Wei Wu, PhD

Wei Wu
Associate Professor
Education

Wei received her Bachelor of Education degree in special education in 1997 and Master of Science degree in Personality Psychology in 2003 from East China Normal University in Shanghai China, and Ph.D. in Quantitative Psychology in 2008 from Arizona State University.

Biography

Wei is an Associate Professor of Psychology at IUPUI. Prior to IUPUI, she was the director for the Quantitative Psychology training program at the University of Kansas.Wei was a co-PI on a NSF grant on planned missing data designs, and a statistical consultant on a Patient-Centered Outcomes Research Institute (PCORI) grant. She was also a statistician for four NIH funded longitudinal studies including: 1) an NICHD-funded five-year longitudinal study of the effects of elementary school grade retention on children’s educational, social, and mental health outcomes; 2) an NIH-funded longitudinal study of well-being in HIV/AIDS and rheumatoid arthritis patients; 3) an NIA funded longitudinal study of Alzheimer’s disease; and 4) an NIH-funded five-year longitudinal study to test resilience of maltreated children placed in foster care. Wei currently serves as an Associate Editor for Behavioral Research Methods and Consulting Editor for Psychological Methods. She was also a panel reviewer for Institute of Education Sciences Social and Behavioral Two scientific peer review panel. 

Research
Wei’s research has been primarily focused on missing data analysis and longitudinal data analysis. Wei’s research has been primarily focused on how to use advanced methods such as multiple imputation to deal with missing data problems in various analytical contexts such as categorical data analysis, mediation analysis, measurement invariance testing, and multilevel modeling. For longitudinal data analysis, Wei is 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. Her 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. In addition to theoretical research on statistical methods, Wei is interested in collaborating with substantive researchers to answer important research questions in health, social, and developmental psychology using advanced statistical methods.