====== Zhiyong Johnny Zhang ======
PhD, [[http://www.virginia.edu/|University of Virginia]]\\
Professor of Quantitative Psychology, [[https://nd.edu|University of Notre Dame]]\\
Director, [[https://bigdatalab.nd.edu|Lab for Big Data Methodology]] \\
Fellow, [[https://iei.nd.edu/|Institute for Educational Initiatives]] \\
Fellow, [[http://www.apa.org/|American Psychological Association]]\\
Editor, [[https://isdsa.org/jbds|Journal of Behavioral Data Science]]
\\
Associate Editor, [[https://www.tandfonline.com/toc/hmbr20/current|Multivariate Behavioral Research]]\\
Editorial Board (Associate Editor), [[https://www.journals.elsevier.com/neurocomputing|Neurocomputing]]\\
Editorial Board (Guest Editor), [[https://www.apa.org/pubs/journals/met|Psychological Methods]]


438 Corbett Family Hall\\
[[https://psychology.nd.edu/|Department of Psychology]]\\
[[https://nd.edu/|University of Notre Dame]]\\

Tel: 574-631-2902\\
Fax: 574-631-8883\\
Email: ZhiyongZhang (at) nd.edu\\
Web: https://nd.psychstat.org\\ https://bigdatalab.nd.edu

{{:research:cv.zhang.pdf|Curriculum Vitae}}
===== Research interests =====
Our Lab for Big Data Methodology aims to develop better statistical methods and software in the areas of education, health, management and psychology. Our most recent research involves the development of new methods for social network and big data analysis. Particularly, we have contributed to the areas of Bayesian methods, Network analysis, Big data analysis, Structural equation modeling, Longitudinal data analysis, Mediation analysis, and Statistical computing and programming.

===== Doctoral Students =====

==== Current students ====

  * 2020 - Now: Lingbo Tong
  * 2021 - Now: Ziqian Xu
  * 2023 - Now: Austin Wyman
  * 2024 - Now: Zongyu Li

==== Former students ====
  * 2024    Sijing Shao (Post-doc at Cornell University, Co-advised with Ross Jacobucci and Guangjian Zhang)
  * 2022    Tyler Wilcox (Post-doc at Cornell University, Co-advised with Lijuan Wang)
  * 2021    Change Che (Senior data scientist at Facebook)
  * 2021    Wen Qu (Assistant professor at the Fudan University)
  * 2018    Haiyan Liu (Tenure-track assistant professor at the University of California, Merced)
  * 2017    Meghan Cain (Now  Senior Statistician at StataCorp, co-advised with Ke-Hai Yuan)
  * 2014    Xin Tong (Tenured associate professor at the University of Virginia)
  * 2011    Zhenqiu Lu (Tenured associate professor at the University of Georgia, co-advised with Ke-Hai Yuan)

