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research:johnny_zhang

Zhiyong Johnny Zhang

Associate Professor of Quantitative Psychology
PhD, 2008, University of Virginia
118D Haggar Hall
Department of Psychology
University of Notre Dame

Tel: 574-631-2902
Voice: 574-626-8886
Fax: 574-631-8883

Email: ZhiyongZhang (at) nd.edu
Web: http://nd.psychstat.org

Curriculum Vitae

Research interests

Publications

Journal Articles

(student author)

  1. Liu, H., & Zhang, Z. (accepted). Logistic Regression with Misclassification in Binary Outcome Variables: A Method and Software. Behaviormetrika
  2. Ke, Z., & Zhang, Z. (accepted). Testing Autocorrelation and Partial Autocorrelation: Asymptotic Methods versus Resampling Techniques. British Journal of Mathematical and Statistical Psychology
  3. Zhang, Z., Jiang, K., Liu, H., & Oh, I.-S. (accepted). Bayesian meta-analysis of correlation coefficients through power prior. Communications in Statistics – Theory and Methods
  4. Cain, M., Zhang, Z., & Yuan, K. (accepted). Univariate and Multivariate Skewness and Kurtosis for Measuring Nonnormality: Prevalence, Influence and Estimation. Behavior Research Methods
  5. Yuan, K.-H., Zhang, Z., & Zhao, Y. (in press). Reliable and More Powerful Methods for Power Analysis in Structural Equation Modeling. Structural Equation Modeling
  6. Cheung, R. Y. M., Cummings, E. M., Zhang, Z., & Davies, P. (2016) Trivariate Modeling of Interparental Conflict and Adolescent Emotional Security: An Examination of Mother-Father-Child Dynamics. Journal of Youth and Adolescence, 45(11), 2336–2352.
  7. Liu, H., Zhang, Z., & Grimm, K. J. (2016). Comparison of Inverse-Wishart and Separation-Strategy Priors for Bayesian Estimation of Covariance Parameter Matrix in Growth Curve Analysis. Structural Equation Modeling, 23 (3), 354-367.
  8. Zhang, Z. (2016). Modeling Error Distributions of Growth Curve Models through Bayesian Methods. Behavior Research Methods, 48(2), 427-444.
  9. Zhang, Z. & Yuan, K.-H. (2016). Robust Coefficients Alpha and Omega and Confidence Intervals with Outlying Observations and Missing Data: Methods and Software. Educational and Psychological Measurement, 76(3), 387–411
  10. Merluzzi, T.V., Philip, E.J., Zhang, Z., & Sullivan, C. (2015). Perceived discrimination, coping, and quality of life for African-American and Caucasian persons with cancer. Cultural Diversity and Ethnic Minority Psychology, 21(3), 337-344.
  11. Bernard, K., Peloso, E., Laurenceau, J-P, Zhang, Z., & Dozier, M. (2015). Examining Change in Cortisol Patterns During the 10-week Transition to a New Childcare Setting. Child Development, 86(2), 456–71.
  12. Serang, S., Zhang, Z., Helm, J., Steele, J. S., & Grimm, K. J. (2015). Evaluation of a Bayesian Approach to Estimating Nonlinear Mixed-Effects Mixture Models. Structural Equation Modelling, 22(2), 202–215.
  13. Yuan, K.-H., Tong, X., & Zhang, Z. (2015). Bias and Efficiency for SEM with Missing Data and Auxiliary Variables: Two-Stage Robust Method versus Two-Stage ML. Structural Equation Modeling, 22(2), 178–192.
  14. Zhang, Z., Hamagami, F., Grimm, K. J., & McArdle, J. J. (2015). Using R Package RAMpath for Tracing SEM Path Diagrams and Conducting Complex Longitudinal Data Analysis. Structural Equation Modeling, 22(1), 132–147. Download
  15. Zhang, Z. (2014b). Monte Carlo Based Statistical Power Analysis for Mediation Models: Methods and Software. Behavior Research Methods, 46(4), 1184-1198 Download
  16. Tong, X., Zhang, Z., & Yuan, K.-H. (2014). Evaluation of Test Statistics for Robust Structural Equation Modeling with Nonnormal Missing Data. Structural Equation Modeling, 21, 553–565. http://www.tandfonline.com/doi/pdf/10.1080/10705511.2014.919820
  17. Zhang, Z. (2014a). WebBUGS: Conducting Bayesian Analysis online. Journal of Statistical Software, 61(7),1-30. http://www.jstatsoft.org/v61/i07/paper
  18. Hardy, S. A., Zhang, Z., Skalski, J. E., Melling, B. S., & Brinton, C. T. (2014). Daily religious involvement, spirituality, and moral emotions. Psychology of Religion and Spirituality, 6(4), 338-348.
  19. Tong, X., & Zhang, Z. (2014). Abstract: Semiparametric Bayesian Modeling With Application in Growth Curve Analysis. Multivariate Behavioral Research, 49, 299-299.
  20. Song, H., & Zhang, Z. (2014). Analyzing Multiple Multivariate Time Series Data Using Multilevel Dynamic Factor Models. Multivariate Behavioral Research, 49(1), 67-77. http://www.tandfonline.com/eprint/G84HgvCIskMS9P3SkRvG/full
