View Full Information | Combine by year | Order by year | Order by author | Default

Personal Information
Johnny Zhang
Professor
University of Notre Dame
zhiyongzhang@nd.edu
https://bigdatalab.nd.edu
Education
[1]2008 Ph.D. in Psychology from University of Virginia.
Work Experience
[2]2020 - Now, Professor, Big Data Lab, University of Notre Dame.
[1]2015 - 2020, Associate Professor, Big Data Lab, University of Notre Dame.
Professional Services
[6]2024 - Now, Editorial Board, Computation.
[5]2021 - Now, Editor, Journal of Behavioral Data Science.
[4]2020 - Now, Associate Editor (Editorial Board), Neurocomputing.
[3]2018 - 2024, President, International Society for Data Science and Analytics.
[2]2016 - 2025, Associate Editor, Multivariate Behavioral Research.
[1]2014 - Now, Consulting Editor (Editorial Board), Psychological Methods.
Awards
[7]2025, Best Paper Award, International Society for Data Science and Analytics.
[6]2024, Jacob Cohen Award for Distinguished Contributions to Teaching and Mentoring, Division 5, American Psychological Association.
[5]2023, Joyce Award for Excellence in Undergraduate Teaching, University of Notre Dame.
[4]2019, Fellow, Division 5, American Psychological Association.
[3]2019, Tanaka Award for Best Article in Multivariate Behavioral Research, Society of Multivariate Experimental Psychology.
[2]2018, Early Career Research Award, Society of Multivariate Experimental Psychology.
[1]2016, Elected Member, Society of Multivariate Experimental Psychology.
Grants
[4] Zhang, Z. (2024-2027, Co-PI). Bayesian Longitudinal Data Modeling in Education Sciences. PI: Cynthia Tong. Co-I: Han Du, Laura Lu, Qian Zhang, Zhiyong Zhang. Amount: $792,636. Institute of Education Sciences.
[3] Zhang, Z. (2021-2025, PI). Methods and Software for Handling Network Data and Text Data in Structural Equation Modeling. PI: Zhiyong Zhang. Co-I: Ke-Hai Yuan and Lijuan Wang. Amount: $861,354. Institute of Education Sciences.
[2] Zhang, Z. (2015-2018, Co-PI). Structural Equation Modeling with Small N and Large p. PI: Ke-Hai Yuan. Co-I: Zhiyong Zhang. Amount: $430,725. National Science Foundation.
[1] Zhang, Z. (2014-2018, PI). A General Framework for Statistical Power Analysis with Non-normal and Missing Data through Monte Carlo Simulation. PI: Zhiyong Zhang. Co-I: Ke-Hai Yuan. Amount: $573,097. Institute of Education Sciences.
Books
[4] Jacobucci, R. , Grimm, K. J. and Zhang, Z. (2023). Machine learning for social and behavioral research. New York, NY: Guilford.
[3] Zhang, Z. , Yuan, K.-H., Wen, Y. and Tang, J. (2020). New developments in data science and data analytics. ISDSA Press. https://doi.org/10.35566/isdsa2019
[2] Zhang, Z. and Yuan, K.-H. (2018). Practical statistical power analysis using Webpower and R. ISDSA Press. https://doi.org/10.35566/power
[1] Zhang, Z. and Wang, L. (2017). Advanced statistics using R. ISDSA Press. https://doi.org/10.35566/advstats
Book Chapters
[13] Cain, M. K. and Zhang, Z. (2018). Posterior distribution. In The SAGE Encyclopedia of Educational Research, Measurement, and Evaluation. SAGE Publications, Inc.. https://doi.org/10.4135/9781506326139.n528
[12] Liu, H. and Zhang, Z. (2018). Probit transformation. In The SAGE Encyclopedia of Educational Research, Measurement, and Evaluation. SAGE Publications, Inc.. https://doi.org/10.4135/9781506326139.n541
[11] Zhang, Z. (2018). Moments of a distribution. In The SAGE Encyclopedia of Educational Research, Measurement, and Evaluation. SAGE Publications, Inc.. https://doi.org/10.4135/9781506326139.n441
[10] Zhang, Z. and Liu, H. (2018). Sample size and measurement occasion planning for latent change score models through monte carlo simulation 1. In Longitudinal multivariate psychology (pp. 189–211). Routledge.