===== Publications =====
[[https://scholar.google.com/citations?user=CPT7KjUAAAAJ&hl=en|Google Scholar]] 
[[https://orcid.org/0000-0003-0590-2196|ORCID]] 
==== Journal Articles ====
  - Yuan, K.-H.,  Ling, L., & Zhang, Z. (accepted). Scale-invariance, equivariance and dependency of  structural equation models. //Structural Equation Modeling: A Multidisciplinary Journal//. https://doi.org/10.1080/10705511.2024.2353168
  - Yuan, K.-H., Zhang Z., & Wang, L. (2024). Signal-to-Noise Ratio in Estimating and Testing the Mediation Effect: Structural Equation Modeling versus Path Analysis with Weighted Composites. //Psychometrika, 89//(3), 974-1006.  https://doi.org/10.1007/s11336-024-09975-4
  - Liu, X., Zhang Z., & Wang, L. (accepted). Detecting mediation effects with the Bayes factor: Performance evaluation and tools for sample size determination. //Psychological Methods//. https://doi.org/10.1037/met0000670
  - Yuan, K.-H., & Zhang, Z. (2024). Modeling Data with Measurement Errors but without Predefined Metrics: Fact versus Fallacy. Journal of Behavioral Data Science, 4(2), 1-28. https://doi.org/10.35566/jbds/yuan
  - *Xu, Z., *Gao, F., *Fa, A., Qu, W., & Zhang, Z. (2024). Statistical Power Analysis and Sample Size Planning for Moderated Mediation Models. //Behavior Research Methods, 56//, 6130–6149. https://doi.org/10.3758/s13428-024-02342-2
  - *Zhao, S., Zhang, Z., & Zhang, H. (2024). Bayesian Inference of Dynamic Mediation Models for Longitudinal Data. //Structural Equation Modeling: A Multidisciplinary Journal, 31//(1), 14-26. https://doi.org/10.1080/10705511.2023.2230519
  - Liu, X., Zhang, Z., Valentino, K., & Wang, L. (2024). The impact of omitting confounders in parallel process latent growth curve mediation models: Three sensitivity analysis approaches. //Structural Equation Modeling: A Multidisciplinary Journal, 31//(1), 132-150. https://doi.org/10.1080/10705511.2023.2189551
  - *Zhang, L., +Li, X., & Zhang, Z. (2023). Variety and Mainstays of the R Developer Community. //R Journal, 15//(3), 5-25. https://doi.org/10.32614/RJ-2023-060
  - *Wilcox, K. T., Jacobucci, R., Zhang, Z., & Ammerman, B. A. (2023). Supervised Latent Dirichlet Allocation with Covariates: A Bayesian Structural and Measurement Model of Text and Covariates. //Psychological Methods, 28//(5), 1178–1206. https://doi.org/10.1037/met0000541
  - Liu, X., Wang, L., & Zhang, Z. (2023). Bayesian hypothesis testing of mediation: Methods and the impact of prior odds specifications. Behavior Research Methods, //55//, 1108–1120. https://doi.org/10.3758/s13428-022-01860-1 
  - *Xu, Z., *Hai, J., *Yang, Y., & Zhang, Z. (2023). Comparison of Methods for Imputing Social Network Data. Journal of Data Science, //21//(3), 599–618 https://doi.org/10.6339/22-JDS1045 
  - Wyman, A., & Zhang, Z. (2023). API Face Value: Evaluating the Current Status and Potential of Emotion Detection Software in Emotional Deficit Interventions. Journal of Behavioral Data Science, 3(1), 59–69. https://doi.org/10.35566/jbds/v3n1/wyman
  - *Mai, Y., *Xu, Z., Zhang, Z., & Yuan, K.-H. (2023). An Open Source WYSIWYG Web Application for Drawing Path Diagrams of Structural Equation Models. Structural Equation Modeling: A Multidisciplinary Journal, 30(2), 328-335. https://doi.org/10.1080/10705511.2022.2101460 
  - Krettenauer, T., Lefebvre, J. P., Hardy, S. A., Zhang, Z., & Cazzell, A. R. (2022) Daily moral identity: Linkages with integrity and compassion. Journal of Personality, 90(5), 663-674. https://doi.org/10.1111/jopy.12689 
  - *Liu, H. ., *Qu, W., Zhang, Z., & Wu, H. (2022). A New Bayesian Structural Equation Modeling Approach with Priors on the Covariance Matrix Parameter. Journal of Behavioral Data Science, 2(2), 23–46. https://doi.org/10.35566/jbds/v2n2/p2  
  - *Lu, L., & Zhang, Z. (2022). How to Select the Best Fit Model among Bayesian Latent Growth Models for Complex Data. Journal of Behavioral Data Science, 2(1), 35–58. https://doi.org/10.35566/jbds/v2n1/p2  
  - Lu, Z. (Laura)*, & Zhang, Z. (2021). Bayesian Approach to Non-ignorable Missingness in Latent Growth Models. //Journal of Behavioral Data Science, 1//(2), 1--30. [[https://doi.org/10.35566/jbds/v1n2/p1|https://doi.org/10.35566/jbds/v1n2/p1]]
  - Zhang, Z. (2021). A Note on Wishart and Inverse Wishart Priors for Covariance Matrix. //Journal of Behavioral Data Science, 1//(2), 119--126. [[https://doi.org/10.35566/jbds/v1n2/p2|https://doi.org/10.35566/jbds/v1n2/p2]]
  - *Liu, H., Jin, I.-H., Zhang, Z., & Yuan, Y. (2021). Social network mediation analysis: A latent space approach. //Psychometrika, 86//(1), 272-298. [[https://doi.org/10.1007/s11336-020-09736-z|https://doi.org/10.1007/s11336-020-09736-z]]
  - Che, C.*, Jin, I.-K., & Zhang, Z. (2021). Network Mediation Analysis Using Model-based Eigenvalue Decomposition. Structural Equation Modeling, 28(1), 148-161. [[https://doi.org/10.1080/10705511.2020.1721292|https://doi.org/10.1080/10705511.2020.1721292]]
