Publications

Journal Articles

  1. Krettenauer, T., Lefebvre, J. P., Hardy, S. A., Zhang, Z., & Cazzell, A. R. (2021). Daily moral identity: Linkages with integrity and compassion. Journal of Personality. https://doi.org/10.1111/jopy.12689

  2. Zhang, Z. & Zhang, D. (2021). What is Data Science? An Operational Definition based on Text Mining of Data Science Curricula. Journal of Behavioral Data Science 1(1), 1-16.

  3. Liu, H. & Zhang, Z. (2021). Birds of a Feather Flock Together and Opposites Attract: The Nonlinear Relationship Between Personality and Friendship, Journal of Behavioral Data Science 1(1), 34-52.

  4. Liu, H., Jin, I.-H., Zhang, Z., & Yuan, Y. (2021). Social network mediation analysis: A latent space approach. Psychometrika, 86(1), 272-298.

  5. Che, C., Jin, I.-K., & Zhang, Z. (2021). Network Mediation Analysis Using Model-based Eigenvalue Decomposition. Structural Equation Modeling28(1), 148-161.

  6. *Kuang, Y., Zhang, Z., Duan, B., & Zhang, P. (2020). Fuzzy Cognitive Maps-based Switched-Mode Power Supply Design Assistant System. IEEE Access, 8, 183014-183024. https://doi.org/10.1109/ACCESS.2020.3029090
  7. *Tong, X., & Zhang, Z. (2020). Robust Bayesian approaches in growth curve modeling: Using Student's t distributions versus semiparametric methods. Structural Equation Modeling, 27(4), 544-560. https://doi.org/10.1080/10705511.2019.1683014
  8. *Wen, Q., *Liu, H., & Zhang, Z. (2020). Generating multivariate non-normal random numbers with specified multivariate skewness and kurtosis. Behavior Research Methods, 52, 939–946. https://doi.org/10.3758/s13428-019-01291-5
  9. *Wilcox, L.T., Jacobucci, R. & Zhang, Z. (2019). Bayesian Supervised Topic Modeling with Covariates (Abstract). Multivariate Behavioral Research. https://doi.org/10.1080/00273171.2019.1695568
  10. *Du, H., Edwards, M., & Zhang, Z. (2019). Bayes factor in one-sample tests of means with a sensitivity analysis: A discussion of separate prior distributions. Behavior Research Methods, 51(5), 1998–2021. https://doi.org/10.3758/s13428-019-01262-w
  11. Serang, S., Grimm, K. J., & Zhang, Z. (2019). On the correspondence between the latent growth curve and latent change score models. Structural Equation Modeling, 26(4), 623-635. https://doi.org/10.1080/10705511.2018.1533835  
  12. *Cain, M. K., & Zhang, Z. (2019). Fit for a Bayesian: An evaluation of PPP and DIC for structural equation modeling. Structural Equation Modeling, 26(1), 39–50. https://doi.org/10.1080/10705511.2018.1490648
  13. Yuan, K., Zhang, Z., & Deng, L. (2019). Fit indices for mean structures with growth curve models. Psychological Methods, 24(1), 36-53. https://doi.org/10.1037/met0000186
  14. *Liu, H., Jin, I. K., & Zhang, Z. (2018). Structural equation modeling of social networks: Specification, estimation, and application. Multivariate Behavioral Research, 53(5), 714730. https://doi.org/10.1080/00273171.2018.1479629
  15. ^Mai, Y., Zhang, Z., & Wen, Z. (2018). Comparing exploratory structural equation modeling and existing approaches for multiple regression with latent variables. Structural Equation Modeling, 25(5), 737749. https://doi.org/10.1080/10705511.2018.1444993
  16. ^Mai, Y., & Zhang, Z. (2018). Review of software packages for Bayesian multilevel modeling. Structural Equation Modeling, 25(4), 650–658. https://doi.org/10.1080/10705511.2018.1431545
  17. *Cain, M. K., Zhang, Z., & Bergeman, C. S. (2018). Time and other considerations in mediation design. Educational and Psychological Measurement, 78(6), 952972. https://doi.org/10.1177/0013164417743003
  18. *Ke, Z., & Zhang, Z. (2018). Testing autocorrelation and partial autocorrelation: Asymptotic methods versus resampling techniques. British Journal of Mathematical and Statistical Psychology, 71(1), 96–116. https://doi.org/10.1111/bmsp.12109
  19. *Tong, X., & Zhang, Z. (2017). Outlying observation diagnostics in growth curve modeling. Multivariate Behavioral Research, 52(6), 768–788. https://doi.org/10.1080/00273171.2017.1374824
  20. Zhang, Z., Jiang, K., *Liu, H., & Oh, I.-S. (2017). Bayesian meta-analysis of correlation coefficients through power prior. Communications in Statistics: Theory and Methods, 46(24), 1198812007. https://doi.org/10.1080/03610926.2017.1288251