[9] Du, H. , Zhang, Z. and Yuan, K.-H. (2017). Power analysis for t-test with non-normal data and unequal variances. In Quantitative Psychology (pp. 373–380). Springer International Publishing. https://doi.org/10.1007/978-3-319-56294-0_32
[8] Mai, Y. and Zhang, Z. (2017). Statistical power analysis for comparing means with binary or count data based on analogous anova. In Quantitative Psychology (pp. 381–393). Springer International Publishing. https://doi.org/10.1007/978-3-319-56294-0_33
[7] Lu, Z. , Zhang, Z. and Cohen, A. (2015). Model selection criteria for latent growth models using bayesian methods. In Quantitative Psychology Research (pp. 319–341). Springer International Publishing. https://doi.org/10.1007/978-3-319-07503-7_21
[6] Lu, Z. and Zhang, Z. (2015). Issues in aggregating time series: Illustration through an ar(1) model. In Quantitative Psychology Research (pp. 357–370). Springer International Publishing. https://doi.org/10.1007/978-3-319-19977-1_25
[5] Zhang, Z. , Wang, L. and Tong, X. (2015). Mediation analysis with missing data through multiple imputation and bootstrap. In Quantitative Psychology Research (pp. 341–355). Springer International Publishing. https://doi.org/10.1007/978-3-319-19977-1_24
[4] Lu, Z. , Zhang, Z. and Cohen, A. (2013). Bayesian methods and model selection for latent growth curve models with missing data. In New Developments in Quantitative Psychology (pp. 275–304). Springer New York. https://doi.org/10.1007/978-1-4614-9348-8_18
[3] Hamagami, F. , Zhang, Z. J. and McArdle, J. J. (2011). A bayesian discrete dynamic system by latent difference score structural equations models for multivariate repeated measures data. In Statistical Methods for Modeling Human Dynamics (pp. 319–348). Routledge.
[2] Wang, L. , Zhang, Z. and Estabrook, R. (2011). Longitudinal mediation analysis of training intervention effects. In Statistical Methods for Modeling Human Dynamics (pp. 349–380). Routledge.
[1] Zhang, Z. and Wang, L. (2008). Methods for evaluating mediation effects: Rationale and comparison. In New trends in psychometrics (Vol. 595) (pp. 604). Universal Academy Press Tokyo.
Refereed Articles
[90] Wyman, A. and 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
[89] Yuan, K.-H. and 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
[88] Zhang, L. , Qu, W. and 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
[87] Shao, S. (S. J. ), Xu, Z. , Liu, Q. , McClure, K. , Jacobucci, R. , Maxwell, S. E. and Zhang, Z. (2025). 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
[86] Xu, Z. and Zhang, Z. (2025). Structural equation models with social networks. Structural Equation Modeling: A Multidisciplinary Journal, 1–10. https://doi.org/10.1080/10705511.2025.2488030
[85] Wyman, A. and 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
[84] Xu, Z. , Gao, F. , Fa, A. , Qu, W. and Zhang, Z. (2024). Statistical power analysis and sample size planning for moderated mediation models. Behavior Research Methods, 56(6), 6130–6149. https://doi.org/10.3758/s13428-024-02342-2
[83] Liu, X. , Zhang, Z. and Wang, L. (2024). Detecting mediation effects with the bayes factor: Performance evaluation and tools for sample size determination. Psychological Methods. https://doi.org/10.1037/met0000670
[82] Liu, X. , Zhang, Z. and Wang, L. (2024). Detecting mediation effects with the bayes factor: Performance evaluation and tools for sample size determination. Psychological Methods. https://doi.org/10.1037/met0000670
[81] Yuan, K.-H., Ling, L. and 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
[80] Yuan, K.-H. and 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
[79] Yuan, K.-H., Zhang, Z. and 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
[78] Liu, X. , Zhang, Z. , Valentino, K. and Wang, L. (2023). 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
[77] Zhao, S. , Zhang, Z. and Zhang, H. (2023). 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
[76] Wyman, A. and 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), 1–11. https://doi.org/10.35566/jbds/v3n1/wyman
[75] Wilcox, K. T. , Jacobucci, R. , Zhang, Z. and 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
[74] Zhang, L. , Li, X. and Zhang, Z. (2023). Variety and mainstays of the r developer community. The R Journal, 15(3), 5–25. https://doi.org/10.32614/rj-2023-060
[73] Xu, Z. , Hai, J. , Yang, Y. and Zhang, Z. (2022). Comparison of methods for imputing social network data. Journal of Data Science, 599–618. https://doi.org/10.6339/22-jds1045
[72] Liu, X. , Zhang, Z. and Wang, L. (2022). Bayesian hypothesis testing of mediation: Methods and the impact of prior odds specifications. Behavior Research Methods, 55(3), 1108–1120. https://doi.org/10.3758/s13428-022-01860-1
[71] Lu, L. and 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
[70] Mai, Y. , Xu, Z. , Zhang, Z. and Yuan, K.-H. (2022). 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
[69] Liu, H. , Qu, W. , Zhang, Z. and Wu, H. (2022). A new bayesian structural equation modeling approach with priors on the covariance matrix parameter. Journal of Behavioral Data Science, 2(2), 1–24. https://doi.org/10.35566/jbds/v2n2/p2
