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

438 (Office), 430 (Lab) 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 text data, 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.

• 2023 - Now: Austin Wyman

• 2026    Ziqian Xu (Post-doc researcher at Nanjing University of Posts and Telecommunications)
• 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 full professor at the University of Virginia)
• 2011    Zhenqiu Lu (Tenured associate professor at the University of Georgia, co-advised with Ke-Hai Yuan)

• 2025 Barbara Byrne Award for Outstanding Book on Multivariate Analysis
• 2025 Best Paper Award, International Society for Data Science and Analytics
• 2024 Jacob Cohen Award for Distinguished Contributions to Teaching and Mentoring, Division 5, American Psychological Association
• 2023 Rev. Edmund P. Joyce, C.S.C., Award for Excellence in Teaching, University of Notre Dame
• 2019 Tanaka Award for Best Article in Multivariate Behavioral Research
• 2019 Elected Fellow, Division 5, American Psychological Association
• 2018 SMEP Early Career Research Award, Society of Multivariate Experimental Psychology
• 2016 Elected member, Society of Multivariate Experimental Psychology

• 2024-Current Editorial Board, Computation
• 2021–Current Editor, Journal of Behavioral Data Science
• 2020–Current Editorial Board (Associate Editor), Neurocomputing
• 2014–Current Consulting Editor (Editorial Board), Psychological Methods
• 2016–2025 Associate Editor, Multivariate Behavioral Research

• Bayesian Longitudinal Data Modeling in Education Sciences, Institute of Education Sciences, 2024–2027, Co-PI (PI: Cynthia Tong, University of Virginia; Co-PI: Han Du, UCAL; Co-I: Laura Lu, University of Georgia, Jackie Zhang, Florida State University), $792,636.
• Methods and Software for Handling Network Data and Text Data in Structural Equation Modeling, Institute of Education Sciences, 2021–2025, PI (Co-PIs: Ke-Hai Yuan, Lijuan Wang), $861,354.
• Structural Equation Modeling with Small N and Large p, National Science Foundation, 2015–2018, Co-PI (PI: Ke-Hai Yuan, University of Notre Dame, Co-PI: Alison Cheng), $430,725.
• A General Framework for Statistical Power Analysis with Non-normal and Missing Data through Monte Carlo Simulation, Institute of Education Sciences, 2014–2018, PI (Co-PI: Ke-Hai Yuan), $573,097.

Google Scholar ORCID

1. Xiao, Y., Liu, H., & Zhang, Z. (accepted). Three-Level Vector Autoregressive Models. Psychological Methods. https://doi.org/10.1037/met0000848
2. Du, H., Liu, Y., & Zhang, Z. (accepted). Testing Group Differences in Nonlinear Growth Curve Modeling with Penalized Spline. Multivariate Behavioral Research.
3. *Xu, Z. & Zhang, Z. (accepted). Influence of the Number of Time Points and Time Interval on Network Models for Understanding Factors Related to Dynamic Friendship Formation. Biopsychosocial Science and Medicine.
4. *Wyman, A., & Zhang, Z. (accepted). Consensus Among Differential Item Functioning Effect Size Measures: A Simplified Approach to Reporting Effect Size. Educational and Psychological Measurement. https://doi.org/10.1177/00131644261458305
5. *Zhang, L., Rahal, C., Kanopka, K., Ulitzsch, E., Zhang, Z., & Domingue, B.W. (accepted). Evaluating Model Predictive Performance in Confirmatory Factor Analysis with Binary Outcomes Using the InterModel Vigorish. Multivariate Behavioral Research. https://doi.org/10.1080/00273171.2026.2645212
6. *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
7. 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
8. *Tong, L., & Zhang, Z. (2026). Neural Network Analysis of Psychological Data: A Step-by-Step Guide. Multivariate Behavioral Research, 62(1), 399-419. https://doi.org/10.1080/00273171.2025.2587379
9. Du, H., Liu, F., Zhang, Z., & Enders, C. (2026). Demystifying Posterior Distributions: A Tutorial on Their Derivation. Multivariate Behavioral Research, 62(1), 211-225. https://doi.org/10.1080/00273171.2025.2570250
10. *Gao, Z., *Tong, L., & Zhang, Z. (2026). Detecting and Evaluating Bias in Large Language Models: Concepts, Methods, and Challenges. Journal of Behavioral Data Science, 6(1), 1-68. https://doi.org/10.35566/jbds/gao
11. *Xu, Z. & Zhang, Z. (2026). Structural Equation Models with Social Networks. Structural Equation Modeling: A Multidisciplinary Journal, 33 (1), 124-133. https://doi.org/10.1080/10705511.2025.2488030
12. *Xu, Z. & Zhang, Z. (2026). Application of the Dynamic Latent Space Model to Social Networks with Time-Varying Covariates. Computation, 14(2), 34. https://doi.org/10.3390/computation14020034
13. *Parra, D., Zhang, Z. & Radvansky, G. A. (2026). Should We All Just Take 10? A Meta-Analysis of Wakeful Rest. Psychonomic Bulletin & Review, 33(49). https://doi.org/10.3758/s13423-025-02778-3
14. *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
15. *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
16. 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
17. *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
18. 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
19. *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
20. 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
21. 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
22. *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
23. *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
24. 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
25. *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
26. *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
27. 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
28. *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
29. 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
30. *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
31. 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
32. *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
33. *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
34. 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
35. 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
36. *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
37. 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
38. 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
39. *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
40. *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
41. *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