  21. *Cain, M. K., Zhang, Z., & Yuan, K. (2017). Univariate and multivariate skewness and kurtosis for measuring nonnormality: Prevalence, influence and estimation. Behavior Research Methods, 49(5), 1716–1735. https://doi.org/10.3758/s13428-016-0814-1
  22. *Liu, H., & Zhang, Z. (2017). Logistic regression with misclassification in binary outcome variables: A method and software. Behaviormetrika, 44(2), 447–476. https://doi.org/10.1007/s41237-017-0031-y
  23. Yuan, K.-H., Zhang, Z., & Zhao, Y. (2017). Reliable and more powerful methods for power analysis in structural equation modeling. Structural Equation Modeling, 24(3), 315330. https://doi.org/10.1080/10705511.2016.1276836
  24. *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. https://doi.org/10.1007/s10964-015-0406-x
  25. *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), 354367. https://doi.org/10.1080/10705511.2015.1057285
  26. Zhang, Z. (2016). Modeling error distributions of growth curve models through Bayesian methods. Behavior Research Methods, 48(2), 427444. https://doi.org/10.3758/s13428-015-0589-9
  27. 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. https://doi.org/10.1177/0013164415594658
  28. 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 Modeling, 22(2), 202–215. https://doi.org/10.1080/10705511.2014.937322
  29. 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. https://doi.org/10.1080/10705511.2014.935750
  30. 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. https://doi.org/10.1111/cdev.12304
  31. 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), 337344. https://doi.org/10.1037/a0037543
  32. 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. https://doi.org/10.1080/10705511.2014.935257
  33. 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), 338348. http://doi.org/10.1037/a0037293
  34. *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. https://doi.org/10.1080/10705511.2014.919820
  35. Zhang, Z. (2014a). WebBUGS: Conducting Bayesian analysis online. Journal of Statistical Software, 61(7), 130. http://doi.org/10.18637/jss.v061.i07
  36. Zhang, Z. (2014b). Monte Carlo based statistical power analysis for mediation models: Methods and software. Behavior Research Methods, 46(4), 11841198. https://doi.org/10.3758/s13428-013-0424-0
  37. Song, H., & Zhang, Z. (2014). Analyzing multiple multivariate time series data using multilevel dynamic factor models. Multivariate Behavioral Research, 49(1), 6777. https://doi.org/10.1080/00273171.2013.851018
  38. *Lu, Z., & Zhang, Z. (2014). Robust growth mixture models with non-ignorable missingness: Models, estimation, selection, and application. Computational Statistics and Data Analysis, 71, 220240. https://doi.org/10.1016/j.csda.2013.07.036
  39. *Tong, X., & Zhang, Z. (2014). Abstract: Semiparametric Bayesian modeling with application in growth curve analysis. Multivariate Behavioral Research, 49, 299299. https://doi.org/10.1080/00273171.2014.912928
  40. Zhang, Z. (2013). Bayesian growth curve models with the generalized error distribution. Journal of Applied Statistics, 40(8), 17791795. https://doi.org/10.1080/02664763.2013.796348
  41. Grimm, K. J., Kuhl, A. P., & Zhang, Z. (2013). Measurement models, estimation, and the study of change. Structural Equation Modeling, 20(3), 504517, DOI: http:// doi.org/10.1080/10705511.2013.797837
  42. 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), 987994. https://doi.org/10.1002/pon.3088
  43. Grimm, K. J., Zhang, Z., Hamagami, F., & Mazzocco, M. (2013). Modeling nonlinear change via latent change and latent acceleration frameworks: Examining velocity and acceleration of growth trajectories. Multivariate Behavioral Research, 48, 117143. https://doi.org/10.1080/00273171.2012.755111
  44. 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), 4778. https://doi.org/10.1080/10705511.2013.742382
  45. Zhang, Z., & Wang, L. (2013). Methods for mediation analysis with missing data. Psychometrika, 78(1), 154184. https://doi.org/10.1007/s11336-012-9301-5
  46. Yuan, K.-H., & Zhang, Z. (2012). Robust structural equation modeling with missing data and auxiliary variables. Psychometrika, 77(4), 803826. https://doi.org/10.1007/s11336-012-9282-4
  47. *Tong, X., and Zhang, Z. (2012). Diagnostics of robust growth curve modeling using Student's t distribution. Multivariate Behavioral Research, 47(4), 493518. 