  - 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. [[https://doi.org/10.35566/jbds/v1n1/p1|https://doi.org/10.35566/jbds/v1n1/p1]]
  - *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. [[https://doi.org/10.35566/jbds/v1n1/p3|https://doi.org/10.35566/jbds/v1n1/p3]]
  - *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|https://doi.org/10.1109/ACCESS.2020.3029090]]
  - *Tong, X., & Zhang, Z. (2020). Robust Bayesian approaches in growth curve modeling: Using Student's t distributions versus a semiparametric method. //Structural Equation Modeling, 27//(4), 544-560//.// [[https://doi.org/10.1080/10705511.2019.1683014|https://doi.org/10.1080/10705511.2019.1683014]]
  - *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|https://doi.org/10.3758/s13428-019-01291-5]]
  - *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|https://doi.org/10.1080/00273171.2019.1695568]]
  - *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|https://doi.org/10.3758/s13428-019-01262-w]]
  - 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%20%5b2017|https://doi.org/10.1080/10705511.2018.1533835]]  
  - *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|https://doi.org/10.1080/10705511.2018.1490648]]
  -  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|https://doi.org/10.1037/met0000186]]
  - *Liu, H., Jin, I. K., & Zhang, Z. (2018). Structural equation modeling of social networks: Specification, estimation, and application. //Multivariate Behavioral Research, 53//(5), 714--730. [[https://doi.org/10.1080/00273171.2018.1479629|https://doi.org/10.1080/00273171.2018.1479629]]
  - %%^%%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), 737--749. [[https://doi.org/10.1080/10705511.2018.1444993|https://doi.org/10.1080/10705511.2018.1444993]]
  - %%^%%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|https://doi.org/10.1080/10705511.2018.1431545]]
  - *Cain, M. K., Zhang, Z., & Bergeman, C. S. (2018). Time and other considerations in mediation design. //Educational and Psychological Measurement//, //78//(6), 952--972. [[https://doi.org/10.1177/0013164417743003|https://doi.org/10.1177/0013164417743003]]
  - *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|https://doi.org/10.1111/bmsp.12109]]
  - *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|https://doi.org/10.1080/00273171.2017.1374824]]
  - 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), 11988--12007. [[https://doi.org/10.1080/03610926.2017.1288251|https://doi.org/10.1080/03610926.2017.1288251]]
  - *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|https://doi.org/10.3758/s13428-016-0814-1]]
  - *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|https://doi.org/10.1007/s41237-017-0031-y]]
  - 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), 315--330. [[https://doi.org/10.1080/10705511.2016.1276836|https://doi.org/10.1080/10705511.2016.1276836]]
  - *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|https://doi.org/10.1007/s10964-015-0406-x]]
  - *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. [[https://doi.org/10.1080/10705511.2015.1057285|https://doi.org/10.1080/10705511.2015.1057285]]
  - Zhang, Z. (2016). Modeling error distributions of growth curve models through Bayesian methods. //Behavior Research Methods//, //48//(2), 427--444. [[https://doi.org/10.3758/s13428-015-0589-9|https://doi.org/10.3758/s13428-015-0589-9]]
  - 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. [[https://doi.org/10.1177/0013164415594658|https://doi.org/10.1177/0013164415594658]]
  - 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|https://doi.org/10.1080/10705511.2014.937322]]
  - 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|https://doi.org/10.1080/10705511.2014.935750]]
  - 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|https://doi.org/10.1111/cdev.12304]]
  - 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. [[https://doi.org/10.1037/a0037543|https://doi.org/10.1037/a0037543]]
  - 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|https://doi.org/10.1080/10705511.2014.935257]]
  - 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. [[http://doi.org/10.1037/a0037293|http://doi.org/10.1037/a0037293]]
  - *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|https://doi.org/10.1080/10705511.2014.919820]]
  - Zhang, Z. (2014a). WebBUGS: Conducting Bayesian analysis online. //Journal of Statistical Software//, //61//(7), 1--30. [[http://doi.org/10.18637/jss.v061.i07|http://doi.org/10.18637/jss.v061.i07]]
  - Zhang, Z. (2014b). Monte Carlo based statistical power analysis for mediation models: Methods and software. //Behavior Research Methods//, //46//(4), 1184--1198. [[https://doi.org/10.3758/s13428-013-0424-0|https://doi.org/10.3758/s13428-013-0424-0]]