42. *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
43. *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
44. 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  
45. *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
46. 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
47. *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
48. ^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
49. ^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
50. *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
51. *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
52. *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
53. 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
54. *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
55. *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
56. 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
57. *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
58. *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
59. 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
60. 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
61. 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
62. 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
63. 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
64. 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
65. 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
66. 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
67. *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
68. Zhang, Z. (2014a). WebBUGS: Conducting Bayesian analysis online. Journal of Statistical Software, 61(7), 1–30. http://doi.org/10.18637/jss.v061.i07
69. 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
70. 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
71. *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
72. 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
73. 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
74. 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
75. 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
76. 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
77. 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
78. 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
79. *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
80. 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
81. 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
82. 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 
83. 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
84. 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
85. *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
86. 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  
87. 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
88. 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
89. 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
90. 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
91. 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
92. 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
93. 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
94. 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
95. 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. *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
2. *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
3. *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
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 

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., Simeunović, M., & Farahani, H. (Eds.) Data Science in Psychology: Using Python in Psychological Research. Springer Nature. https://link.springer.com/book/10.1007/978-3-032-18312-5
   • Kovač, N., Blagojević, M., Ratković, K., Simeunović, M., Done, L.-C. W., Zhang, Z. J., Farahani, H., & Watson, P. (2026). Datasets Preparation. Data Science in Psychology, 103–125. https://doi.org/10.1007/978-3-032-18312-5_5
   • Kovač, N., Blagojevič, M., Ratković, K., Watson, P., Zhang, Z. J., & Farahani, H. (2026). Confirmatory Factor Analysis. Data Science in Psychology, 203–226. https://doi.org/10.1007/978-3-032-18312-5_9
   • Kovač, N., Blagojević, M., Zhang, Z. J., Ratković, K., Watson, P., & Farahani, H. (2026). Time-Dependent Modelling in Psychological Data. Data Science in Psychology, 323–349. https://doi.org/10.1007/978-3-032-18312-5_14
2. P. Watson, H. Farahani & T. Bezdan (Eds.), Introduction to Intricate Artificial Psychology with Python. Academic Press. https://www.sciencedirect.com/science/book/9780443302480
   • Kovač, N., Farahani, H., Zhang, Z., & Watson, P. (2026). Complex network analysis. https://doi.org/10.1016/B978-0-443-30248-0.00011-5
   • Farahani, H., Kovač, N., Simeunović, M., Watson, P., & Zhang, Z. (2026). Network approach in psychology. Academic Press. https://doi.org/10.1016/B978-0-443-30248-0.00016-4

1. 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
2. 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
3. *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
4. *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.
5. 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
6. 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.
7. ^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
8. *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
9. 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
10. *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
11. *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
12. *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
13. 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.
14. 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.
15. 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. [Paper Presentation] Liu, M., & Zhang, Z. (2026, July 21–24). Evaluating the performance of LLM in processing bilingual data with code switching through experimental design. Paper presented at the 2026 annual International Meeting of the Psychometric Society, Seoul, South Korea.
2. [Paper Presentation] Wyman, A., & Zhang, Z. (2026, July 21–24). Nonnormality, Missing Data, and Small Samples: Challenges and Opportunities for Determining the Number of Factors using Robust SEM Fit Indices. Paper presented at the 2026 annual International Meeting of the Psychometric Society, Seoul, South Korea.
3. [Workshop] Zhang, Z. (2026, July). Invited workshop on EEG Data Analysis using R at the 2026 ISDSA Annual Meeting. Washington, DC.
