Dr. Wei Wu, Ph.D

College of Liberal Arts and Sciences - Psychology
Associate Professor
Primary office:
Fraser Hall
Room 411
University of Kansas
1415 Jayhawk Boulevard
Lawrence, KS 66045-7556

Ph.D., 2008, Arizona State University
Research Areas: Quantitative Psychology

Teaching Interests

  • Regression analysis
  • Multivariate statistics
  • Structural equation modeling
  • Missing data analysis


My primary quantitative research interests include model fit evaluation, growth curve modeling, structural equation modeling, multilevel (mixed) modeling and missing data analysis. I am also interested in the application of these methods in education, social, developmental and health related studies.

Research Interests

  • Longitudinal Data Analysis
  • Structural equation modeling
  • Multilevel (mixed) modeling
  • Missing data analysis
  • Model fit and model selection

Selected Publications

Lang, K. M., & Wu, W. (in press). Comparison of imputation strategies to nominal missing data. Multivariate Behavioral Research.

Little, T. D., Deboeck, P., & Wu, W. (2016). Longitudinal Data Analysis. In S. K. Whitbourne (Ed.), Emerging Trends in the Behavioral and Social Sciences. Willey and Blackwell.

Little, T. D., Lang, K. M., Wu, W., & Rhemtulla, M. (2016). Missing data. In . (Ed.), Developmental Psychopathology.

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 Behavioral 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. DOI:10.1080/10705511.2014.938578

Gu, F., Preacher, K., Wu, W., & Yung, Y. (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

Jorgensen, T. D., Rhemtulla, M., Schoemann, A. M., McPherson, B., Wu, W., & Little, T. D. (2014). Optimal assignment methods in three-form planned missing data designs for longitudinal panel studies. International Journal of Behavioral Development, 38, 397–410. DOI:10.1177/0165025414531094

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

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. DOI:10.1177/0165025413515169

Pornprasertmanit, S., Wu, W., & Little, T. D. (2013). Taking into account sampling variability of model selection indices: A parametric bootstrap approach (Abstract). Multivariate Behavioral Research, 48, 168-169.

Wu, W., Selig, J. N., & Little, T. D. (2013). Longitudinal data analysis. In T. D. Little (Ed.), Handbook of Quantitative Methods. Oxford.

Pornprasertmanit, S., Wu, W., & Little, T. D. (2013). Using a Monte Carlo approach for nested model comparisons in structural equation modeling. (Vol. 66, pp. 187-197). In Springer Proceedings in Mathematics & Statistics.

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.

Yan, Y., Wu, W., Strunk, B., & Garbutt, J. (2013). Use of factor analysis models to evaluate measurement invariance properties of the Asthma Control Questionnaire (ACQ). Quality of Life Research. DOI:10.1007/s11136-013-0474-x

West, S. G., Taylor, A. B., & Wu, W. (2012). Model fit and model selection in structural equation modeling. In R. H. Hoyle (Ed.), Handbook of Structural Equation Modeling. New York: Guilford Press.

Wu, W., & Little, T. D. (2011). Quantitative Research Methods. In B. B. Brown & M. Prinstein (Eds.), Encyclopedia of Adolescence. Vol 1: Normative Development. Oxford, UK: Elsevier.

Hughes, J. N., Wu, W., & West, S. G. (2011). Teacher Performance Goal Practices and Elementary Students’ Behavioral Engagement: A Developmental Perspective. Journal of School Psychology, 49, 1-23.

Gu, F., & Wu, W. (2011). Using SAS PROC TCALIS for Multigroup Structural Equation Modeling with Mean Structures. British Journal of Mathematical and Statistical Psychology, 64, 516-537.

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. (2010). Sensitivity of SEM Fit Indices to Misspecifications in Growth Curve Models: A Simulation Study. Multivariate Behavioral Research, 45, 420–452.

Parenteau, S. C., Hamilton, N. A., Wu, W., Latinis, K., Waxenberg, L. B., & Brinkmeyer, M. Y. (2010). The Mediating Role of Secular Coping Strategies in the Relationship between Religious Appraisals and Adjustment to Chronic Pain: The Middle Road to Damascus. Social Indicators Research.

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.

West, S. G., & Wu, W. (2008). Evaluating fit in growth models for longitudinal data: Insights from SEM and HLM (abstract). International Journal of Psychology, 43, 369.

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.

West, S. G., Aiken, L. S., Wu, W., & Taylor, A. B. (2007). Multiple regression: Application of the basics and beyond in personality research. In R. W. Robins, R. C. Fraley, & R. F. Krueger (Eds.), Handbook of research methods in personality psychology. New York: Guilford Press.

Khoo, S., West, S. G., Wu, W., & Kwok, O. (2005). Longitudinal methods. In M. Eid & E. Diener (Eds.), Handbook of psychological measurement: A multimethod perspective. Washington, DC: American Psychological Association books.

Selected Grants

Salyers, Michelle, (Principal), The impact of burnout on patient-centered care: A comparative effectiveness trial in mental health, PCORI, $1,506,292, (01/01/2013 - 12/31/2016) . Federal. Status: Funded.

Wu, Wei, (Principal), Little, Todd, (Co-Principal), Planned missing research designs: power and validity of planned missing data designs in longitudinal research, No. 1053160, National science foundation, $422,900, (06/01/2011 - 05/31/2015) . Federal. Status: Funded.

Wu, Wei, (Principal), Concordance correlations in evaluating model fit for growth curve models, New Faculty General Fund Program, $8,000, (06/01/2010 - 06/30/2012) . University (KU or KUMC). Status: Funded.

Follow Us

Follow the Department of Psychology on Facebook Follow University of Kansas News on Twitter Follow KU on Youtube

One of 34 U.S. public institutions in the prestigious Association of American Universities
Nearly $290 million in financial aid annually
44 nationally ranked graduate programs.
—U.S. News & World Report
Top 50 nationwide for size of library collection.
23rd nationwide for service to veterans —"Best for Vets," Military Times