Wei WuAssociate Professor, Department of Psychology
As a quantitative psychologist, I am interested in developing, improving, and evaluating statistical methods that are useful in social and behavioral science research. My research has been primarily focused on missing data analysis and longitudinal data analysis. In addition to theoretical research on quantitative methods, I am interested in collaborating with substantive researchers to answer important research questions in health, social, and developmental psychology using advanced statistical methods.
Missing data analysis. Missing data are ubiquitous in social and behavioral science research and can potentially affect the outcome of any statistical analysis. Missing data techniques such as full information maximum likelihood and multiple imputation (MI) that represent clear advances over previous approaches have been developed over the past few decades. My research has been primarily focused on how to appropriately use these methods, especially MI, in different analytical context such as categorical data analysis, mediation analysis, measurement invariance testing, and multilevel modeling. In addition, I am interested in developing planned missing data designs can be exploited to improve the validity and efficiency of a study.
Longitudinal data analysis. I am interested in both analysis and design issues related to longitudinal data analysis, especially methods to analyze change such as growth curve modeling and methods to probe possible causal effects such as cross lagged panel models. My research has been focused on four theoretical issues: 1) evaluating model fit for growth curve models, 2) modeling truly nonlinear change trajectories, 3) accounting for random coefficients (individual differences in causal effects) and heteroscedasticity in longitudinal mediation models, and 4) designing longitudinal studies to maximize the power and efficiency to detect the target effects of interest.
Dr. Wu is accepting new students for enrollment in Fall 2018.
2008 Ph.D., Quantitative Psychology - Arizona State University
2003 M.S., Personality Psychology - East China Normal University, Shanghai, P. R. China
1997 B.Ed., Special Education - East China Normal University, Shanghai, P. R. China
Courses Taught / Teaching
PSY-B 305: Statistics
Publications & Professional Activities
* The author is a former or current student/graduate advisee.
** The author is a former post doc.
Wu, W., Carroll*, A. I., & Chen*, P-Y. A Single-Level Random Coefficients Cross-Lagged Panel Models for Longitudinal Mediation Analysis. (in press). Behavioral Research Methods.
Lang*, M. K., Wu, W. (2017). Comparison of imputation strategies to nominal missing data. Multivariate Behavior Research, 1-15. doi:10.1080/00273171.2017.1289360
Wu, W., Jia*, F., Kinai*, R., & Little, T. D. (2016). Optimal number and allocation of repeated Measures for linear spline growth modeling: A search for efficient designs. International Journal of Behavior Development. doi: 10.1177/0165025416644076
Wu, W., Jia*, F., & Enders, C. K. (2015). A comparison of imputation strategies for ordinal missing data on Likert scale variables. Multivariate Behavioral Research. doi: 10.1080/00273171.2015.1022644
Wu. W., Jia*, F., Rhemtulla, M., & Little, T. D. (2015). Search for efficient complete and planned missing data designs for analysis of change. Behavioral Research Methods. doi: 10.3758/s13428-015-0629-5
Wu, W., & Lang*, K. M. (2015): Proportionality Assumption in Latent Basis Curve Models: A Cautionary Note, Structural Equation Modeling, Advance online publication. doi: 10.1080/10705511.2014.938578
Gu*, F., Preacher, K. J., Wu, W., & Yung, Y-F. (2014). A Computationally efficient state space approach to estimating multilevel regression models and multilevel confirmatory factor models. Multivariate Behavioral Research, 49, 119-129, doi: 10.1080/00273171.2013.866537
Schoemann, A. M., Miller*, P. R., Pornprasermanit*, P., & Wu, W. (2014). Using Monte Carlo simulations to determine power and sample size for planned missing designs. International Journal of Behavior Development. Advance online publication. doi: 10.1177/0165025413515169
Rhemtulla**, M., Jia*, F., Wu, W., & Little, T. D. (2014). Planned missing designs to optimize the efficiency of latent growth parameter estimates. International Journal of Behavior Development. Advance online publication. doi:10.1177/0165025413514324
Wu, W., & Jia*, F. (2013). A new procedure to test mediation with missing data through nonparametric bootstrapping. Multivariate Behavioral Research. 48, 663 - 691.
Wu, W., & West, S. G. (2013). Detecting misspecification in mean structures for growth curve models: performance of pseudo R2 and concordance correlation coefficients. Structural Equation Modeling, 20, 455-478.
Wu, W., & Little, T. D. (2011). Quantitative Research Methods. In B. B. Brown and M. Prinstein (Eds.), Encyclopedia of Adolescence. Vol 1: Normative Development. Oxford, UK: Elsevier.
Wu, W., West, S. G., & Hughes, J. N. (2010). Effect of grade retention in first grade on psychosocial outcomes and school relationships. Journal of Educational Psychology, 102, 135-152.
Wu, W., West, S. G., & Taylor, A. B. (2009). Evaluating model fit for growth curve models: integration of fit indices from SEM and MLM frameworks. Psychological Methods, 14, 183-201.
Wu, W., West, S. G., & Hughes, J. N. (2008). Effect of retention in first grade on children's achievement trajectories over four years: A piecewise growth analysis using propensity score matching. Journal of Educational Psychology, 100, 727-740.
Wu, W., West, S. G., & Hughes, J. N. (2008). Short-term effects of grade retention on the growth rate of Woodcock Johnson III broad math and reading scores. Journal of School Psychology, 46, 85-105* The author is a former or current KU student/graduate advisee.
Honors, Awards and Grants
2004 -2008 Longitudinal study of grade retention in elementary school. (PI: Jan Hughes). Role: Research assistant and data analyst. Funded by National Institute of Child Health and Human Development.
2010 - 2013 Concordance correlations in evaluating model fit for growth curve models. (PI: Wei Wu). Funded by New Faculty General Fund Program. Total Costs: $8,000.
2010 - 2014 Testing determinants of resilience: maltreatment and the development of adaptive behavior. (PI: Yolanda Jackson; Co-PI: Todd Little). Role: Statistical Consultant. Funded by National Institutes of Health. Total Costs: $1,700,000.
2011 - 2016 Planned missing research designs: power and validity of planned missing data designs in longitudinal research (PI: Wei Wu, CO-PI: Todd Little). Funded by the National Science Foundation (NSF, No. 1053160). Total Costs: $422,900.
2012 Empirical Sampling Distribution (ESD) Approach: A general approach to model fit evaluation and model selection. (PI: Wei Wu, CO-PI: Todd Little). Submitted to National science foundation (NSF). Not funded.
2013-2016 The impact of burnout on patient-centered care: A comparative effectiveness trial in mental health. (PI: Michelle Salyers). Role: Statistical Consultant. Funded by Patient-Centered Outcomes Research Institute (PCORI). Total costs: $1,506,292
Associate Editor (2017 -)
Behavioral Research Methods
Psychological Methods (2015 - )