https://doi.org/10.1080/00273171.2012.692614  

  1. Yuan, K.-H., & Zhang, Z. (2012). Structural equation modeling diagnostics using R package semdiag and EQS. Structural Equation Modeling: An Interdisciplinary Journal, 19(4), 683702. https://doi.org/10.1080/10705511.2012.713282
  2. 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), 244264.  https://doi.org/10.1214/10-BJPS132
  3. 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), 12411262. https://doi.org/10.1080/02664763.2011.644528
  4. *Tong, X., Zhang, Z., & Yuan, K.-H. (2011). Abstract: Evaluation of test statistics for robust structural equation modeling with nonnormal missing data. Multivariate Behavioral Research, 46(6), 10161016. https://doi.org/10.1080/00273171.2011.636715  
  5. Wang, L. & Zhang, Z. (2011). Estimating and testing mediation effects with censored data. Structural Equation Modeling, 18(1), 1834. 

http://doi.org/10.1080/10705511.2011.534324

  1. 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), 217230. https://doi.org/10.1037/a0021600
  2. *Lu, Z., Zhang, Z., & Lubke, G. (2011). Bayesian inference for growth mixture models with latent class dependent missing data. Multivariate Behavioral Research, 46(4), 567597. https://doi.org/10.1080/00273171.2011.589261
  3. 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), 186200.  https://doi.org/10.1080/10888691.2011.618099
  4. *Lu, Z., Zhang, Z., & Lubke, G. (2010). Abstract: Bayesian inference for growth mixture models with non-ignorable missing data. Multivariate Behavioral Research, 45(6), 1028–1028. https://doi.org/10.1080/00273171.2010.534381   
  5. 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) 394401. https://doi.org/10.1123/ijspp.4.3.394
  6. 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) 727745. https://doi.org/10.1007/s11336-009-9113-4
  7. Zhang, Z., & Wang, L. (2009). Statistical power analysis for growth curve models using SAS. Behavior Research Methods, 41(4), 10831094. https://doi.org/10.3758/BRM.41.4.1083
  8. 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. https://doi.org/10.1080/10705510802154281
  9. 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.  https://doi.org/10.1080/10705510802339106
  10. 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.  https://doi.org/10.1080/00273170802285941
  11. 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.  https://doi.org/10.1016/j.lindif.2007.01.004
  12. 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), 374383. https://doi.org/10.1177/0165025407077764
  13. Zhang, Z., & Nesselroade J. R. (2007). Bayesian estimation of categorical dynamic factor models. Multivariate Behavioral Research, 42(4), 729–756. https://doi.org/10.1080/00273170701715998

Books and Monographs

  1. Zhang, Z., Yuan, K.-H., Wen, Y., & Tang, J. (Eds.). (2020). New developments in data science and data analytics: Proceedings of the 2019 meeting of the International Society for Data Science and Analytics. Granger, IN: ISDSA Press. https://doi.org/10.35566/isdsa2019. To order: https://www.amazon.com/gp/product/1946728039
  2. Zhang, Z., & Yuan, K.-H. (Eds.). (2018). Practical statistical power analysis using Webpower and R. Granger, IN: ISDSA Press. To order: https://www.amazon.com/gp/product/1946728020. Free E-book:  https://bit.ly/32ybdzQ
  3. Zhang, Z. & Wang, L. (2017). Advanced statistics using R. Granger, IN: ISDSA Press. Retrievable from https://advstats.psychstat.org/.