4. [Paper Presentation] Zhang, Z., & Wang, L. (2026, July). Growth Curve Modeling of Censored Longitudinal Count Data. Invited talk at the 2025 Annual Meeting of ISDSA, Beijing, China.
5. [Paper Presentation] Liu, M., & Zhang, Z. (2026, July). How to simulate multilingual text from LLM with desired features? Invited talk at the 2025 Annual Meeting of ISDSA, Beijing, China.
6. [Paper Presentation] Li, C., Wyman, A., Zhang, Z., & Clements, C. (2026, April 20–26). Temporal Trends in ADOS Diagnostic Classifications over Two Decades. Presented at the 2026 International Society for Autism Research Annual Meeting, Prague, Czechia.
7. [Poster Presentation] Wyman, A., Yu, Y., Li, C., Zhang, Z., & Clements, C. (2026, April 20–26). Sex Bias in the ADOS: A Multi-Module Comparison of Differential Item Functioning Among 23,000 Participants from NDAR, SSC, SPARK, and AIC. Presented at the 2026 International Society for Autism Research Annual Meeting, Prague, Czechia.
8. [Poster Presentation] Wyman, A., & Zhang, Z. (2026, April 8–11). Consensus Among DIF Effect Size Measures in Dichotomous Item Response Theory Models. Presented at the 2026 Annual Meeting of the National Council on Measurement in Education, Los Angeles, CA.
9. [Paper Presentation] 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.
10. [Chaired Symposium] Zhang, Z. (2025, August). Structural Equation Models with Social Network Data. Symposium at the 2025 Annual Convention of APA, Denver, CO.
11. [Paper Presentation] Zhang, Z. (2025, August). Structural Equation Models with Social Network Data. Presentation at the 2025 Annual Convention of the American Psychological Association, Denver, CO.
12. [Workshop] Zhang, Z. (2025, July). Invited workshop on Structural Equation Models with Social Network Data at the 2025 ISDSA Annual Meeting. Washington, DC.
13. [Paper Presentation] 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.
14. [Paper Presentation] 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.
15. [Paper Presentation] 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.
16. [Invited Lecture] Zhang, Z. (2025, May). Utilizing Social Network Data in Psychological and Educational Research. Invited talk at the East China Normal University, Shanghai, China.
17. [Poster Presentation] 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.
18. [Poster Presentation] 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.
19. [Paper Presentation] Zhang, Z. (2024, August). Factor analysis with text data through Universal Sentence Encoder. Paper presented at the 2024 Annual Convention of the American Psychological Association, Seattle, WA.
20. [Organized Meeting] Zhang, Z., & Yuan, K.-H. (2024, July). The 2024 ISDSA Meeting on Behavioral Data Science. Vienna, Austria.
21. [Invited Lecture] 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.
22. [Paper Presentation] Zhang, Z. (2024, July). Mediation Analysis with Text Data. Paper presented at the IMPS 2024 Annual Meeting, Prague, Czech.
23. [Invited Lecture] Zhang, Z. (2024, June). Structural Equation Modeling with Network Data. Invited talk at the 2024 ICSA Applied Statistics Symposium, Nashville, TN.
24. [Invited Lecture] 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.
25. [Poster Presentation] Wyman, A., & Zhang, Z. (2024, May 23–26). Estimating Emotions: Comparing Cross-Sectional, Repeated-Measure, and Emotion Recognition API Approaches. Presented at the 2024 annual convention of the Association for Psychological Science, San Francisco, CA.
26. [Invited Lecture] Zhang, Z. (2024, May 17). Structural Equation Modeling with Text Data. Invited talk at the Nanjing University of Posts and Telecommunications, Nanjing, China.
27. [Invited Lecture] Zhang, Z. (2024, May 15). Prevalence, Influences, and Handling Methods of Non-normal Data. Invited talk at the Tsinghua University, Beijing, China.
28. [Paper Presentation] Zhang, Z. (2023, October). WebPower as an open system for statistical power analysis. Online presentation at the 2023 SMEP Meeting.
29. [Organized Meeting] Zhang, Z., & Yuan, K.-H. (2023, July). The 2023 ISDSA Meeting on Behavioral Data Science. Shanghai, China.
30. [Invited Lecture] 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.
31. [Invited Lecture] 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.
32. [Workshop] Zhang, Z. (2023, July). Invited workshop on Deep Learning Using R at the 2023 ISDSA Annual Meeting. Online.
33. [Workshop] Du, H. & Zhang, Z. (2023, May). Power Analysis. Invited workshop conducted at the 2023 Annual Convention of Association for Psychological Science, Washington DC.
