Faking in High-Stakes Personality Assessments: A Response-Time-Based Latent Response Mixture Modeling Approach

When personality assessments are employed in high-stakes contexts, there is the risk that test-takers provide overly positive descriptions of themselves. This response bias is known as faking and has often been addressed in latent variable models through an additional dimension capturing each test-taker’s faking degree. Such models typically assume a homogeneous response strategy for all test-takers, with substantive traits and faking jointly influencing responses to all items. In this research, we present a latent response mixture item response theory (IRT) model of faking that accounts for changes in test-takers’ response strategies over the course of the assessment. The model translates theoretical considerations about test-taker behavior into different model components for item responses and corresponding item-level response times (RT), thereby allowing to account for, identify, and investigate different faking-related response strategies on the person-by-item level. In a parameter recovery study, we found that the model parameters can be estimated well under realistic conditions. Also, we applied the model to an empirical dataset (N = 1824) from a job application context, showcasing its utility in real high-stakes assessment data. This OSF project contains the JAGS model syntaxes, R code for model estimation, as well as the materials of the pre-study assessing response time behavior (instructions, data, and analysis script) and pilot study assessing desirability values (instructions, a screenshot, data, and analysis script; from Seitz, Alagöz, & Meiser, 2025).