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Dynamic fit index cutoffs for one-factor models.

Daniel McNeish1, Melissa G Wolf2

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Traditional fit index cutoffs poorly assess one-factor models. A new dynamic fit cutoff approach offers a more accurate method for evaluating model fit in behavioral research.

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Area of Science:

  • Psychometrics
  • Behavioral Research Methodology

Background:

  • Assessing one-factor models is crucial in behavioral research.
  • Traditional fit indices (RMSEA, CFI, SRMR) with established cutoffs are commonly used.
  • The appropriateness of these cutoffs for one-factor models is uncertain as they were derived for multifactor models.

Purpose of the Study:

  • To evaluate the sensitivity of traditional fit index cutoffs to misspecifications in one-factor models.
  • To investigate the performance of the dynamic fit cutoff approach as an alternative for assessing one-factor model fit.

Main Methods:

  • A simulation study was conducted to test traditional and dynamic fit index cutoffs.
  • The study examined the sensitivity of these cutoffs to common misspecifications in one-factor models.

Main Results:

  • Traditional fit index cutoffs demonstrated very poor sensitivity to misspecification in one-factor models.
  • The dynamic fit cutoff approach showed excellent accuracy and stability in classifying correct or misspecified one-factor models.

Conclusions:

  • Traditional fit index cutoffs are unreliable for assessing one-factor models.
  • Dynamic fit index cutoffs present a promising and more accurate alternative for evaluating model fit in one-factor contexts.