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Journal Articles

  1. Zhang, Z., Davis, H. P., Salthouse, T. A., & Tucker-Drob, E. A. (2007). Correlates of individual, and age-related, differences in short-term learning. Learning and Individual Differences, 17(3), 231?C240. Download

  2. Zhang, Z.,  Hamagami, F., Wang, L., Grimm, K. J., & Nesselroade, J. R. (2007). Bayesian analysis of longitudinal data using growth curve models. International Journal of Behavioral Development, 31(4), 374-383.Download

  3. Zhang, Z., & Nesselroade J. R. (2007). Bayesian estimation of categorical dynamic factor models. Multivariate Behavioral Research, 42(4), 729-756. Download

  4. Zhang, Z., Hamaker, E. L., & Nesselroade, J. R. (2008). Comparisons of four methods for estimating dynamic factor models. Structural Equation Modeling, 15(3), 377-402. Download

  5. Zhang, Z.,  McArdle, J. J., Wang, L., & Hamagami, F. (2008). A SAS interface for Bayesian analysis with WinBUGS. Structural Equation Modeling, 15(4), 705?C728. Download NOTE: Some SAS codes were not shown exactly during the final publishing process. Please download the final draft instead of the published one. Final Draft

  6. Wang, L., Zhang, Z., McArdle, J. J., & Salthouse, T. A. (2008). Investigating ceiling effects in longitudinal data analysis. Multivariate Behavioral Research, 43(3), 476?C496. Download

  7. Winter, W. C., Hammond, W. R., Zhang, Z., & Green, N. H. (2009). Measuring circadian advantage in Major League Baseball: A 10-year retrospective study. International. Journal of Sports Physiology and Performance, 4(3) 394-401.

  8. Hamaker, E. L., Zhang, Z., & van der Maas, H. L. J. (2009). Dyads as dynamic systems: Using threshold autoregressive models to study dyadic interactions. Psychometrika, 74(4) 727-745. Download

  9. Zhang, Z., & Wang, L. (2009). Statistical power analysis for growth curve models using SAS. Behavior Research Methods, 41(4), 1083-1094. Download PDF

  10. Wang, L. & Zhang, Z. (2011). Estimating and testing mediation effects with censored data. Structural Equation Modeling, 18(1), 18-34.

  11. Hardy, S. A., White, J., Zhang, Z., &  Ruchty, J.(2011). Parenting and the socialization of religiousness and spirituality. Psychology of Religion and Spirituality, 3(3), 217-230. doi: 10.1037/a0021600. Manuscript

  12. Zhang, Z., & Wang, L. (accepted). A note on the robustness of a full Bayesian method for non-ignorable missing data analysis. Brazilian Journal of Probability and Statistics. Manuscript

  13. Lu, Z., Zhang, Z., & Lubke, G. (2011). Bayesian Inference For Growth Mixture Models With Latent Class Dependent Missing Data. Multivariate Behavioral Research, 46(4), 567-597. Manuscript

  14. Zhang, Z., Lai, K., Lu, Z., & Tong, X. (in press). Bayesian inference and application of robust growth curve models using student??s t distribution. Structural equation modeling. Manuscript

  15. Zhang, Z., Browne, M. W., & Nesselroade, J. R. (2011). Higher?Corder factor invariance and idiographic mapping of constructs to observables. Applied Developmental Sciences, 15(4), 186-200. Manuscript

  16. Yuan, K.-H., & Zhang, Z. (accepted). Structural equation modeling diagnostics using R package semdiag and EQS. Structural Equation Modeling: An Interdisciplinary Journal.

  17. Zhang, Z., McArdle, J. J., & Nesselroade, J. R. (accepted). Growth Rate Models: Emphasizing Growth Rate Analysis through Growth Curve Modeling. Journal of Applied Statistics Manuscript

  18. Yuan, K.-H., & Zhang, Z. (In press). Robust Structural Equation Modeling with Missing Data and Auxiliary Variables. Psychometrika.

  19. Grimm, K. J., Kuhl, A. P., & Zhang, Z. (in press). An illustration of measurement model effects on studying change. Structural Equation Modeling.

Book Chapters

  1. Zhang, Z., & Wang, L. (2008). Methods for evaluating mediation effects: Rationale and comparison. In K. Shigemasu, A. Okada, T. Imaizumi, & T. Hoshino (Eds.), New trends in psychometrics(pp. 585-594). Tokyo: Universal Academy Press.Download

  2. Hamagami, F., Zhang, Z., & McArdle, J. J. (2009). Modeling latent difference score models using Bayesian algorithms. In S.-M. Chow, E. Ferrer, & F. Hsieh (Eds), Statistical methods for modeling human dynamics: An interdisciplinary dialogue (pp. 319-348). New Jersey: Lawrence Erlbaum Associates.

  3. Wang, L., Zhang, Z., & Estabrook, R. (2009). Longitudinal mediation analysis of training intervention effects. In S.-M. Chow, E. Ferrer, & F. Hsieh (Eds), Statistical methods for modeling human dynamics: An interdisciplinary dialogue(pp. 349-380). New Jersey: Lawrence Erlbaum Associates.


  1. Zhang, Z. & Yuang, K.-H. (2011). semdiag: An R package for structural equation modeling diagnostics. Retrievable from http://www.r-project.org.

  2. Zhang, Z., & Wang, L. (2011). bmem: An R packages for mediation analysis with ignorable and non-ignorable missing data. Retrievable from http://www.r-project.org.

  3. Zhang, Z., & Wang, L. (2009). SAS macros for power analysis of growth curve models, Version 1.0. Retrievable from http://saspower.psychstat.org

  4. Zhang, Z., & Wang, L. (2008). BAUW as an OpenBUGS plugin, Version 1.0. Retrievable from http://bauw.psychstat.org

  5. Zhang, Z., & Wang, L. (2007). MedCI: Mediation Confidence Intervals, Version 3.0. Retrievable from http://medci.psychstat.org

  6. Zhang, Z., & Wang, L. (2006). BAUW: Bayesian Analysis Using WinBUGS, Version 1.0. Retrievable from http://bauw.psychstat.org Citations

  7. Zhang, Z. (2006). LDSM: A C++ program for generating codes for analyzing latent difference score model in Mplus. Retrievable from http://www.psychstat.org/us/article.php/38

  8. Zhang, Z., & Nesselroade, J. R. (2005). Selection: A C++ program for analyzing selection effects. Retrievable from http://www.psychstat.org/us/article.php/64

  9. Zhang, Z., & Nesselroade, J. R. (2004). DFA: Dynamic Factor Analysis, Version 2.0. Retrievable from http://dfa.psychstat.org

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