Refereed Publications in Proceedings and Books

  1. *Qu, W. & Zhang, Z. (2020). An application of aspect-based sentiment analysis on teaching evaluation. New Developments in Data Science and Data Analytics: Proceedings of the 2019 Meeting of the International Society for Data Science and Analytics. Granger:  ISDSA Press.
  2. *Qu, W., *Liu, H., & Zhang, Z. (in press).  Permutation Test on Logistic Regression Coefficients with Social Net-work Data. Quantitative Psychology, Springer Proceedings in Mathematics & Statistics. New York:  Springer.
  3. Zhang, Z., +Ye, M., +Huang, Y., & +Sun, N. (2018). A longitudinal social network clustering method based on tie strength. Proceedings of 2018 IEEE international conference on big data (pp. 1690–1697).
  4. Zhang, Z., & *Liu, H. (2018). Sample size and measurement occasion planning for latent change score models through Monte Carlo simulation. In E. Ferrer, S. M. Boker, and K. J. Grimm (Eds.), Advances in longitudinal models for multivariate psychology: A festschrift for Jack McArdle (pp. 189–211). New York, NY: Routledge.
  5. ^Mai, Y., & Zhang, Z. (2017). 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 psychologyThe 81st annual meeting of the psychometric society (pp. 381–393). Springer Proceedings in Mathematics & Statistics. New York, NY: Springer.
  6. *Du, H., Zhang, Z., & Yuan, K.-H. (2017). 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 psychologyThe 81st annual meeting of the psychometric society (pp. 373–380). Springer Proceedings in Mathematics & Statistics. New York, NY: Springer.
  7. 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 researchThe 79th annual meeting of the psychometric society (pp. 341–355). Springer Proceedings in Mathematics & Statistics. New York, NY: Springer.
  8. *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 researchThe 79th annual meeting of the psychometric society (pp. 357–370). Springer Proceedings in Mathematics & Statistics. New York, NY: Springer.
  9. *Lu, Z., Zhang, Z., & Cohen, A. (2015). 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 researchThe 78th annual meeting of the psychometric society (pp. 319–341).Springer Proceedings in Mathematics & Statistics.  New York, NY: Springer.
  10. *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 (pp. 275–304). Springer Proceedings in Mathematics & Statistics. New York, NY: Springer.
  11. 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 York, NY: Lawrence Erlbaum Associates.
  12. 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 York, NY: Lawrence Erlbaum Associates.
  13. 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.

Encyclopedia Entries

  1. *Liu, H., & Zhang, Z. (2018). Probit transformation. The SAGE encyclopedia of educational research, measurement, and evaluation (p. 1300). Thousand Oaks, CA: Sage.
  2. Zhang, Z. (2018). Moments of a Distribution. The SAGE encyclopedia of educational research, measurement, and evaluation (p. 1084–1085). Thousand Oaks, CA: Sage.
  3. *Cain, M., & Zhang, Z. (2018). Posterior. The SAGE encyclopedia of educational research, measurement, and evaluation (p. 1274–1275). Thousand Oaks, CA: Sage.

Book Review

  1. Zhang, Z. (2018). Psychometrics from a Bayesian perspective: A review of Bayesian Psychometric Modeling (Levy & Mislevy, 2016). Journal of Educational and Behavioral Statistics, 43(4), 502–505. https://doi.org/10.3102/1076998618778011

