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

High-stakes personality assessments are often compromised by faking, where test-takers distort their responses according to social desirability. Many previous models have accounted for faking by modeling an additional latent dimension that quantifies each test-taker’s degree of faking. Such models assume a homogeneous response strategy among all test-takers, with substantive traits and faking jointly influencing responses to all items. However, such a model does not allow for changes in test-takers’ faking behavior over the course of the questionnaire. To address this limitation, I present a response-time-based latent response mixture model. This model assumes that item responses of each test-taker are a mixture of different latent classes related to different measurement models of substantive traits and faking. Taking into account item-level response times, the model enables the investigation of differences in the time test-takers require to respond honestly, edit a trait-based response in the direction of desirability, or fake a response entirely. For an empirical demonstration, the model is applied to a dataset (N = 1824) from a real-life personnel selection setting.