A Signal Detection Model for Multiple-Choice Exams

A New Approach to Desirable Responding: Multidimensional Item Response Model of Overclaiming Data

A variety of approaches have been presented for assessing desirable responding in self-report measures. Among them, the overclaiming technique asks respondents to rate their familiarity with a large set of real and nonexistent items (foils). The application of signal detection formulas to the endorsement rates of real items and foils yields indices of (a) knowledge accuracy and (b) knowledge bias. This overclaiming technique reflects both cognitive ability and personality. Here, we develop an alternative measurement model based on multidimensional item response theory (MIRT). We report three studies demonstrating this new model's capacity to analyze overclaiming data. First, a simulation study illustrates that MIRT and signal detection theory yield comparable indices of accuracy and bias-although MIRT provides important additional information. Two empirical examples-one based on mathematical terms and one based on Chinese idioms-are then elaborated. Together, they demonstrate the utility of this new approach for group comparisons and item selection. The implications of this research are illustrated and discussed.

Driver's Personality and Behavior for Boosting Automobile Security and Sensing Health Problems Through Fuzzy Signal Detection Case Study: Mexico City

Automobile security became an essential theme over the last years, and some automakers invested much money for collision avoidance systems, but personalization of their driving systems based on the user's behavior was not explored in detail. Furthermore, efficiency gains could be had with tailored systems. In Mexico, 80% of automobile accidents are caused by human beings; the remaining 20% are related to other issues such as mechanical problems. Thus, 80% represents a significant opportunity to improve safety and explore driving efficiency gains. Moreover, when driving aggressively, it could be connected with mental health as a post-traumatic stress disorder. This paper proposes a Tailored Collision Mitigation Braking System, which evaluates the driver's personality driving treats through signal detection theory to create a cognitive map that understands the driving personality of the driver. In this way, aggressive driving can be detected; the system is then trained to recognize the personality trait of the driver and select the appropriate stimuli to achieve the optimal driving output. As a result, when aggressive driving is detected continuously, an automatic alert could be sent to the health specialists regarding particular risky behavior linked with mental problems or drug consumption. Thus, the driving profile test could also be used as a detector for health problems.

About the Equivalence of the Latent D-Scoring Model and the Two-Parameter Logistic Item Response Model

This article shows that the recently proposed latent D-scoring model of Dimitrov is statistically equivalent to the two-parameter logistic item response model. An analytical derivation and a numerical illustration are employed for demonstrating this finding. Hence, estimation techniques for the two-parameter logistic model can be used for estimating the latent D-scoring model. In an empirical example using PISA data, differences of country ranks are investigated when using different metrics for the latent trait. In the example, the choice of the latent trait metric matters for the ranking of countries. Finally, it is argued that an item response model with bounded latent trait values like the latent D-scoring model might have advantages for reporting results in terms of interpretation.