliable for determining the number of dimensions in assessments with dichotomous items. Their effectiveness depends heavily on various model characteristics, questioning their general utility.

Keywords:
Categorical latent variable modelingexploratory factor analysisitem factor analysismodel fit

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Area of Science:

  • Psychometrics
  • Statistical Modeling

Background:

  • Researchers often use model fit statistics and thresholds to determine dimensionality in exploratory item factor analysis (IFA).
  • However, these indices were developed for confirmatory analysis with continuous data, not categorical data typical in IFA.

Purpose of the Study:

  • To investigate the reliability of common model fit statistics and their thresholds in identifying the correct number of latent dimensions for dichotomous items in IFA.
  • To assess how factors like loading magnitude, intercorrelation, indicator count, and model complexity influence the effectiveness of these statistics.

Main Methods:

  • Utilized Monte Carlo simulation methods to generate data from three-factor structures with varying parameters.
  • Fit various models to the generated data and evaluated the performance of popular fit statistics: chi-square, root mean square error of approximation (RMSEA), comparative fit index (CFI), and Tucker-Lewis index (TLI).

Main Results:

  • The effectiveness of standard fit statistic thresholds varied significantly across different statistics.
  • Performance was highly conditional on numerous features of the underlying data-generating model, including factor loading and intercorrelation magnitudes, and the presence of cross-loadings or minor factors.
  • No single statistic consistently and accurately identified the optimal dimensionality across all simulated conditions.

Conclusions:

  • Conventional model fit thresholds provide questionable utility for determining dimensionality in exploratory item factor analysis with dichotomous indicators.
  • Researchers should exercise caution when relying solely on these statistics and thresholds for dimensional assessment in IFA contexts.
  • Further research may be needed to develop more appropriate methods for assessing dimensionality with categorical data in IFA.