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Modeling Faking in the Multidimensional Forced-Choice Format: The Faking Mixture Model.

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  • 1Department of Psychology, School of Social Sciences, Mannheim, Germany. susanne.frick@uni-mannheim.de.

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Summary
This summary is machine-generated.

Multidimensional forced-choice (MFC) questionnaires may not fully reduce faking. A new Faking Mixture model estimates block fakability, showing matching items is necessary but not sufficient for faking reduction in psychological assessments.

Keywords:
fakingitem response theorymixture modelmultidimensional forced-choice

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Area of Science:

  • Psychological Measurement
  • Psychometrics
  • Item Response Theory

Background:

  • The multidimensional forced-choice (MFC) format is theorized to mitigate response distortion (faking) by presenting respondents with item blocks of matched desirability.
  • However, the assumption that item-level desirability matching effectively reduces faking at the block level requires empirical investigation.

Purpose of the Study:

  • To propose and validate a novel mixture item response theory model, the Faking Mixture model, designed to quantify the fakability of MFC item blocks.
  • To assess the efficacy of item desirability matching in reducing faking within MFC questionnaires.

Main Methods:

  • Development of a Faking Mixture model, a novel mixture item response theory approach for analyzing faking in MFC formats.
  • Utilizing within-subject data from both high- and low-stakes testing contexts for model estimation.
  • Conducting simulation studies to evaluate parameter recovery under diverse conditions.
  • Empirical validation of the model with real-world data.

Main Results:

  • Simulation results demonstrated good parameter recovery for the Faking Mixture model across various conditions.
  • Empirical validation indicated that matching item desirability within blocks is a necessary but insufficient strategy for reducing faking in MFC questionnaires.
  • The Faking Mixture model provides a viable method for estimating the fakability of MFC blocks.

Conclusions:

  • The Faking Mixture model offers a robust statistical framework for assessing and understanding faking in multidimensional forced-choice assessments.
  • Current item matching strategies in MFC formats may not adequately control for response distortion, highlighting the need for advanced analytical approaches.
  • The Faking Mixture model can inform test construction processes to develop more resilient MFC questionnaires that minimize fakability.