Naidan Tu, Ph.D.
Assistant Professor
422 Bluemont Hall
Background and Research Interests
Dr. Tu’s research focuses on the development, application, and evaluation of psychometric methods to improve noncognitive assessments used in organizational and educational contexts. Her work aims to improve the effectiveness of personnel selection and educational admissions where assessment scores are used to inform high-stakes decisions about people. Her primary research areas include response bias prevention and detection, forced choice measurement, item response theory, computerized adaptive testing, and emerging technologies in assessment. Her work has been published in leading peer-reviewed journals such as Organizational Research Methods, Behavior Research Methods, and Applied Psychological Measurement. She also develops R packages, including bmggum, fcirt, and fcscoring, to make these methods accessible to the broader research and practice community.
Areas of Expertise
- Response bias prevention and detection, applicant faking in noncognitive assessment
- Psychometrics, item response theory, forced choice test, computerized adaptive testing
- Personnel selection, personality and individual differences, human resource development
Education
Ph.D. in Industrial/Organization Psychology, University of South Florida
M.A. in Industrial/Organizational Psychology, University of South Florida
M.S. in Industrial/Organizational Psychology, University of Illinois Urban Champaign
B.A. degrees in Psychology, University of Maryland, College Park
B.A. degrees in Economics, University of Maryland, College Park
Ongoing and Upcoming Future Projects
Examples of ongoing and upcoming future projects focus on (1) constructing and scoring forced choice tests, (2) detecting aberrant responding, such as faking, random responding, or AI-generated responses in personality assessments, and (3) improving assessment efficiency in organizational and educational settings using collateral information.
Selected Publications
Tu, N., Joo, S., Lee, P., & Stark, S. (2026). fcirt: An R package for forced choice models in item response theory. Applied Psychological Measurement, 50(3), 145–147.
Tu, N., Joo, S., & Stark, S. (2025). Examining the tradeoffs of exposure control and collateral information with multidimensional forced-choice computerized adaptive testing. Behavior Research Methods, 57(7), 207.
Kumar, L. S., Tu, N., Joo, S., & Stark, S. (2025). Detecting DIF with the multi-unidimensional pairwise preference model: Lord's chi-square and IPR-NCDIF methods. Applied Psychological Measurement, 49(8), 419–439.
Zhang, B., Tu, N., Angrave, L., Zhang, S., Sun, T., Tay, L., & Li, J. (2024). The generalized Thurstonian unfolding model (GTUM): Advancing the modeling of forced-choice data. Organizational Research Methods, 27(4), 713–747.
Tu, N., Kumar, L. S., Joo, S., & Stark, S. (2024). Linking methods for multidimensional forced choice tests using the multi-unidimensional pairwise preference model. Applied Psychological Measurement, 48(3), 104–124.
Tu, N., Zhang, B., Angrave, L., Sun, T., & Neuman, M. (2023). Estimating the Multidimensional Generalized Graded Unfolding Model with Covariates Using a Bayesian Approach. Journal of Intelligence, 11(8), 163.
Tu, N., Joo, S., Lee, P., & Stark, S. (2023). Comparison of parameter estimation approaches for multi-unidimensional pairwise preference tests. Behavior Research Methods, 55(6), 2764–2786.
Tu, N., Zhang, B., Angrave, L., & Sun, T. (2021). bmggum: An R package for Bayesian estimation of the multidimensional generalized graded unfolding model with covariates. Applied Psychological Measurement, 45(7–8), 553–555.