# Hao Wu

### Associate Professor of Psychology and Human Development

My research focuses on the evaluation of statistical models used in psychology and education. This includes identifiability, the quantification of various sources of uncertainty, model fit, model selection and effect size. My research interest also includes robust and nonparametric methods. I also collaborate with researchers on applied projects.

#### Representative Publications

Selected Publications

* equal contribution; # my student

Zhang, X. & **Wu. H.** (online) Investigating structural model fit evaluation. *Structural Equation Modeling.*

Du, H. & **Wu. H.** (online) Estimating the weight matrix in distributionally weighted least squares estimation: An empirical Bayesian solution.* Structural Equation Modeling.*

Shinn, M., Yu, H., Zoltowski, A. R. & **Wu. H.** (online) Learning more from homeless point-in-time counts. *Housing Policy Debate*

Cho, S.-J.*, **Wu. H.*** & Naveiras (2024) The effective sample size in Bayesian information criterion for level-specific fixed and random-effect selection in a two-level nested model. *British Journal of Mathematical and Statistical Psychology, 77,* 289-315

Liu, H., Qu, W., Zhang, Z. & **Wu, H.** (2022) A new Bayesian structural equation modeling approach with priors on the covariance matrix parameter. *Journal of Behavioral Data Science, 2(2)*, 23-46

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.** & Yu, H. (2019). Learning latent parameters without human response patterns: Item response theory with artificial crowds. *Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing,* 4240–4250.

Lalor, J. P., **Wu, H.**, Munkhdalai, T. & Yu, H. (2018). Understanding deep learning performance through an examination of test set difficulty: A psychometric case study. *Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing,* 4711–4716.

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. (2015b) Random model discrepancy: Interpretation and technicalities (a rejoinder). *Psychometrika, 80(3),* 619-624

**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, 2*75-286

**Wu, H.**, Myung, I. J. & Batchelder, W. H. (2010b). On the complexity of multinomial processing tree models. *Journal of Mathematical Psychology, 54,* 291–303