Wei Wu (吴蔚)

Quantitative Psychology, Department of Psychology , University of Kansas

I am an assistant professor at KU. My primary research interests include model fit and model selection, 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.

MY CV

My google scholar citations

  • Ph.D. Quantitative Psychology, Arizona State University --- 2008

  • M.S. Personality Psychology, East China Normal University --- 2003

  • B.Ed. Special Education, East China Normal University --- 1997
  •  

     

     

     

     

     

     

     

  • Psyc 650/790 Statistical Methods in Psychology I
  • This is the first statistic course at graduate level in psychology. The course is designed to provide you a good conceptual understanding of the central ideas and applications of multiple regression, as well as a brief review of the fundamental statistical principals so that you will have enough background to go on and take more advanced statistics courses. In the class, you will learn methods of exploring and visualizing data, linear and curvilinear regression analysis with continuous outcome variables. You will learn centering to improve the interpretation of the results of a regression model. You will learn using dummy, effect and contrast codes to conduct focused tests when the independent variables are categorical. You will learn how to model interaction effect of two independent variables. You will also learn how to select, diagnose and improve your models, and how to conduct power analysis for regression analysis and missing data analysis. Finally, you will learn the basics of using SAS and R for multiple regression analysis and using Gpower for power analysis.

  • Psyc 693/893 Multivariate Statistics
  • The goal of the course is to provide you with adequate familiarity with classic multivariate statistics to use this family of analytic techniques in your own research. You will learn multivariate analysis of variance, discriminant analysis, exploratory factor analysis, cluster analysis, and survival analysis. You will learn both the underlying mathematics and the applications of the techniques.

  • Psyc 996 Structural Equation Modeling II
  • The course is designed to promote a depth of understanding the statistical theory and practice of SEM as it is employed in the social and behavioral sciences. More specifically, you will get exposure to major advanced SEM topics including latent interaction, power analysis for SEM, multilevel SEM, mixture modeling, latent Curve modeling, Bayesian SEM, and how to handle violation of assumptions of SEM. You will gain proficiency testing models with Mplus, interpreting results, and drawing meaningful substantive conclusions.

  • Psyc 993 missing data analysis
  • The primary goal of the course is to promote a solid understanding of the logic and implementation of modern missing data techniques. The following topics are included: missing data theory, traditional missing data techniques, maximum likelihood estimation, EM algorithm, multiple imputation, planned missing data designs, techniques for missing not at random data, and sensitivity analysis. Students will learn how to implement the missing data techniques in SAS, Mplus and R. This class assumes familiarity with multiple regression and structural equation modeling.

     

     

     

     

       

     

     

    1. Lang*, M. K., Wu, W. (in revision). MIBEN: Multiple Imputation with the Bayesian Elastic Net. Psychological Methods..

    2. Lang*, M. K., Wu, W. (in revision). Comparison of imputation strategies to nominal missing data. Multivariate Behavior Research..

    3. 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

    4. Little, T. D., Lang*, M. K., Wu, W., & Rhemtulla, M. (2016). Missing data. In Dante, C. (Ed.) Developmental Psychopathology, 3rd Edition.

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

    6. 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 supplementary materials

    7. Wu, W., Jia*, F., & Enders, C. K. (2015). A comparison of imputation strategies for ordinal missing data on Likert scale variables. Multivariate Behavioral Research, 50(5), 484-503.doi: 10.1080/00273171.2015.1022644. Download article here

    8. Wu, W., & Lang*, K. M. (2015). Proportionality Assumption in Latent Basis Curve Models: A Cautionary Note, Structural Equation Modeling: A Multidisciplinary Journal, 23, 140-154. doi: 10.1080/10705511.2014.938578

    9. Paat, Y.-F., Wu, W., & Hope, T. L. (2014). Child health and family emotional climate in early childhood. Journal of Family Social Work, 17(5), 401-417. doi: 10.1080/10522158.2014.940636

    10. 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

    11. 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

    12. 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

    13. 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(5), 397–410. doi:10.1177/0165025414531094

    14. Wu, W., & Jia*, F. (2013). A new procedure to test mediation with missing data through nonparametric bootstrapping. Multivariate Behavioral Research. 48, 663 – 691. Supplementary material; SAS macro for the empirical example; R script

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

    16. 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.

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

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

    19. 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.

    20. Parenteau, S. C., Hamilton, N. A. , Wu, W., Latinis, K., Waxenberg, L. B., and Brinkmeyer, M. Y. (2011). 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.

    21. 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.

    22. Wu, W., & Little, T. D. (2012). Quantitative Research Methods. In B. B. Brown and M. Prinstein (Eds.), Encyclopedia in Adolescence.

