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Dynamic fit index cutoffs for treating likert items as continuous.

Daniel McNeish1

  • 1Department of Psychology, Arizona State University.

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Traditional factor analysis guidelines may be inaccurate for Likert-type data. This study extends the dynamic fit index (DFI) to improve model fit assessment for Likert-type responses, ensuring reliable conclusions in psychological research.

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Area of Science:

  • Psychometrics
  • Statistical modeling
  • Quantitative psychology

Background:

  • Empirical factor analyses frequently use Likert-type responses, often treated as continuous data.
  • Traditional model fit index cutoffs were developed for continuous data, creating a methodological disconnect.
  • Existing guidelines may inaccurately assess model fit when applied to Likert-type responses.

Purpose of the Study:

  • To address the disconnect between traditional factor analysis guidelines and the common use of Likert-type responses.
  • To extend the dynamic fit index (DFI) framework to effectively accommodate Likert-type data characteristics.
  • To improve the sensitivity of model fit assessment for Likert-type responses in factor analysis.

Main Methods:

  • An illustrative simulation study was conducted to assess the impact of treating Likert-type responses as continuous.
  • The dynamic fit index (DFI) framework was extended to incorporate data characteristics like Likert scale points and response distributions.
  • Two simulation studies were performed using 5-point Likert-type responses to evaluate the extended DFI method.

Main Results:

  • Treating 5-point Likert responses as continuous can significantly reduce the sensitivity of traditional fit index cutoffs to misspecification.
  • The extended DFI method demonstrated improved performance compared to traditional cutoffs and DFI based on multivariate normality.
  • The proposed DFI extension consistently maintained over 90% sensitivity to misspecification with 5-point Likert-type responses.

Conclusions:

  • Applying traditional factor analysis cutoffs to Likert-type data can lead to inaccurate conclusions about model fit adequacy.
  • The extended dynamic fit index (DFI) framework provides a more reliable method for assessing model fit with Likert-type responses.
  • This research offers a crucial methodological advancement for researchers utilizing Likert-type data in factor analysis.