34. [Paper Presentation] +Wyman, A., & Zhang, Z. (2022, October). API Face Value: Enhancing Emotional Deficit Interventions with Emotion Detection Software. Presented at the ninety-third annual conference of the Indiana Association of the Social Sciences, Gary, IN, United States.
35. [Invited Lecture] Zhang, Z. (2022, August, Chair). Methods and Applications of Network Science in Psychology. Invited symposium conducted at the 2022 Annual Convention of the American Psychological Association, Minneapolis, MN.
36. [Workshop] Tong, X., Du, H., & Zhang, Z. (2022, June). Workshop on Bayesian Longitudinal Data Modeling. Two-day workshop supported by the Association of Psychological Science.
37. [Workshop] Zhang, Z. (2022, June). Workshop on Statistical Power Analysis for Structural Equation Modeling at the 2022 ISDSA Annual Meeting. Online.
38. [Organized Meeting] Zhang, Z., & Yuan, K.-H. (2022, May). The 2022 ISDSA Meeting on Behavioral Data Science. Notre Dame, IN.
39. [Paper Presentation] Xu, Z., Hai, J., Yang, Y., & Zhang, Z. (2022, May). Comparison of Methods for Imputing Social Network Data.* Paper presented at the 2022 Annual Convention of the American Psychological Association, Minneapolis, MN.
40. [Paper Presentation] Xu, Z., Hai, J., Yang, Y., & Zhang, Z. (2022, May). Comparison of Methods for Imputing Social Network Data.* Paper presented at the 2022 Annual Meeting of the International Society for Data Science and Analytics, Notre Dame, IN, USA.
41. [Paper Presentation] Zhang, Z. (2022, May). Social Network Analysis in the Framework of Structural Equation Modeling. Paper presented at the 2022 Annual Meeting of the International Society for Data Science and Analytics, Notre Dame, IN, USA.
42. [Invited Lecture] Zhang, Z. (2022, April). Prevalence, Influences, and Handling Methods of Non-normal Data. Invited talk at the University of Southern California.
43. [Invited Lecture] Zhang, Z. (2021, November). Social Network Analysis In The Framework Of Structural Equation Modeling. Invited talk by Data Analytics Colloquium.
44. [Invited Lecture] Zhang, Z. (2021, November). What is Data Science? Invited talk by Data Science Forum.
45. [Workshop] Zhang, Z. (2021, June). Workshop on Statistical Power analysis at the 2021 ISDSA Annual Meeting. Online.
46. [Organized Meeting] Zhang, Z., & Yuan, K.-H. (2021, June). The 2021 ISDSA Meeting on Behavioral Data Science. Notre Dame, IN. (Online)
47. [Invited Lecture] Zhang, Z. (2020, November). Quantitative Psychology at the Age of Data Science. Presented to the Monday Symposium in Measurement and Statistics at University Of Maryland & the Brownbag Series of the Quantitative Psychology Program at The Ohio State University. (Online)
48. [Invited Lecture] Zhang, Z. (2020, July). Psychometric Models for Social Network Data Analysis. Invited talk at the 85th Annual Meeting of Psychometric Society. (Online)
49. [Paper Presentation] Wilcox, K. T., Jacobucci, R., and Zhang, Z. (2020, July). Combining topic modeling and regression: Supervised topic modeling with covariates.* Paper presented at the 85th Annual Meeting of Psychometric Society. (Online)
50. [Paper Presentation] Qu, W. & Zhang, Z. (2020, July). Evaluating the effect of multivariate non-normality on confirmatory factor analysis.* Paper presented at the 85th Annual Meeting of Psychometric Society. (Online)
51. [Organized Meeting] Zhang, Z., & Yuan, K.-H. (2020, May). The 2020 Annual Meeting of the International Society for Data Science and Analytics. Notre Dame, IN. (Online)
52. [Invited Lecture] Zhang, Z. (2019, October). Measure changes in networks. Cattell Award address at the Annual Meeting of the Society of Multivariate Experimental Research, Baltimore, MA.
53. [Invited Lecture] Zhang, Z. (2019, August). A comparison of machine learning methods for understanding teaching evaluation comments. Invited talk at the 2019 Global Summit on Artificial Intelligence and Big Data in Education, Beijing, China.
54. [Invited Lecture] Zhang, Z. & Liu, H. (2019, July). Social Network Analysis in the Structural Equation Modeling Framework. Invited talk at the Yangtze Normal University, Chongqing, China.