  - Song, H., & Zhang, Z. (2014). Analyzing multiple multivariate time series data using multilevel dynamic factor models. //Multivariate Behavioral Research//, //49//(1), 67--77. [[https://doi.org/10.1080/00273171.2013.851018|https://doi.org/10.1080/00273171.2013.851018]]
  - *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. [[https://doi.org/10.1016/j.csda.2013.07.036|https://doi.org/10.1016/j.csda.2013.07.036]]
  - *Tong, X., & Zhang, Z. (2014). Abstract: Semiparametric Bayesian modeling with application in growth curve analysis. //Multivariate Behavioral Research//, //49//, 299--299. [[https://doi.org/10.1080/00273171.2014.912928|https://doi.org/10.1080/00273171.2014.912928]]
  - Zhang, Z. (2013). Bayesian growth curve models with the generalized error distribution. //Journal of Applied Statistics//, //40//(8), 1779--1795. [[https://doi.org/10.1080/02664763.2013.796348|https://doi.org/10.1080/02664763.2013.796348]]
  - 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://%20doi.org/10.1080/10705511.2013.797837|http:// doi.org/10.1080/10705511.2013.797837]]
  - 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. [[https://doi.org/10.1002/pon.3088|https://doi.org/10.1002/pon.3088]]
  - 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//, 117--143. [[https://doi.org/10.1080/00273171.2012.755111|https://doi.org/10.1080/00273171.2012.755111]]
  - 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. [[https://doi.org/10.1080/10705511.2013.742382|https://doi.org/10.1080/10705511.2013.742382]]
  - Zhang, Z., & Wang, L. (2013). Methods for mediation analysis with missing data. //Psychometrika//, //78//(1), 154--184. [[https://doi.org/10.1007/s11336-012-9301-5|https://doi.org/10.1007/s11336-012-9301-5]]
  - Yuan, K.-H., & Zhang, Z. (2012). Robust structural equation modeling with missing data and auxiliary variables. //Psychometrika//, //77//(4), 803--826. [[https://doi.org/10.1007/s11336-012-9282-4|https://doi.org/10.1007/s11336-012-9282-4]]
  - *Tong, X., and Zhang, Z. (2012). Diagnostics of robust growth curve modeling using Student's t distribution. //Multivariate Behavioral Research//, //47//(4), 493--518. [[https://doi.org/10.1080/00273171.2012.692614|https://doi.org/10.1080/00273171.2012.692614]] 
  - 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. [[https://doi.org/10.1080/10705511.2012.713282|https://doi.org/10.1080/10705511.2012.713282]]
  - 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.  [[https://doi.org/10.1214/10-BJPS132|https://doi.org/10.1214/10-BJPS132]]
  - 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. [[https://doi.org/10.1080/02664763.2011.644528|https://doi.org/10.1080/02664763.2011.644528]]
  - *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), 1016--1016. [[https://doi.org/10.1080/00273171.2011.636715|https://doi.org/10.1080/00273171.2011.636715]]  
  - Wang, L. & Zhang, Z. (2011). Estimating and testing mediation effects with censored data. //Structural Equation Modeling//, //18//(1), 18--34. [[http://doi.org/10.1080/10705511.2011.534324|http://doi.org/10.1080/10705511.2011.534324]]
  - 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. [[https://doi.org/10.1037/a0021600|https://doi.org/10.1037/a0021600]]
  - *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. [[https://doi.org/10.1080/00273171.2011.589261|https://doi.org/10.1080/00273171.2011.589261]]
  - 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.  [[https://doi.org/10.1080/10888691.2011.618099|https://doi.org/10.1080/10888691.2011.618099]]
  - *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|https://doi.org/10.1080/00273171.2010.534381]]   
  - 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. [[https://doi.org/10.1123/ijspp.4.3.394|https://doi.org/10.1123/ijspp.4.3.394]]
  - 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. [[https://doi.org/10.1007/s11336-009-9113-4|https://doi.org/10.1007/s11336-009-9113-4]]
  - Zhang, Z., & Wang, L. (2009). Statistical power analysis for growth curve models using SAS. //Behavior Research Methods//, //41//(4), 1083--1094. [[https://doi.org/10.3758/BRM.41.4.1083|https://doi.org/10.3758/BRM.41.4.1083]]
  - 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|https://doi.org/10.1080/10705510802154281]]
  - 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|https://doi.org/10.1080/10705510802339106]]
  - 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|https://doi.org/10.1080/00273170802285941]]
  - 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|https://doi.org/10.1016/j.lindif.2007.01.004]]
  - 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. [[https://doi.org/10.1177/0165025407077764|https://doi.org/10.1177/0165025407077764]]
  - 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|https://doi.org/10.1080/00273170701715998]]