  21. Lu, Z., & Zhang, Z. (2014). Robust Growth Mixture Models with Non-ignorable Missingness: Models, Estimation, Selection, and Application. Computational Statistics and Data Analysis, 71, 220-240. Download
  22. Zhang, Z. (2013). Bayesian Growth Curve Models with the Generalized Error Distribution. Journal of Applied Statistics, 40(8), 1779-1795. download
  23. Grimm, K. J., Kuhl, A. P., & Zhang, Z. (2013). Measurement Models, Estimation, and the Study of Change. Structural Equation Modeling, 20(3), 504-517, DOI: http://dx.doi.org/10.1080/10705511.2013.797837.
  24. Philip, E. J., Merluzzi, T. V., Zhang, Z. & Heitzmann, C. (2013). Depression and Cancer Survivorship: Importance of Coping Self-Efficacy in Post-Treatment Survivors. Psycho-Oncology, 22(5), 987-994.
  25. Zhang, Z., Lai, K., Lu, Z., & Tong, X. (2013). Bayesian inference and application of robust growth curve models using student’s t distribution. Structural equation modeling, 20(1), 47-78. Manuscript http://www.tandfonline.com/eprint/bI5aVbVq2uwI7Xs8HiBq/full
  26. Zhang, Z., & Wang, L. (2013). Methods for mediation analysis with missing data. Psychometrika, 78(1), 154-184. Manuscript Additional information
  27. Yuan, K.-H., & Zhang, Z. (2012). Structural equation modeling diagnostics using R package semdiag and EQS. Structural Equation Modeling: An Interdisciplinary Journal, 19(4), 683-702. Manuscript
  28. Yuan, K.-H., & Zhang, Z. (2012). Robust Structural Equation Modeling with Missing Data and Auxiliary Variables. Psychometrika, 77(4), 803-826. Manuscript
  29. Tong, X., and Zhang, Z. (2012). Diagnostics of Robust Growth Curve Modeling using Student's t Distribution. Multivariate Behavioral Research,47(4), 493-518. Manuscript Software
  30. Zhang, Z., & Wang, L. (2012). A note on the robustness of a full Bayesian method for non-ignorable missing data analysis. Brazilian Journal of Probability and Statistics, 26(3), 244-264. Manuscript
  31. Zhang, Z., McArdle, J. J., & Nesselroade, J. R. (2012). Growth Rate Models: Emphasizing Growth Rate Analysis through Growth Curve Modeling. Journal of Applied Statistics, 39(6), 1241-1262. Manuscript http://www.tandfonline.com/eprint/7pWwYdzgIsEcTSQF4CHp/full
  32. Wang, L. & Zhang, Z. (2011). Estimating and testing mediation effects with censored data. Structural Equation Modeling, 18(1), 18-34. Download http://www.tandfonline.com/eprint/gR8X6zdCYk8UP2n58Y5d/full
  33. 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
  34. 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 http://www.tandfonline.com/eprint/448QBknPccekgTn7FvbB/full
  35. Tong, X., Zhang, Z., & Yuan, K.-H. (2011). Evaluation of Test Statistics for Robust Structural Equation Modeling with Non-normal Missing Data (Abstract). Multivariate Behavioral Research, 46(6), 1016-1016.
  36. Zhang, Z., Browne, M. W., & Nesselroade, J. R. (2011). Higher–order factor invariance and idiographic mapping of constructs to observables. Applied Developmental Sciences, 15(4), 186-200. Manuscript
  37. Lu, L., Zhang, Z., & Lubke, G. (2010). Bayesian Inference For Growth Mixture Models With Non-ignorable Missing Data (Abstract). Multivariate Behavioral Research, 45(6), 1028–1028.
  38. 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.
  39. 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
  40. Zhang, Z., & Wang, L. (2009). Statistical power analysis for growth curve models using SAS. Behavior Research Methods, 41(4), 1083-1094. Download PDF
  41. 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
  42. Zhang, Z., McArdle, J. J., Wang, L., & Hamagami, F. (2008). A SAS interface for Bayesian analysis with WinBUGS. Structural Equation Modeling, 15(4), 705–728. 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 http://www.tandfonline.com/eprint/bCBAPfGTvNXQkJAHCdph/full
  43. Wang, L., Zhang, Z., McArdle, J. J., & Salthouse, T. A. (2008). Investigating ceiling effects in longitudinal data analysis. Multivariate Behavioral Research, 43(3), 476–496. Download
  44. 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), 231–240. Download
  45. 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
  46. Zhang, Z., & Nesselroade J. R. (2007). Bayesian estimation of categorical dynamic factor models. Multivariate Behavioral Research, 42(4), 729-756. Download