55. [Invited Lecture] Zhang, Z. & Liu, H. (2019, July). A Structural Equation Modeling Framework for Social Network Analysis. Invited talk at the University of Science and Technology of China, Hefei, China.
56. [Invited Lecture] Zhang, Z. (2019, July). Improving teaching evaluation using text mining. Invited talk at the 2019 Meeting of the International Society for Data Science and Analytics, Nanjing, China.
57. [Organized Meeting] Zhang, Z., & Yuan, K.-H. (2019, July). The 2019 Annual Meeting of the International Society for Data Science and Analytics. Nanjing, China.
58. [Workshop] Zhang, Z. (2019, July). Data mining methods for education and psychology. Workshop conducted at the 2019 Global Summit on Artificial Intelligence and Big Data in Education, Beijing, China.
59. [Paper Presentation] Qu, W., Liu, H., & Zhang, Z. (2019, July). Permutation Test on Logistic Regression Coefficients with Social Network Data.* Paper presented at the 85th Annual Meeting of Psychometric Society. Santiago, Chile.
60. [Paper Presentation] Qu, W., & Zhang, Z. (2019, July). An Application of Aspect-Based Sentiment Analysis on Teaching Evaluation.* Paper presented at the 2019 Annual Meeting of the International Society for Data Science and Analytics. Nanjing, China.
61. [Invited Lecture] Zhang, Z. (2019, March). Stones from one hill may serve to polish the jade of another: Bridging quantitative psychology and data science. Invited talk at the Pennsylvania State University, University Park, PA.
62. [Paper Presentation] Zhang, Z., +Ye, M., +Huang, Y., & +Sun, N. (2018, December). A longitudinal social network clustering method based on tie strength. Paper presented at the 2018 IEEE Big Data Conference, Seattle, WA.
63. [Invited Lecture] Zhang, Z. (2018, July). A blessing or a curse? An overview of non-normal data and missing data. Invited talk at the 2018 International Conference on Management and Operations Research, Beijing, China. [Invited keynote]
64. [Invited Lecture] Zhang, Z., +Ye, M., +Huang, Y., & +Sun, N. (2018, July). A longitudinal social network clustering method based on tie strength. Invited talk at the 8th International Forum on Statistics, Beijing, China.
65. [Poster Presentation] Zhang, Z. (2018, May). A new software program for practical statistical power analysis. Poster presented at the 30th Annual Convention of the Association for Psychological Science, San Francisco, CA.
66. [Poster Presentation] +Tzakis, T., Liu, H., & Zhang, Z. (2018, May). A review of social network analysis in psychological research.* Poster presented at the 30th Annual Convention of the Association for Psychological Science, San Francisco, CA.
67. [Poster Presentation] +Tzakis, T., & Zhang, Z. (2018, March). A review of social network analysis in psychological research. Poster presented at the 2018 Conference of Michigan Academy, Alma, MI.
68. [Paper Presentation] Zhang, Z. (2017, Oct). Two-stage Bayesian estimation in structural equation modeling. Paper presented at the 2017 meeting of the Society of Multivariate Experimental Psychology, Minneapolis, MN.
69. [Poster Presentation] Zhang, Z. (2017, August). Practical statistical power analysis for multilevel modeling: Methods and software. Poster presented at the 125th Annual Convention of the American Psychological Association, Washington DC.
70. [Invited Lecture] Zhang, Z. (2017, June). Modeling non-normal distributions in mixed-effects and multilevel models. Invited talk at the 2017 ICSA Applied Statistics Symposium, Chicago, IL.
71. [Invited Lecture] Zhang, Z. (2017, May). Statistical methods and software for handling non-normal data in social, behavioral and economic sciences. Invited talk at Henan University, Kaifeng, China.
72. [Poster Presentation] Zhang, Z., & Liu, H. (2017, May). Sample size planning for latent change score models through Monte Carlo simulation.* Poster presented at the 30th Annual Convention of the Association for Psychological Science, Boston, MA.
73. [Poster Presentation] Cain, M. K., & Zhang, Z. (2017, May). Fit for a Bayesian: An evaluation of PPP and DIC.* Poster presented at the 2017 Modern Modeling Methods Conference, Storrs, CT.
74. [Organized Meeting] Yuan, K.-H., & Zhang, Z. (2017, May). Statistics in social sciences: Present and future. Beijing, China.
75. [Invited Lecture] Zhang, Z. (2017, March). Two-stage Bayesian estimation in structural equation modeling. Invited talk at the ACMS Statistics Seminar, Department of ACMS, University of Notre Dame, Notre Dame, IN.