==== Books and Monographs ====

  - Jacobucci, R., Grimm, K. J., & Zhang, Z. (2023). Machine Learning for social and behavioral research. New York, NY: Guilford.
  - 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|https://doi.org/10.35566/isdsa2019]]. To order: [[https://www.amazon.com/gp/product/1946728039|https://www.amazon.com/gp/product/1946728039]]
  - Zhang, Z., & Yuan, K.-H. (Eds.). (2018). //Practical statistical power analysis using Webpower and R//. Granger, IN: ISDSA Press. https://doi.org/10.35566/power. To order: [[https://www.amazon.com/gp/product/1946728020|https://www.amazon.com/gp/product/1946728020]]. Free E-book:  [[https://bit.ly/32ybdzQ|https://bit.ly/32ybdzQ]]
  - Zhang, Z. & Wang, L. (2017). //Advanced statistics using R//. Granger, IN: ISDSA Press. https://doi.org/10.35566/advstats. Retrievable from [[https://advstats.psychstat.org/|https://advstats.psychstat.org/]]. 

==== Refereed Publications in Proceedings and Books ====


  - Zhang,Z., Qu, W. (2020). Kurtosis. Dana S. Dunn (Ed.) //Oxford Bibliographies in Psychology//. New York: Oxford University Press.
  - *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.
  - *Qu, W., *Liu, H., & Zhang, Z. (2020).  Permutation test of regression coefficients in social network data analysis. //Quantitative Psychology. IMPS 2019. Springer Proceedings in Mathematics & Statistics//, 322. Springer, Cham. DOI:10.1007/978-3-030-43469-4 28..
  - Zhang, Z., <sup>+</sup>Ye, M., <sup>+</sup>Huang, Y., & <sup>+</sup>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).
  - 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.
  - %%^%%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 psychology//--//The 81st annual meeting of the psychometric society// (pp. 381--393). Springer Proceedings in Mathematics & Statistics. New York, NY: Springer.
  - *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 psychology//--//The 81st annual meeting of the psychometric society// (pp. 373--380). Springer Proceedings in Mathematics & Statistics. New York, NY: Springer.
  - 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// (pp. 341--355). Springer Proceedings in Mathematics & Statistics. New York, NY: Springer.
  - *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// (pp. 357--370). Springer Proceedings in Mathematics & Statistics. New York, NY: Springer.
  - *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 research//--//The 78th annual meeting of the psychometric society// (pp. 319--341).Springer Proceedings in Mathematics & Statistics.  New York, NY: Springer.
  - *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.
  - 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.
  - 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.
  - 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**

