Zhiyong Johnny Zhang

PhD, University of Virginia
Professor of Quantitative Psychology, University of Notre Dame
Director, Lab for Big Data Methodology
Faculty Fellow, Institute for Educational Initiatives
Fellow, American Psychological Association
Editor, Journal of Behavioral Data Science
Editorial Board (Associate Editor), Neurocomputing
Editorial Board (Guest Editor), Psychological Methods

438 Corbett Family Hall
Department of Psychology
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

Curriculum Vitae

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.

• 2021 - Now: Ziqian Xu
• 2023 - Now: Austin Wyman
• 2024 - Now: Zongyu Li

• 2025    Lingbo Tong (Tenure-track assistant professor at University of Wisconsin, Madison)
• 2024    Sijing Shao (Tenure-track assistant professor at Florida International University, Co-advised with Ross Jacobucci and Guangjian Zhang)
• 2022    Tyler Wilcox (Statistical Consultant 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 (Tenured associate 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)

Google Scholar ORCID

1. Du, H., Liu, F., Zhang, Z., & Enders, C. (accepted). Demystifying Posterior Distributions: A Tutorial on Their Derivation. Multivariate Behavioral Research. https://doi.org/10.1080/00273171.2025.2570250
2. *Tong, L., & Zhang, Z. (accepted). Neural Network Analysis of Psychological Data: A Step-by-Step Guide. Multivariate Behavioral Research. https://doi.org/10.1080/00273171.2025.2587379
3. Parra, D., Zhang, Z. & Radvansky, G. A. (accepted). Should We All Just Take 10? A Meta-Analysis of Wakeful Rest. Psychonomic Bulletin & Review.
4. *Xu, Z. & Zhang, Z. (in press). Structural Equation Models with Social Networks. Structural Equation Modeling: A Multidisciplinary Journal. https://doi.org/10.1080/10705511.2025.2488030
5. *Shao, S., *Xu, Z., Liu, Q., McClure, K., Jacobucci, R., Maxwell, S. M., & Zhang, Z. (accepted). Zero inflation in intensive longitudinal data: why is it important and how should we deal with it? Psychological Methods. https://doi.org/10.1037/met0000754
6. 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
7. Wyman, A., & Zhang, Z. (2025). Evaluating the Threat of Phantom Faces in Emotion Detection AI through Simulation. Journal of Behavioral Data Science, 5(2), 1-15. https://doi.org/10.35566/jbds/wyman51
8. *Wyman, A. & Zhang, Z. (2025). A Tutorial on the Use of Artificial Intelligence Tools for Facial Emotion Recognition in R. Multivariate Behavioral Research, 60(3), 641-655. https://doi.org/10.1080/00273171.2025.2455497
9. Yuan, K.-H., & Zhang, Z. (2025). Parameterizing the LISREL Model as a Correlation Structure Model for More Efficient Parameter Estimates and More Powerful Statistical Tests. Structural Equation Modeling: A Multidisciplinary Journal, 32(3), 475-497. https://doi.org/10.1080/10705511.2025.2450323
10. *Zhang, L., *Qu, W., & Zhang, Z. (2025). Bayesian Growth Curve Modeling with Measurement Error in Time. Multivariate Behavioral Research, 60(4),748-766. https://doi.org/10.1080/00273171.2025.2473937
11. Yuan, K.-H., Ling, L., & Zhang, Z. (2024). Scale-invariance, equivariance and dependency of structural equation models. Structural Equation Modeling: A Multidisciplinary Journal, 31(6), 1027-1042. https://doi.org/10.1080/10705511.2024.2353168 Get bib
12. *Tong, L., Qu, W., & Zhang, Z. (2024). Comparison of the K1 Rule, Parallel Analysis, and the Bass- Ackward Method on Identifying the Number of Factors in Factor Analysis. Fudan Journal of the Humanities and Social Sciences, 1-28. https://doi.org/10.1007/s40647-024-00423-2
13. 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
14. 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
15. *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
16. *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
17. 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
18. *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
19. *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
20. 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
21. *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
22. 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
23. *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
24. 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
25. *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
26. *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
27. 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
28. 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
29. *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
30. 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
31. 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
32. *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
33. *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
34. *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
35. *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
36. *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
37. *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
38. 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  
39. *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
40. 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
41. *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
42. ^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
43. ^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
44. *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
45. *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
46. *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
47. 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
48. *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
49. *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
50. 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
51. *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
52. *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
53. 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
54. 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
55. 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
56. 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
57. 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
58. 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
59. 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
60. 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
61. *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
62. Zhang, Z. (2014a). WebBUGS: Conducting Bayesian analysis online. Journal of Statistical Software, 61(7), 1–30. http://doi.org/10.18637/jss.v061.i07
63. 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
64. 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
65. *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
66. *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
67. 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
68. 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:// doi.org/10.1080/10705511.2013.797837
69. 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
70. 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
71. 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
72. 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
73. 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
74. *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
75. 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
76. 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
77. 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
78. *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  
79. 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
80. 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
81. *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
82. 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
83. *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   
84. 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
85. 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
86. 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
87. 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
88. 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
89. 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
90. 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
91. 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
92. 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

