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Disentangling Qualitatively Different Faking Strategies in High-Stakes Personality Assessments: A Mixture Extension
Timo Seitz1, Ö Emre C Alagöz1, Thorsten Meiser1
1University of Mannheim, Mannheim, Germany.
Educational and Psychological Measurement
|August 4, 2025
Summary
This study introduces a new mixture model to detect diverse faking strategies in personality tests. It improves accuracy by recognizing that not all individuals fake in the same way during assessments.
Area of Science:
- Psychological Measurement
- Psychometrics
- Personality Assessment
Background:
- High-stakes personality tests are vulnerable to faking, where individuals manipulate responses for social desirability.
- Existing models often assume a single faking strategy, potentially misrepresenting response patterns.
- This homogeneity assumption can lead to model misspecification if individuals employ different response strategies.
Purpose of the Study:
- To propose and validate a mixture modeling extension of the multidimensional nominal response model (M-MNRM).
- To account for qualitatively different response strategies in high-stakes personality assessments.
- To investigate the relationship between strategy use and external variables.
Main Methods:
- Development of the mixture multidimensional nominal response model (M-MNRM).
- Simulation studies to assess parameter recovery and classification accuracy.
- Analysis of three empirical high-stakes datasets from personnel selection contexts.
Main Results:
- The M-MNRM demonstrated good parameter recovery and high classification accuracy in simulations.
- Empirical analyses confirmed the consistent presence of distinct latent classes representing different response strategies.
- Evidence suggests response behavior heterogeneity is prevalent in high-stakes assessment data.
Conclusions:
- The M-MNRM effectively accounts for diverse response strategies in personality assessments.
- Acknowledging response heterogeneity is crucial for accurate interpretation of high-stakes testing data.
- The proposed model offers utility for improving psychological measurement in applied settings.
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