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Collapsing sparse response categories in Likert scales impacts model fit indices. This study recommends specific fit indices like RMSEA and TLI for analyzing data with response category collapsing.

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Area of Science:

  • Psychometrics
  • Quantitative Psychology
  • Statistical Modeling

Background:

  • Sparse data in item response categories is common in applied research.
  • Collapsing response categories can address data sparsity but its methodological impact is unclear.

Purpose of the Study:

  • To investigate the effects of collapsing response categories on model fit indices within a confirmatory factor analysis framework.
  • To assess the impact of category collapsing on common fit indices under varying sparsity conditions.

Main Methods:

  • A simulation study was conducted using confirmatory factor analysis models.
  • Response categories with low endorsement frequencies were systematically collapsed.
  • The impact on chi-square, CFI, TLI, RMSEA, and SRMR was evaluated.

Main Results:

  • Category collapsing significantly affects model fit indices, especially under severe sparsity.
  • Collapsing reduces model rejections for both correctly and incorrectly specified models.
  • RMSEA and TLI demonstrated differential sensitivity to category collapsing, with specific recommendations provided.

Conclusions:

  • Category collapsing is a consequential data handling technique in psychometric analysis.
  • Researchers should be mindful of its impact on model evaluation and choose appropriate fit indices.
  • The study offers guidance for managing sparse data in Likert-type scale analyses.