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
Lalor, J. P., Wu, H., Chen, L., Mazur, K. and 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
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.* and Wu, H.* (2018). Parameter uncertainty in structural equations models: Confidence sets and fungible estimates, Psychological Methods, 23(4), 635–653
Cheng, C# and 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. and 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.
Wu, H. and 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. and Wu, H. (2015). Profile likelihood-based confidence regions for structural equation models. Psychometrika, 80(4), 1123-1145
Wu, H. and Browne, M. W. (2015a) Quantifying adventitious error in a covariance structure as a random effect. Psychometrika, 80(3), 571-600
Dong, L.*, Wu, H.* and Waldman, I. (2014) Measurement and structural invariance of the antisocial process screening device. Psychological Assessment, 26(2), 598-608
Wu, H. and Neale, M. C. (2013). On the likelihood ratio tests in bivariate ACDE models. Psychometrika, 78(3), 441-463
Wu, H. and Neale, M. C. (2012). Adjusted confidence intervals for a bounded parameter. Behavior Genetics, 42, 886-898
Wu, H., Myung, I. J. and 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. and Batchelder, W. H. (2010b). On the complexity of multinomial processing tree models. Journal of Mathematical Psychology, 54, 291–303