76. [Invited Lecture] Zhang, Z., & Liu, H. (2016, October). Sample size planning for latent change score models through Monte Carlo simulation.* Invited talk at the Conference on Advances in Longitudinal Models for Multivariate Psychology: A Festschrift for Jack McArdle, Richmond, VA.
77. [Poster Presentation] Zhang, Z. (2016, October). Practical statistical power analysis for structural equation modeling: Methods and software. Poster presented at the 87th Annual Meeting of the Indiana Academy of the Social Sciences, Westville, IN.
78. [Workshop] Zhang, Z. (2016, August). Practical statistical power analysis for simple and complex models. Workshop conducted at the 124th Annual Convention of the American Psychological Association, Denver, CO.
79. [Paper Presentation] Liu, H., & Zhang, Z. (2016, July). Logistic regression with misclassification in binary outcome variables: Method and software.* Paper presented at the Annual Meeting of the Psychometric Society, Asheville, NC.
80. [Paper Presentation] Zhang, Z. (2016, July). Statistical power analysis for mediation with non-normal and missing data. Paper presented at the Annual Meeting of the Psychometric Society, Asheville, NC.
81. [Poster Presentation] ^Mai, Y., & Zhang, Z. (2016, May). Multilevel modeling through path diagramming: An online graphical interface. Poster presented at the 28th Annual Convention of the Association for Psychological Science, Chicago, IL.
82. [Poster Presentation] Liu, H., & Zhang, Z. (2016, May). Power of logistic regression with correction of misclassifications.* Poster presented at the 28th APS Annual Convention of Association for Psychological Science, Chicago, IL.
83. [Poster Presentation] Liu, H., & Zhang, Z. (2016, May). Power of logistic regression with correlated predictors.* Poster presented at the 28th APS Annual Convention of Association for Psychological Science, Chicago, IL.
84. [Invited Lecture] Zhang, Z., & Yuan, K.-H. (2015, December). Online statistical software for simple and complex models. Invited software demonstration/tutorial at the IES PI meeting, Washington, D.C.
85. [Invited Lecture] Zhang, Z. (2015, June). Statistical power analysis for mediation effects through WebPower. Invited talk at the Renmin University of China, Beijing, China.
86. [Chaired Symposium] Zhang, Z., & Yuan, K.-H. (2015, May, Chaired Symposiums). Methods and software for statistical power analysis with non-normal data. Symposium conducted at the 27th Annual Convention of the Association for Psychological Science, New York, NY.
87. [Invited Lecture] Zhang, Z. (2015, March). Bayesian factor analysis. Invited talk at the University of Southern California, Los Angeles, CA.
88. [Invited Lecture] Zhang, Z. (2014, September). The use of relaxed and Bayesian assumptions on error terms in dynamic models of change. Invited talk at the 2014 Society for Research in Child Development themed meeting: Developmental Methodology, San Diego, CA.
89. [Paper Presentation] Lu, Z., & Zhang, Z. (2014, July). Aggregating time series: Illustration through an AR(1) model.* Paper presented at the 79th Annual Meeting of the Psychometric Society, Madison, Wisconsin.
90. [Paper Presentation] Zhang, Z., Wang, L., & Tong, X. (2014, July). Mediation analysis with missing data through multiple imputation and bootstrap.* Paper presented at the 79th Annual Meeting of the Psychometric Society, Madison, Wisconsin.
91. [Paper Presentation] Liu, H., & Zhang, Z. (2014, July). Separating-strategy priors for covariance matrices.* Paper presented at the 79th Annual Meeting of the Psychometric Society, Madison, Wisconsin.
92. [Chaired Symposium] Zhang, Z. (2014, May). New developments in Bayesian analysis. Symposium conducted at the 26th Annual Convention of the Association for Psychological Science, San Francisco, CA.
93. [Paper Presentation] Lu, Z., Zhang, Z., & Cohen, A. (2014, May). Bayesian model selection criteria for latent growth models.* Paper presented at the 26th Annual Convention of the Association for Psychological Science, San Francisco, CA.
94. [Paper Presentation] Tong, X., & Zhang, Z. (2014, May). Robust semi-parametric Bayesian methods in growth curve modeling with nonnormal data.* Paper presented at the 26th Annual Convention of the Association for Psychological Science, San Francisco, CA.
95. [Paper Presentation] Zhang, Z., Jiang, K., & Liu, H. (2014, May). Bayesian meta-analysis of correlation coefficients through power prior.* Paper presented at the 26th Annual Convention of the Association for Psychological Science, San Francisco, CA.