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

**Book Review**

  - 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|https://doi.org/10.3102/1076998618778011]]

==== Software Development ====

  - <sup>+</sup>Xu, J., Zhang, Z., & *Qu, W. (2018). webnetvis: Interactive network visualization online [Computer software]. Retrieved from [[https://webnetvis.psychstat.org/|https://webnetvis.psychstat.org]].
  - *Wen, Q., *Liu, H., & Zhang, Z. (2018). mnormr: An R package for multivariate non-normal data generation [Computer software]. Retrieved from https://cran.r-project.org/package=mnormt.
  - Zhang, Z., & <sup>+</sup>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|https://play.google.com/store/apps/details?id=org.psychstat.webpower]].
  - 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|https://CRAN.R-project.org/package=WebPower]]. (//Installed more than 3,000 times from May 2018 to May 2019//)
  - 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|http://psychstat.org/nonnormal]].                    
  - *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|https://r-forge.r-project.org]].
  - *Liu, H., & Zhang, Z. (2016). logistic4p: Logistic regression with misclassification in dependent variables [Computer software]. Retrieved from [[https://r-forge.r-project.org|https://r-forge.r-project.org]].
  - %%^%%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|http://semdiag.psychstat.org]].
  - Zhang, Z., Yuan, K.-H., & ^Mai, Y. (2015-2018). WebPower: Statistical power analysis online [Computer software]. Retrieved from [[http://webpower.psychstat.org|http://webpower.psychstat.org]].
  - 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|https://CRAN.R-project.org/package=coefficientalpha]].
  - Zhang, Z. (2014-2018). WebBUGS: Conducting Bayesian analysis online [Computer software]. Retrieved from [[http://webbugs.psychstat.org|http://webbugs.psychstat.org]].
  - 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/|http://webbugs.psychstat.org/modules/metacorr/]].
  - 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|https://CRAN.R-project.org/package=RAMpath]].
  - Zhang, Z. & Yuan, K.-H. (2012-2018). WebSEM: Conducting SEM analysis online [Computer software]. Retrieved from [[https://websem.psychstat.org/|https://websem.psychstat.org]].
  - 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]].
  - 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]].
  - 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|https://CRAN.R-project.org/package=bmem]].
  - Zhang, Z., & Wang, L. (2009). SAS macros for power analysis of growth curve models [Computer software]. Retrievable from [[http://saspower.psychstat.org/|http://saspower.psychstat.org]].
  - Zhang, Z., & Wang, L. (2008). BAUW as an OpenBUGS plugin [Computer software]. Retrievable from [[http://bauw.psychstat.org|http://bauw.psychstat.org]].
  - 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|http://www.psychstat.org/us/sort.php/25.htm]].
  -  Zhang, Z., & Wang, L. (2007). MedCI: Mediation confidence intervals [Computer software]. Retrieved from http://www.psychstat.org/us/sort.php/31.htm.
  -  Zhang, Z., & Wang, L. (2006). BAUW: Bayesian analysis using WinBUGS [Computer software]. Retrieved from [[http://bauw.psychstat.org|http://bauw.psychstat.org]].
  -  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|http://www.psychstat.org/us/article.php/38]].
  -  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|http://www.psychstat.org/us/article.php/64]].
  -  Zhang, Z., & Nesselroade, J. R. (2004). DFA: Dynamic factor analysis [Computer software]. Retrieved from [[http://dfa.psychstat.org|http://dfa.psychstat.org]].