1. Jacobucci, R., Grimm, K. J., & Zhang, Z. (2023). Machine Learning for social and behavioral research. New York, NY: Guilford. To order: https://www.amazon.com/Learning-Behavioral-Research-Methodology-Sciences-ebook/dp/B0C6PGD5HD
   
2. 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
   
3. 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. Free E-book:  https://bit.ly/32ybdzQ
   
4. 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/.

1. Kovač, N., Farahani, H., Zhang, Z., & Watson, P. (2026). Complex network analysis. In P. Watson, H. Farahani & T. Bezdan (Eds.), Introduction to Intricate Artificial Psychology with Python (pp. 135-175). Academic Press. https://doi.org/10.1016/B978-0-443-30248-0.00011-5
2. Farahani, H., Kovač, N., Simeunović, M., Watson, P., & Zhang, Z. (2026). Network approach in psychology. In P. Watson, H. Farahani & T. Bezdan (Eds.), Introduction to Intricate Artificial Psychology with Python (pp. 135-175). Academic Press. https://doi.org/10.1016/B978-0-443-30248-0.00016-4
3. Yuan, K.-H., & Zhang, Z. (2023). Statistical and Psychometric Properties of Three Weighting Schemes of the PLS-SEM Methodology. In Latan, H., Hair, Jr., J.F., Noonan, R. (Eds.), Partial Least Squares Path Modeling. Springer, Cham. https://doi.org/10.1007/978-3-031-37772-3_4
4. Zhang,Z., Qu, W. (2020). Kurtosis. Dana S. Dunn (Ed.) Oxford Bibliographies in Psychology. New York: Oxford University Press. https://doi.org/10.1093/obo/9780199828340-0276
5. *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. https://doi.org/10.35566/isdsa2019c6
6. *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. https://doi.org/10.1007/978-3-030-43469-4_28.
7. 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). https://doi.org/10.1109/BigData.2018.8621925
8. 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.
9. ^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. https://doi.org/10.1007/978-3-319-56294-0_33
10. *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. https://doi.org/10.1007/978-3-319-56294-0_32
11. 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. https://doi.org/10.1007/978-3-319-19977-1_24
12. *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. https://doi.org/10.1007/978-3-319-19977-1_25
13. *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. https://doi.org/10.1007/978-3-319-07503-7_21
14. *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. https://doi.org/10.1007/978-1-4614-9348-8_18
15. 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.
16. 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.
17. 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. https://doi.org/10.4135/9781506326139.n541
2. Zhang, Z. (2018). Moments of a Distribution. The SAGE encyclopedia of educational research, measurement, and evaluation (p. 1084–1085). Thousand Oaks, CA: Sage.https://doi.org/10.4135/9781506326139.n441
3. *Cain, M., & Zhang, Z. (2018). Posterior Distribution. The SAGE encyclopedia of educational research, measurement, and evaluation (p. 1274–1275). Thousand Oaks, CA: Sage. https://doi.org/10.4135/9781506326139.n528

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

1. *Tong, L., ⁺Xu, J., *Qu, W., & Zhang, Z.(2025). webnetvis: Interactive network visualization online [Computer software]. Retrieved from https://webnetvis.psychstat.org.
2. *Wen, Q., *Liu, H., & Zhang, Z. (2018). mnonr: An R package for multivariate non-normal data generation [Computer software]. Retrieved from https://cran.r-project.org/package=mnonr.
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.

