Associate Professor of Psychology and Human Development
My research focuses on the evaluation of statistical models used in psychology and education, especially structural equation models. This includes identifiability, the quantification of various sources of uncertainty, model fit and effect size. My research interest also includes robust and nonparametric methods and hypothesis tests under nonstandard situations. I also collaborate with researchers on applied projects.
* equal contribution; # student
Gu, F., Wu, H., Yung, Y. -F., & Wilkins, J. L. M. (2021) Standard error estimates for rotated estimates of canonical correlation analysis: an implementation of the infinitesimal jackknife method. Behaviormetrika,48, 143–168.
Lalor, J. P., Wu, H., Chen, L., Mazur, K. & Yu, H. (2018). ComprehENotes: An instrument for assessing patient EHR note reading comprehension: development and validation. Journal of Medical Internet Research, 20(4):e139. doi:10.2196/jmir.9380
Gu, F & Wu, H.(2018). Simultaneous canonical correlation analysis with invariant canonical loadings. Behaviormetrika, 45(1),111-132. https://doi.org/10.1007/s41237-017-0042-8
Wu, H. (2018). Approximations to the distribution of test statistic in covariance structure analysis: a comprehensive study, British Journal of Mathematical and Statistical Psychology, 71, 334-362
Pek, J.* & Wu, H.* (2018). Parameter uncertainty in structural equations models: Confidence sets and fungible estimates, Psychological Methods, 23(4), 635–653
Cheng, C# & Wu, H. (2017). Confidence intervals of fit indexes by inverting a bootstrap test, Structural Equation Modeling, 24(6), 870-880.
Wu, H. (2016) A note on the identifiability of fixed effect 3PL models. Psychometrika, 81(4), 1093-1097
Wu, H. & Estabrook, C. R. (2016) Identification of CFA models of different levels of invariance for ordered categorical outcomes. Psychometrika, 81(4), 1014-1045
Lalor, J. P., Wu, H., & Yu, H. (2016). Building an Evaluation Scale using Item Response Theory. Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing, 648–657.
Gu, F. & Wu, H.(2016). Raw data maximum likelihood estimation for principal component analysis and two types of common principal component model: A state space approach. Psychometrika,81(3), 751-773
Wu, H. & Lin, J. (2016) A Scaled F-distribution as Approximation to the Distribution of Test Statistic in Covariance Structure Analysis, Structural Equation Modeling, 23(3), 409-421
Pek, J. & Wu, H. (2015). Profile likelihood-based confidence regions for structural equation models. Psychometrika, 80(4), 1123-1145
Wu, H. & Browne, M. W. (2015a) Quantifying adventitious error in a covariance structure as a random effect. Psychometrika, 80(3), 571-600
Dong, L.*, Wu, H.* & Waldman, I. (2014) Measurement and structural invariance of the antisocial process screening device. Psychological Assessment, 26(2), 598-608
Wu, H. & Neale, M. C. (2013). On the likelihood ratio tests in bivariate ACDE models. Psychometrika, 78(3), 441-463
Wu, H. & Neale, M. C. (2012). Adjusted confidence intervals for a bounded parameter. Behavior Genetics, 42, 886-898
Wu, H., Myung, I. J. & Batchelder, W. H. (2010a). Minimum description length model selection of multinomial processing tree models. Psychonomic Bulletin and Review, 17, 275-286
Wu, H., Myung, I. J. & Batchelder, W. H. (2010b). On the complexity of multinomial processing tree models. Journal of Mathematical Psychology, 54, 291–303