96. [Paper Presentation] Lu, Z., & Zhang, Z. (2014, April). Robust growth mixture models with non-ignorable missingness.* Paper presented at the 2014 Annual Meeting of National Council on Measurement in Education, Philadelphia, Pennsylvania.
97. [Paper Presentation] Liu, X., Liu, F., Simon, M., & Zhang, Z. (2014, April). Are the score gains suspicious? – A Bayesian growth analysis approach. Paper presented at the 2014 Annual Meeting of National Council on Measurement in Education, Philadelphia, Pennsylvania.
98. [Workshop] Zhang, Z., & Yuan K.-H. (2013, August). Robust SEM for non-normal and missing data using WebSEM. Workshop conducted at the 121th Annual Convention of the American Psychological Association, Washington DC.
99. [Paper Presentation] Zhang, Z., & Grimm, K. J. (2013, April). A random-coefficient latent change score model for nonlinear growth data. Paper presented at the 2013 Biennial Meeting of Society for Research in Child Development, Seattle, Washington.
100. [Paper Presentation] Lu, Z., Zhang, Z., & Cohen, A. (2012, July). Latent growth curve models with non-ignorable missing data: Bayesian inference and model selection criteria.* Paper presented at the 77th Annual International Meeting of the Psychometric Society, Lincoln, Nebraska.
101. [Invited Lecture] Yuan, K.-H., Tong, X., & Zhang, Z. (2012, July). Bias and efficiency for SEM with missing data and auxiliary variables: Robust method versus normal distribution based ML.* Invited talk at the 2nd meeting of the Institute of Mathematical Statistics Asia Pacific Rim, Tsukuba, Japan.
102. [Chaired Symposium] Zhang, Z., & Yuan, K.-H. (2012, May). Robust statistical data analysis. Symposium conducted at the 24th Annual Convention of the Association for Psychological Science, Chicago, IL.
103. [Paper Presentation] Zhang, Z., Lai, K., Lu, Z., & Tong, X. (2012, May). Bayesian robust growth curve modeling based on Student's t distribution.* Paper presented at the 24th Annual Convention of the Association for Psychological Science, Chicago IL.
104. [Paper Presentation] Yuan, K.-H., & Zhang, Z. (2012, May). Robust structural equation modeling with missing data and auxiliary variables. Paper presented at the 24th Annual Convention of the Association for Psychological Science, Chicago IL.
105. [Paper Presentation] Tong, X., Zhang, Z., & Yuan, K.-H. (2012, May). Evaluation of fit statistics for robust SEM with non-normal missing data.* Paper presented at the 24th Annual Convention of the Association for Psychological Science, Chicago IL.
106. [Paper Presentation] Lu, Z., & Zhang, Z. (2012, May). Robust growth mixture modeling using Bayesian methods.* Paper presented at the 24th Annual Convention of the Association for Psychological Science, Chicago IL.
107. [Paper Presentation] Lu, Z., Zhang, Z., & Cohen, A. (2012, April). Latent growth curve models with non-ignorable missing data: Bayesian inference and model selection criteria.* Paper presented at the 2012 Annual Meeting of the National Council on Measurement in Education (NCME), Vancouver, BC, Canada.
108. [Paper Presentation] Zhang, Z. & Lu, Z. (2012, February). Issues in aggregating time series: Illustration through an AR(1) model.* Paper presented at the 2012 Society for Research in Child Development Themed Meeting: Developmental Methodology, Tampa, Florida.
109. [Invited Lecture] Lu, Z., Zhang, Z., & Lubke, G. (2012, January). Bayesian inference for growth mixture models with latent class dependent missing data.* Invited talk at the Hong Kong Institute of Education, Hong Kong, China.
110. [Paper Presentation] Zhang, Z., & Wang, L. (2011, August). Overview of full Bayesian analysis of non-ignorable missing data. Paper presented at the 119th Annual Convention of the American Psychological Association, Washington DC.
111. [Paper Presentation] Lu, Z., Zhang, Z., & Lubke, G. (2011, August). Bayesian inference for growth mixture models with non-ignorable missing data.* Paper presented at the 119th Annual Convention of the American Psychological Association, Washington DC.
112. [Paper Presentation] Wang, L. & Zhang, Z. (2011, August). Bayesian estimation and inference on mediation effects with censored data. Paper presented at the 119th Annual Convention of the American Psychological Association, Washington DC.
