Areas of Research

Our current research focus is the multidisciplinary field of Computational Psychometrics which uses the methods of psychometrics, cognitive science, usability engineering, mathematical statistics, and statistical machine learning to address problems in understanding and identifying probabilistic models of human knowledge, human learning models, and human information processing.

Recent Talks and Publications

• Golden, R. M. (in press) Maximum likelihood estimation using a possibly misspecified parameter redundant model. To appear in Quantitative Psychology: The 88th Annual Meeting of the Psychometric Society.
  
• Hosseinpour, R. and Golden, Richard M. (in press). Assessment of misspecification in CDMs using a Generalized Information Matrix Test. To appear in Quantitative Psychology: The 88th Annual Meeting of the Psychometric Society.
  
• Malaiya, R. (in press). Fitting a drift-diffusion Item Response Theory model to complex cognition response times. To appear in Quantitative Psychology: The 88th Annual Meeting of the Psychometric Society.
  
• Golden, R. M. Inferring Causal Relationships from Observational Data Sets. Talk presented at the Frontiers in Brain Health Lecture Series, Center for Brain Health, University of Texas at Dallas, Dallas, Texas, May 19, 2023. Click here to watch the lecture!
  
• Fox, C. P. and Golden, R. M. (2023). Regularized robust confidence interval estimation in Cognitive Diagnostic Models. In M. Wibert, D. Molenaar, J. Gonzale, S. Kim, & H. Hwang (Eds.). Quantitative Psychology: The 87th Annual Meeting of the Psychometric Society, 42, 233-242. Springer Proceedings in Mathematics and Statistics. https://doi.org/10.1007/978-3-031-27781-8_21
  
• Golden, R. M. (2020). Statistical Machine Learning: A Unified Framework. Texts in Statistical Science Series. CRC Press.