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Hao Wu

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.

Representative Publications

* equal contribution; # student

Wu, H. (in press). Approximations to the distribution of test statistic in covariance structure analysis: a comprehensive study, British Journal of Mathematical and Statistical Psychology

Pek, J.* and Wu, H.* (in press). Parameter uncertainty in structural equations models: Confidence sets and fungible estimates, Psychological Methods

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

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

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