Book Chapters

  1. Mai, Y., & Zhang, Z. (in press). Statistical Power Analysis for Comparing Means with Binary or Count Data Based on Analogous ANOVA. In L. A. van der Ark, M. Wiberg, S. A. Culpepper, J. A. Douglas, and W.-C. Wang (Eds.) Quantitative Psychology - The 81st Annual Meeting of the Psychometric Society, Asheville, North Carolina, 2016. Springer Proceedings in Mathematics & Statistics.
  2. Du, H., Zhang, Z., & Yuan, K.-H. (in press). Power analysis for t-test with non-normal data and unequal variances. In L. A. van der Ark, M. Wiberg, S. A. Culpepper, J. A. Douglas, and W.-C. Wang (Eds.) Quantitative Psychology - The 81st Annual Meeting of the Psychometric Society, Asheville, North Carolina, 2016. Springer Proceedings in Mathematics & Statistics.
  3. Zhang, Z., Wang, L., & Tong, X. (2015). Mediation Analysis with Missing Data through Multiple Imputation and Bootstrap. In L. A. van der Ark, D. M. Bolt, W.-C. Wang, J. A. Douglas, & S.-M. Chow (Eds.) Quantitative Psychology Research: the 79th Annual Meeting of the Psychometric Society. Springer Proceedings in Mathematics & Statistics. (pp. 341–355).
  4. Lu, Z., & Zhang, Z. (2015). Issues in Aggregating Time Series: Illustration through an AR(1) Model. In L. A. van der Ark, D. M. Bolt, W.-C. Wang, J. A. Douglas, & S.-M. Chow (Eds.) Quantitative Psychology Research: the 79th Annual Meeting of the Psychometric Society. Springer Proceedings in Mathematics & Statistics. (pp. 357–370).
  5. Lu, Z., Zhang, Z., & Cohen, A. (2014). Model selection criteria for latent growth models using Bayesian methods. In R. E. Millsap, D. M. Bolt, L. A. van der Ark, & W.-C. Wang (Eds.), Quantitative Psychology Research, volume 89 of Springer Proceedings in Mathematics & Statistics (pp. 319–341). Springer International Publishing.
  6. Lu, Z., Zhang, Z., & Cohen, A. (2013). Bayesian methods and model selection for latent growth curve models with missing data. In R. E. Millsap, L. A. van der Ark, D. M. Bolt, & C. M. Woods (Eds.), New Developments in Quantitative Psychology, volume 66 of Springer Proceedings in Mathematics & Statistics (pp.275–304). Springer New York.
  7. 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.
  8. 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.
  9. 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

