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A New Measurement Equivalence Technique Based on Latent Class Regression as Compared with Multiple Indicators

Jamshid Jamali1, Seyyed Mohammad Taghi Ayatollahi1, Peyman Jafari1

  • 1Department of Biostatistics, Faculty of Medicine, Shiraz University of Medical Sciences, Shiraz, Iran.

Acta Informatica Medica : AIM : Journal of the Society for Medical Informatics of Bosnia & Herzegovina : Casopis Drustva Za Medicinsku Informatiku Bih
|August 3, 2016
PubMed
Summary

The 12-item General Health Questionnaire (GHQ-12) demonstrates measurement equivalence among Iranian nurses. A new discrete latent variable method, Latent Class Regression (LCR), shows high agreement with the continuous latent variable method (MIMIC).

Keywords:
GHQ-12MIMIC modellatent class regressionmeasurement equivalencemental disorders

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Area of Science:

  • Psychiatry and Mental Health
  • Psychometrics
  • Nursing Research

Background:

  • Measurement equivalence is crucial for valid comparisons in mental health assessments across groups.
  • Traditional methods for assessing measurement equivalence, like Differential Item Functioning (DIF), often assume continuous latent variables.
  • This assumption may not hold for all mental health instruments, necessitating alternative approaches.

Purpose of the Study:

  • To compare Latent Class Regression (LCR), a method for discrete latent variables, with the Multiple Indicators Multiple Cause (MIMIC) model for continuous latent variables.
  • To assess the measurement equivalence of the 12-item General Health Questionnaire (GHQ-12) among Iranian nurses using both LCR and MIMIC methods.
  • To evaluate the suitability of LCR for discrete latent variable analysis in the context of the GHQ-12.

Main Methods:

  • A cross-sectional survey was conducted in 2014 with 771 nurses in southern Iran.
  • The 12-item General Health Questionnaire (GHQ-12) and sociodemographic questions were used to identify Minor Psychiatric Disorders (MPD).
  • Two uniform-DIF detection methods, Latent Class Regression (LCR) for discrete latent variables and Multiple Indicators Multiple Cause (MIMIC) for continuous latent variables, were applied.

Main Results:

  • Latent Class Regression (LCR) with two classes indicated that 27.4% of nurses experienced Minor Psychiatric Disorders (MPD).
  • Gender was identified as a significant factor influencing the level of MPD.
  • Both LCR and MIMIC methods showed high agreement in detecting DIF and DIF-free items across gender, age, education, and marital status.

Conclusions:

  • The 12-item General Health Questionnaire (GHQ-12) is largely an invariant measure for assessing MPD among nurses.
  • The high convergence between LCR and MIMIC supports the use of LCR for discrete latent variables, such as the GHQ-12, particularly with adequate sample sizes.
  • The findings validate the GHQ-12's utility and suggest LCR as a robust method for analyzing discrete latent variables in similar research contexts.