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Detecting Social Desirability Bias Using Factor Mixture Models.

Walter L Leite1, Lou Ann Cooper2

  • 1a Research and Evaluation Methodology Program , College of Education, University of Florida.

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PubMed
Summary
This summary is machine-generated.

This study introduces a new method using factor mixture models to detect social desirable bias (SDB) in survey responses. It identifies individuals, items, and factors contributing to SDB, offering a more accurate approach than traditional correlation methods.

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Area of Science:

  • Psychometrics
  • Social Psychology
  • Educational Measurement

Background:

  • Social desirable bias (SDB) is a significant challenge in self-report measures.
  • Traditional methods for diagnosing and correcting SDB using total scores and partial correlations have limitations.

Purpose of the Study:

  • To present a novel method utilizing factor mixture models to identify social desirable bias.
  • To pinpoint examinees prone to biased responses, items eliciting SDB, and predictive external variables.

Main Methods:

  • Application of factor mixture models to analyze SDB.
  • Examination of the interaction between scale items, testing context, and respondent traits.
  • Demonstration using the Attitude toward Interprofessional Service-Learning scale.

Main Results:

  • The factor mixture model approach effectively identifies individuals and items associated with SDB.
  • This method offers a more nuanced understanding of SDB compared to correlational techniques.
  • External variables predicting SDB were identified in the analyzed sample.

Conclusions:

  • Factor mixture models provide a robust framework for detecting and understanding social desirable bias.
  • This approach enhances the validity of self-report measures in research.
  • The findings have implications for improving survey design and data interpretation in health professions education.