    23. Wu, W., Selig, J. N., & Little, T. D. (2012). Longitudinal data analysis. In Little, T. D. (Ed.) Handbook of Quantitative Methods. Oxford.

    24. 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.

    25. 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.

    26. 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. Download article here; Download supplementary material here

    27. 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.Download article here

    28. 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. Download article here , Download supplementary materials here

    29. 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. Download article here

    30. 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. Download article here

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

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

    1. R package SEEDMC

    SEEDMC stands for search for efficient designs using Monte Carlo simulation. SEEDMC implements a systematic procedure proposed in Wu, Jia, Rhemtulla, and Little (2015) to search for efficient complete data and planned missing data designs for growth-curve modeling. SEEDMC creates the design pool, and uses functions in the R package MplusAutomation (Hallquist & Wiley, 2014) to automate the Monte Carlo simulation in Mplus (Muthén & Muthén, 1998-2012). The current version of the package (i.e., SEEDMC 1.0.0) can accommodate user specified unconditional linear and quadratic GCMs, budget and sample size constraints, and output efficient designs for any single effect and multiple effects based on a selected threshold.

    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.

    The package can be downloaded here. SEEDMC_1.0.0 or on GitHub https://github.com/fjia/SEEDMC

    To install the package locally, click "Packages -> Install package(s) from local zip file...". Before running the functions, make sure that the working directory is appropriately set using setwd(), because the package will save all Mplus outputs and the R result under the working directory. The help documents for the package can be accessed by running help(package = "SEEDMC") in R.

    The citation for the package is
    Jia, F., & Wu, W. (2015). SEEDMC: SEarch for Efficient Designs using Monte Carlo Simulation. R package version 1.0.0.

    2. A four steps procedure to implement the MI(BOOT) approach proposed by Wu and Jia (2013) for test of mediation effects with multiple imputation using SAS and Mplus. 

    The procedure can accommodate any mediation models that can be analyzed using Mplus (e.g., longitudinal mediation models or latent mediation models).  A simple example is used to illustrate the steps involved in the procedure. In this example, there are six variables: y (outcome variable), m (mediator), x (predictor), a1, a2, and a3 are auxiliary variables. The dataset used in the example can be downloaded here.

    Step 1: obtain m (e.g., 50) imputations and k (e.g., 100) bootstrap samples for each imputation.  There are in total m*k (e.g., 5,000) samples. Write out the imputed datasets and the bootstrap samples as external text files which will be used in step 2 for data analysis.  Two text files are also created to index the imputed datasets (implist.dat) and the bootstrap datasets (blist.dat).  The step is implemented using the sas macro % mibootstep1. The macro can be downloaded here.

    Below are the macro variables you need to specify in the macro.
    %let nimp = 50; *number of imputed datasets;
    %Let nboot = 100; *number of bootstrap samples;
    %let folder = R:\users\wwei\medmiss\example; * the folder where the original data is;
    %let varlist = y x m a1 a2 a3; * the list of variables that are included in the imputation model;

    Step 2: fit the mediation model using Mplus to the imputed datasets. Record the final point estimate of ab. The example mplus syntax for this step is here.

    Step 3: fit the mediation model using Mplus to each of the bootstrap samples. The bootstrap datasets are read in as if they were simulated datasets.  The parameter estimates from each of the bootstrap datasets is saved to an external dataset using the savedata command in Mplus. In this step, one needs to figure out the number of variables/columns in the saved dataset and the column(s) that contains the parameter estimates for the target mediation effect. The example mplus syntax for this step is here.

    In the example, the estimates are saved in a dataset called sample.dat. The 9th column in this dataset contains the estimates for the mediation effect.

    Step 4. Computed the bias corrected confidence interval for the target mediation effect based on the outcomes from steps 2 and 3. This step is implemented using the sas macro %bcci which will print out the lower (bc_low) and upper limit (bc_up) of the interval.  The macro can be downloaded here.
    Note that this macro is written to compute the bias corrected confidence interval for only one mediation effect at a time. If there are multiple mediation effects in the model, you just need to run the macro for each of the effects by modifying the values for the the macro variables ab and abe correspondingly.

    Below are the macro variables you need to specify

    %let savedata = R:\users\wwei\medmiss\example\sample.dat; * the dataset contains the parameter estimates from each of the boostrap samples;
    %let varlist2 = x1 - x33; * the list of variables in the dataset from step 3;
    %let ab = x9; * the variable that represents the mediation effect. In the example, it is the 9th variable;
    %let abe = 0.153; * the point estimate of ab, this value is obtained from step 2;
    %let alpha = 0.95; *confidence level;

    Reference.
    Wu, W., & Jia, F. (2013). A new procedure to test mediation with missing data through nonparametric bootstrapping and multiple imputation. Multivariate Behavioral Research, 48(5), 663-691.