Linh Nguyen


PhD Student, University of Minnesota

Headshot of Linh Nguyen

Talk Title

Alleviating Response Biases With "thirt": An R Package Applying the Thurstonian Item Response Theory Model for Forced-Choice Questionnaires


Personality assessments are becoming increasingly popular outside of the academic settings, including hiring and promotion decisions. In these high-stakes settings, traditional personality questionnaires using Likert scales tend to incur response biases, in which participants may easily manipulate their responses to fit a desirable personality profile. Forced-choice questionnaires prevent biased responding by asking participants to rank different items each describing different constructs, which might be equally as socially desirable. However, these assessments provide ipsative scores that cannot be compared between individuals. I developed and tested "thirt", an R package created specifically to simulate and estimate parameters under the Thurstonian Item Response Theory model for multi-dimensional forced-choice questionnaires. I will (1) explain the motivation behind forced-choice questionnaires for personality assessments, (2) describe the fundamentals of item response theory models in general and specifications of the current model, (3) describe the data-generating model and Markov chain Monte Carlo estimation algorithm behind the R package "thirt", and (4) present simulation results that compares parameter recovery and performance between "thirt" and an existing R package "thurstonianIRT" which relies on existing general-purpose algorithms to fit the model. This paper provides evidence that by creating an estimation algorithm specific to this model, we are able to successfully and much more efficiently recover important parameters.


I am a statistical consultant and psychology researcher, with an MS '22 in statistics and pursuing a PhD '24 in personality psychology at the University of Minnesota. I am pursuing a career in data science to apply my dual training in substantive individual difference research and technical statistical analysis. I enjoy data management, visualization, and analysis. I am passionate about effective scientific communication and translating technical findings to real-world applications.