Software Development

  1. +Xu, J., Zhang, Z., & *Qu, W. (2018). webnetvis: Interactive network visualization online [Computer software]. Retrieved from https://webnetvis.psychstat.org.
  2. *Wen, Q., *Liu, H., & Zhang, Z. (2018). mnormr: An R package for multivariate non-normal data generation [Computer software]. Retrieved from https://gitlab.psychstat.org.
  3. Zhang, Z., & +Keenan, A. (2017). WebPower: An Android app for statistical power analysis [Computer software]. Retrieved from https://play.google.com/store/apps/details?id=org.psychstat.webpower.
  4. Zhang, Z., Yuan, K.-H., & ^Mai, Y. (2018). WebPower: An R package for statistical power analysis [Computer software]. Retrieved from https://CRAN.R-project.org/package=WebPower. (Installed more than 3,000 times from May 2018 to May 2019)
  5. Zhang, Z., Yuan, K.-H., & *Cain, M. (2016). Software for estimating univariate and multivariate skewness and kurtosis [Computer software]. Retrieved from http://psychstat.org/nonnormal.                                            
  6. *Ke, Z., & Zhang, Z. (2016). pautocorr: Testing autocorrelation and partial autocorrelation through bootstrap and surrogate methods [Computer software]. Retrieved from https://r-forge.r-project.org.
  7. *Liu, H., & Zhang, Z. (2016). logistic4p: Logistic regression with misclassification in dependent variables [Computer software]. Retrieved from https://r-forge.r-project.org.
  8. ^Mai, Y., Zhang, Z., & Yuan, K.-H. (2015). An online interface for drawing path diagrams for structural equation modeling [Computer software]. Retrieved from http://semdiag.psychstat.org.
  9. Zhang, Z., Yuan, K.-H., & ^Mai, Y. (2015-2018). WebPower: Statistical power analysis online [Computer software]. Retrieved from http://webpower.psychstat.org.
  10. Zhang, Z., & Yuan, K.-H. (2015). coefficientalpha: Robust Cronbach's alpha and McDonald's omega for non-normal and missing data [Computer software]. Retrieved from https://CRAN.R-project.org/package=coefficientalpha.
  11. Zhang, Z. (2014-2018). WebBUGS: Conducting Bayesian analysis online [Computer software]. Retrieved from http://webbugs.psychstat.org.
  12. Zhang, Z., Jiang, J., & Liu, H. (2013). An online software for meta-analysis of correlation [Computer software]. Retrieved from http://webbugs.psychstat.org/modules/metacorr/.
  13. Zhang, Z., McArdle, J. J., Hamagami, F., & Grimm, K. J. (2013). RAMpath: Structural equation modeling using RAM notation [Computer software]. Retrieved from https://CRAN.R-project.org/package=RAMpath.
  14. Zhang, Z. & Yuan, K.-H. (2012-2018). WebSEM: Conducting SEM analysis online [Computer software]. Retrieved from https://websem.psychstat.org.
  15. Yuan, K.-H.  & Zhang, Z. (2011). rsem: An R package for robust structural equation modeling with non-normal and missing data [Computer software]. Retrieved from  https://CRAN.R-project.org/package=rsem.
  16. Zhang, Z. & Yuan, K.-H. (2011). semdiag: An R package for structural equation modeling diagnostics [Computer software]. Retrievable from  https://CRAN.R-project.org/package=semdiag.
  17. Zhang, Z., & Wang, L. (2011). bmem: An R packages for mediation analysis with ignorable and non-ignorable missing data [Computer software]. Retrieved from https://CRAN.R-project.org/package=bmem.
  18. Zhang, Z., & Wang, L. (2009). SAS macros for power analysis of growth curve models [Computer software]. Retrievable from http://saspower.psychstat.org.
  19. Zhang, Z., & Wang, L. (2008). BAUW as an OpenBUGS plugin [Computer software]. Retrievable from http://bauw.psychstat.org.
  20. Zhang, Z., McArdle, J. J., Wang, L., & Hamagami, F. (2008). SAS scripts for Bayesian analysis with WinBUGS [Computer software]. Retrieved from http://www.psychstat.org/us/sort.php/25.htm.
  21.  Zhang, Z., & Wang, L. (2007). MedCI: Mediation confidence intervals [Computer software]. Retrieved from http://www.psychstat.org/us/sort.php/31.htm.
  22. Zhang, Z., & Wang, L. (2006). BAUW: Bayesian analysis using WinBUGS [Computer software]. Retrieved from http://bauw.psychstat.org.
  23. Zhang, Z. (2006). LDSM: A C++ program for generating codes for analyzing latent difference score model in Mplus [Computer software]. Retrieved from http://www.psychstat.org/us/article.php/38.
  24. Zhang, Z., & Nesselroade, J. R. (2005). Selection: A C++ program for analyzing selection effects [Computer software]. Retrieved from http://www.psychstat.org/us/article.php/64.
  25. Zhang, Z., & Nesselroade, J. R. (2004). DFA: Dynamic factor analysis [Computer software]. Retrieved from http://dfa.psychstat.org.

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