1. Zhang, Z. (2025, October). Introduction to BigSEM for SEM Analysis with Text and Network Data. Presentation at the 2025 Annual Meeting of the Society of Multivariate Experimental Psychology, Notre Dame, IN.
2. Zhang, Z. (2025, August). Structural Equation Models with Social Network Data. Presentation at the 2025 Annual Convention of the American Psychological Association, Denver, CO.
3. Zhang, Z. (2025, July). Designing Experiments to Evaluate Bias in Large Language Models. Invited talk at the 2025 Annual Meeting of ISDSA, Washington, DC, USA.
4. Wyman, A., & Zhang, Z. (2025, July). Emotion Detection AI Deceived by Two-Faced Images. Invited talk at the 2025 Annual Meeting of ISDSA, Washington, DC, USA.
5. Xu, Z., & Zhang, Z. (2025, July). Analyzing the Relationships between Different Forms of Friendship Using Network-Based Methods. Invited talk at the 2025 Annual Meeting of ISDSA, Washington, DC, USA.
6. Zhang, Z. (2025, May). Utilizing Social Network Data in Psychological and Educational Research. Invited talk at the East China Normal University, Shanghai, China.
7. Wyman, A., Yu, Y., Zhang, Z., Herrington, J., Yerys, B., & Clements, C. (2025, April 30-May 3). Racial Bias in the ADOS: Comparison of Differential Item Functioning and Moderated Nonlinear Factor Analysis Among More Than 4,000 Participants from NDAR and SSC. Presented at the 2025 International Society for Autism Research Annual Meeting, Seattle, WA.
8. Wyman, A., & Zhang, Z. (2025, April 22-26). Simulation-based Power Analysis for DIF/DTF in IRT Graded Response Models. Presented at the 2025 Annual Meeting of the National Council on Measurement in Education, Denver, CO.
9. Zhang, Z. (2024, July). Introduction to an online app for SEM analysis with text data. Invited talk at the 2024 Annual Meeting of ISDSA, Vienna, Austria.
10. Zhang, Z. (2024, June). Structural Equation Modeling with Network Data. Invited talk at the 2024 ICSA Applied Statistics Symposium, Nashville, TN.
11. Zhang, Z. (2024, June). A Two-Stage Method to Utilize Text Information in Structural Equation Modeling. Invited talk at the Beijing Normal University, Beijing, China.
12. Zhang, Z. (2024, May 17). Structural Equation Modeling with Text Data. Invited talk at the Nanjing University of Posts and Telecommunications, Nanjing, China.
13. Zhang, Z. (2024, May 15). Prevalence, Influences, and Handling Methods of Non-normal Data. Invited talk at the Tsinghua University, Beijing, China.
14. Wyman, A., & Zhang, Z. (2024, May 23-26). Estimating Emotions: Comparing Cross-Sectional, Repeated-Measure, and Emotion Recognition API Approaches. To be presented at the 2024 annual convention of the Association for Psychological Science, San Francisco, CA, United States.3
15. Wyman, A., & Zhang, Z. (2024, July 21-24). How are you really feeling?: A dynamic network approach to detecting nomothetic patterns of emotion regulation ability . To be presented at the 2024 annual meeting of the International Society for Data Science and Analytics, Vienna, Austria.
16. Zhang, Z. (2024, July). New insights in longitudinal data and mediation analysis. Symposium organized at the IMPS 2024 Annual Meeting, Prague, Czech.
17. Shao, S., Xu, Z., McClure, K., Zhang, Z. (2023). Zero inflation in longitudinal data: why is it important and how should we deal with it? Presentation at the International Meeting of the Psychometric Society, College Park, MD.
18. Xu Z. & Zhang Z. (July 2023). Integrating structural equation modeling with social network analysis. Annual Meeting of International Society for Data Science and Analytics. Annual Meeting of the International Society for Data Science and Analytics, 4–6 July, Shanghai, China.
19. Tong, L. & Zhang, Z. (2023). Evaluation of the Bass-Ackward Method for Identifying the Number of Factors. Annual Meeting of the International Society for Data Science and Analytics, 4–6 July, Shanghai, China.
20. Zhang, Z. (2023, July). Social Network Analysis in the Framework of Structural Equation Modeling. Invited talk at the Nanjing University of Posts and Telecommunications, Nanjing, China.
21. Zhang, Z. (2023, July). Statistical power for linear and quadratic growth curve models with ignorable and non-ignorable missing data. Invited talk at the 2023 Annual Meeting of ISDSA, Shanghai, China.