Software

  1. Zhang, Z., Yuan, K.-H., & Cain, M. (2016). Software for estimating univariate and multivariate skewness and kurtosis. Retrieved from http://psychstat.org/nonnormal
  2. Ke, Z., & Zhang, Z. (2016). pautocorr: Testing Autocorrelation and Partial Autocorrelation Through Bootstrap and Surrogate Methods. R package retrievialbe from https://r-forge.r-project.org.
  3. Liu, H., & Zhang, Z. (2016). logistic4p: Logistic Regression with Misclassification in Dependent Variables. R package retrievialbe from https://r-forge.r-project.org.
  4. Mai, Y., Zhang, Z., & Yuan, K.-H. (2015) An Online Interface for Drawing Path Diagrams for Structural Equation Modeling. Retrieved from http://semdiag.psychstat.org
  5. Zhang, Z., Yuan, K.-H., & Mai, Y. (2015-2016). WebPower: Statistical power analysis online. Retrieved from http://webpower.psychstat.org.
  6. Zhang, Z., & Yuan, K.-H. (2015). coefficientalpha: Robust Cronbach's alpha and McDonald's omega for non-normal and missing data. http://CRAN.R-project.org/package=coefficientalpha
  7. Zhang, Z. (2014). WebBUGS: Conducting Bayesian Analysis online. Retrievable from http://webbugs.psychstat.org.
  8. Zhang, Z., Jiang, J., & Liu, H. (2013). An online software for meta-analysis of correlation. Available at http://webbugs.psychstat.org/modules/metacorr/.
  9. Zhang, Z., McArdle, J. J., Hamagami, F., & Grimm, K. J. (2013). RAMpath: Structural Equation Modeling using RAM Notation. R package version 0.3.6. http://CRAN.R-project.org/package=RAMpath
  10. Zhang, Z., Yuan, K.-H., & Mai, Y. (2012-2016). WebSEM: Structural equation modeling online. Retrievable from https://websem.psychstat.org.
  11. Zhang, Z., & Tong, X. (2011). Online software of distribution diagnostics for robust growth curve models. Available at http://nd.psychstat.org/research/mbr2012.
  12. Yuan, K.-H., & Zhang, Z. (2011). rsem: An R package for Robust non-normal SEM with Missing Data. Available at http://CRAN.R-project.org/package=rsem.
  13. Zhang, Z. & Yuan, K.-H. (2011). semdiag: An R package for structural equation modeling diagnostics. Retrievable from http://CRAN.R-project.org/package=semdiag.
  14. Zhang, Z., & Wang, L. (2011). bmem: An R packages for mediation analysis with ignorable and non-ignorable missing data. Retrievable from http://CRAN.R-project.org/package=bmem.
  15. Zhang, Z., Tong, X., & Lu, Z. (2010). Bayesian estimation of robust growth curve models using Student's t distribution. Available at http://webstats.psychstat.org/semrgcm/..
  16. Zhang, Z., & Wang, L. (2009). SAS macros for power analysis of growth curve models, Version 1.0. Retrievable from http://saspower.psychstat.org
  17. Zhang, Z., & Wang, L. (2008). BAUW as an OpenBUGS plugin, Version 1.0. Retrievable from http://bauw.psychstat.org
  18. Zhang, Z., & Wang, L. (2007). MedCI: Mediation Confidence Intervals, Version 3.0. Retrievable from http://medci.psychstat.org
  19. Zhang, Z., & Wang, L. (2006). BAUW: Bayesian Analysis Using WinBUGS, Version 1.0. Retrievable from http://bauw.psychstat.org Citations
  20. 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
  21. Zhang, Z., & Nesselroade, J. R. (2005). Selection: A C++ program for analyzing selection effects. Retrievable from http://www.psychstat.org/us/article.php/64
  22. Zhang, Z., & Nesselroade, J. R. (2004). DFA: Dynamic Factor Analysis, Version 2.0. Retrievable from http://dfa.psychstat.org

Other Publications

  1. Winter, W., Potenziano, B., Zhang, Z., Green, N., & Hammond, W.(2010). Chronotype as a predictor of performance in major league baseball pitchers, Sleep, 2010, 33, A188-A189.

Conference Presentations


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