113. [Paper Presentation] Tong, X., & Zhang, Z. (2011, August). Bayesian inference for robust growth curve modeling using t distributions.* Paper presented at the 119th Annual Convention of the American Psychological Association, Washington DC.
114. [Chaired Symposium] Zhang, Z. (2011, August). Bayesian methods for non-normal and non-ignorable missing data analysis. Symposium conducted at the 119th Annual Convention of the American Psychological Association, Washington DC.
115. [Paper Presentation] Lu, Z., Zhang, Z., & Lubke, G. (2011, July). Bayesian inference for growth mixture models with latent class dependent missing data.* Paper presented at the 76th Annual International Meeting of the Psychometric Society, Hong Kong, China.
116. [Invited Lecture] Zhang, Z. (2011, June). Introduction to Bayesian analysis. Invited lecture at the Renmin University of China, Beijing, China.
117. [Invited Lecture] Zhang, Z., McArdle, J. J., & Nesselroade, J. R. (2011, May). Growth rate models: Emphasizing growth rate analysis through growth curve modeling. Invited talk at the Nesselroade Festschrift, Charlottesville, VA.
118. [Paper Presentation] Lu, Z., Zhang, Z., & Lubke, G. (2010, September). Bayesian inference for growth mixture models with non-ignorable missing data.* Paper presented at the Annual Society of Multivariate Experimental Psychology Graduate Student Pre-conference, Atlanta, GA.
119. [Poster Presentation] Zhang, Z., & Wang, L. (2010, August). Power analysis for linear and nonlinear growth curve modeling. Poster presented at the 118th Annual Convention of the American Psychological Association, San Diego, CA.
120. [Paper Presentation] Zhang, Z. (2010, July). Testing the invariance of latent traits in multiple group analysis. Paper presented at the 7th Conference of the International Test Commission, Hong Kong, China.
121. [Workshop] Zhang, Z. (2009, August). Introduction to Bayesian analysis. Workshop presented at the 117th Annual Convention of the American Psychological Association, Toronto, Canada.
122. [Invited Lecture] Zhang, Z. (2009, July). Bayesian analysis. Invited workshop at the University of Southern California, Los Angeles, CA.
123. [Paper Presentation] Zhang, Z. (2009, June). Bayesian SEM: Current developments and future directions. Paper presented at the 21th Annual Convention of the Association for Psychological Science, San Francisco, CA.
124. [Paper Presentation] Zhang, Z. (2007, October). Bootstrap analysis of mediation effects. Paper presented at the Annual Society of Multivariate Experimental Psychology Graduate Student Pre-conference, Chapel Hill, NC.
125. [Poster Presentation] Zhang, Z., & Wang, L. (2007, August). Bayesian analysis of longitudinal data using growth curve models. Poster presented at the 115th Annual Convention of the American Psychological Association, San Francisco, CA.
126. [Paper Presentation] Zhang, Z., & Wang, L. (2007, July). Methods evaluating mediation effect: Rationale and comparison. Paper presented at the 72nd Annual Meeting of the Psychometric Society, Tokyo, Japan.
127. [Poster Presentation] Zhang, Z., Wang, L., & Hamagami, F. (2006, August). Using WinBUGS inside SAS for Bayesian analysis. Poster presented at the 114th Annual Convention of the American Psychological Association, New Orleans, LA.
128. [Paper Presentation] Wang, L., Zhang, Z., & McArdle, J. J. (2006, June). Investigating the ceiling effects in longitudinal data analysis. Paper presented at the 71st Annual Meeting of the Psychometric Society, Montreal, Canada.
129. [Paper Presentation] Zhang, Z., Wang, L., & Nesselroade, J. R. (2006, June). Growth rate models and Bayesian estimation. Paper presented at the 71st Annual Meeting of the Psychometric Society, Montreal, Canada.
130. [Poster Presentation] Wang, L. & Zhang, Z. (2006, April). Memory training on individual learning performance for independent and vital older adults. Poster presented at the 19th Cognitive Aging Conference, Atlanta, GA.
131. [Poster Presentation] Zhang, Z., Wang, L., & Hamagami, F. (2006, April). Evaluation of the intervention of memory training on short-term learning for elderly. Poster presented at the 19th Cognitive Aging Conference, Atlanta, GA.
132. [Paper Presentation] Xin, T., Zhang, Z., & Yuan, K.-H. (2011). Evaluation of test statistics for robust structural equation modeling with non-normal missing data.* Paper presented at the Annual Society of Multivariate Experimental Psychology Graduate Student Pre-conference, Oklahoma.