Han Du


Assistant Professor
Ph.D.: University of Notre Dame
Primary Area: Quantitative
Address: Pritzker Hall 4538
Email: hdu@psych.ucla.edu

Research and Teaching Interests:

My methodological interests have evolved along three inter-related lines: (1) Bayesian methods and statistical computing; (2) longitudinal data analysis and time series analysis; and (3) study design and sample size determination. I also work on developing new meta-analysis and machine learning techniques. From a substantive perspective, I am interested in applying quantitative methods in developmental, clinical, cognitive, educational, and health research.

Han Du‘ lab: dulab.psych.ucla.edu

Curriculum Vitae

Representative Publications:

Du, H., & Bentler, P.M. (In press). Distributionally-weighted least squares in structural equation modeling. Psychological Methods.

Du, H., & Enders, C. K., Keller, B. T., Bradbury, T. & Karney, B. (In press). A Bayesian latent variable selection model for nonignorable missingness. Multivariate Behavioral Research.

Enders, C. K., Du, H., & Keller, B. T. (2020). A model-based imputation procedure for multilevel regression models with random coefficients, interaction effects, and nonlinear terms. Psychological Methods, 25(1), 88–112.

Du, H., Bradbury, T. N., Lavner, J. A., Meltzer, A. L., McNulty, J. K., Neff, L. A., & Karney, B. R. (2020). A comparison of Bayesian synthesis approaches for studies comparing two means: A tutorial. Research synthesis methods11(1), 36-65.

Ray, L. A., Du, H., Grodin, E., Bujarski, S., Meredith, L., Ho, D., Nieto, S., & Wassum, K. (Advance online publication). Capturing habitualness of drinking and smoking behavior in humans. Drug and Alcohol Dependence.

Du, H., & Wang, L. (2019). Testing variance components in linear mixed modeling using permutation. Multivariate behavioral research, 1-17.

Du, H., Edwards, M. C., & Zhang, Z. (2019). Bayes factor in one-sample tests of means with a sensitivity analysis: A discussion of separate prior distributions. Behavior research methods51(5), 1998-2021.

Park, I. J., Du, H., Wang, L., Williams, D. R., & Alegría, M. (2019). The Role of Parents’ Ethnic-Racial Socialization Practices in the Discrimination–Depression Link among Mexican-Origin Adolescents. Journal of Clinical Child & Adolescent Psychology, 1-14.

Gao, M., Du, H., Davies, P. T., & Cummings, E. M. (2019). Marital conflict behaviors and parenting: Dyadic links over time. Family relations68(1), 135-149.

Enders, C. K., Hayes, T., & Du, H. (2018). A comparison of multilevel imputation schemes for random coefficient models: Fully conditional specification and joint model imputation with random covariance matrices. Multivariate behavioral research53(5), 695-713.

Du,  H., & Wang,  L. (2018).  Investigating  reliabilities  of  intraindividualvariability  indicators  with  autocorrelated  longitudinal  data. Multivariate Behavioral Research, 53(4), 502-520.

Park, I. J. K., Du, H., Wang, L., Williams, D. R., & Alegría, M. (2018). Racial/ethnic discrimination and mental health in Mexican-origin youths and their parents: Testing the “linked lives” hypothesis. Journal of Adolescent Health, 62, 480-487.

Du, H., Liu, F., & Wang, L. (2017). A Bayesian” fill-in” method for correcting for publication bias in meta-analysis. Psychological Methods, 22(4), 799-817.

Planalp, E.M., Du, H., Braungart-Rieker, J.M., & Wang, L. (2017). Growth curve modeling to studying change: A comparison of approaches using longitudinal dyadic data with distinguishable dyads. Structural Equation Modeling, 24(1), 129-147.

Du, H., Zhang, Z., & Yuan, K.-H. (2016). Power analysis for t-test with non-normal data and unequal variancess. In van der Ark, L.A., Culpepper, S., Douglas, J.A., Wang, W.-C., & Wiberg, M. (Eds.), Proceedings of Quantitative Psychology: The 81st Annual Meeting of the psychometric Society. New York, NY: Springer.

Du, H., & Wang, L. (2016). A Bayesian power analysis procedure considering uncertainty in effect size estimates from a meta-analysis. Multivariate Behavioral Research, 51(1), 589-605.

Du, H., & Wang, L. (2016). The impact of the number of dyads on estimation of dyadic data analysis using multilevel modeling. Methodology, 12(1), 21-31.