[68] Zhang, Z. (2021). A note on wishart and inverse wishart priors for covariance matrix. Journal of Behavioral Data Science, 1(2). https://doi.org/10.35566/jbds/v1n2/p2
[67] Liu, H. , Jin, I. H. , Zhang, Z. and 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
[66] Lu, Z. and 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
[65] Zhang, Z. and 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
[64] Liu, H. and 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
[63] Krettenauer, T. , Lefebvre, J. P. , Hardy, S. A. , Zhang, Z. and Cazzell, A. R. (2021). Daily moral identity: Linkages with integrity and compassion. Journal of Personality, 90(5), 663–674. https://doi.org/10.1111/jopy.12689
[62] Kuang, Y. , Zhang, Z. J. , Duan, B. and 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
[61] Che, C. , Jin, I. H. and Zhang, Z. (2020). Network mediation analysis using model-based eigenvalue decomposition. Structural Equation Modeling: A Multidisciplinary Journal, 28(1), 148–161. https://doi.org/10.1080/10705511.2020.1721292
[60] Yuan, K.-H., Zhang, Z. and Deng, L. (2019). Fit indices for mean structures with growth curve models. Psychological Methods, 24(1), 36–53. https://doi.org/10.1037/met0000186
[59] Du, H. , Edwards, M. C. and 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
[58] Qu, W. , Liu, H. and Zhang, Z. (2019). A method of generating multivariate non-normal random numbers with desired multivariate skewness and kurtosis. Behavior Research Methods, 52(3), 939–946. https://doi.org/10.3758/s13428-019-01291-5
[57] Tong, X. and Zhang, Z. (2019). Robust bayesian approaches in growth curve modeling: Using student’stdistributions versus a semiparametric method. Structural Equation Modeling: A Multidisciplinary Journal, 27(4), 544–560. https://doi.org/10.1080/10705511.2019.1683014
[56] Wilcox, K. T. , Jacobucci, R. and Zhang, Z. (2019). Bayesian supervised topic modeling with covariates. Multivariate Behavioral Research, 55(1), 141–141. https://doi.org/10.1080/00273171.2019.1695568
[55] Mai, Y. and Zhang, Z. (2018). Software packages for bayesian multilevel modeling. Structural Equation Modeling: A Multidisciplinary Journal, 25(4), 650–658. https://doi.org/10.1080/10705511.2018.1431545
[54] Mai, Y. , Zhang, Z. and Wen, Z. (2018). Comparing exploratory structural equation modeling and existing approaches for multiple regression with latent variables. Structural Equation Modeling: A Multidisciplinary Journal, 25(5), 737–749. https://doi.org/10.1080/10705511.2018.1444993
[53] Zhang, Z. (2018). A review of bayesian psychometric modeling. Journal of Educational and Behavioral Statistics, 43(4), 502–505. https://doi.org/10.3102/1076998618778011
[52] Cain, M. K. and Zhang, Z. (2018). Fit for a bayesian: An evaluation of ppp and dic for structural equation modeling. Structural Equation Modeling: A Multidisciplinary Journal, 26(1), 39–50. https://doi.org/10.1080/10705511.2018.1490648
[51] Liu, H. , Jin, I. H. and 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
[50] Serang, S. , Grimm, K. J. and Zhang, Z. (2018). On the correspondence between the latent growth curve and latent change score models. Structural Equation Modeling: A Multidisciplinary Journal, 26(4), 623–635. https://doi.org/10.1080/10705511.2018.1533835
[49] Zhang, Z. , Jiang, K. , Liu, H. and 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] Yuan, K.-H., Zhang, Z. and Zhao, Y. (2017). Reliable and more powerful methods for power analysis in structural equation modeling. Structural Equation Modeling: A Multidisciplinary Journal, 24(3), 315–330. https://doi.org/10.1080/10705511.2016.1276836
[47] Liu, H. and 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
[46] Ke, Z. and Zhang, Z. (J. ) (2017). 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
[45] Tong, X. and 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
[44] Cain, M. K. , Zhang, Z. and Bergeman, C. S. (2017). Time and other considerations in mediation design. Educational and Psychological Measurement, 78(6), 952–972. https://doi.org/10.1177/0013164417743003
[43] Cain, M. K. , Zhang, Z. and Yuan, K.-H. (2016). 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
[42] Zhang, Z. (2015). 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
[41] Zhang, Z. and Yuan, K.-H. (2015). 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
[40] Merluzzi, T. V. , Philip, E. J. , Zhang, Z. and Sullivan, C. (2015). Perceived discrimination, coping, and quality of life for african-american and caucasian persons with cancer. Cultural Diversity & Ethnic Minority Psychology, 21(3), 337–344. https://doi.org/10.1037/a0037543
[39] Liu, H. , Zhang, Z. and Grimm, K. J. (2015). Comparison of inverse wishart and separation-strategy priors for bayesian estimation of covariance parameter matrix in growth curve analysis. Structural Equation Modeling: A Multidisciplinary Journal, 23(3), 354–367. https://doi.org/10.1080/10705511.2015.1057285
[38] Cheung, R. Y. M. , Cummings, E. M. , Zhang, Z. and Davies, P. T. (2015). 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
[37] Zhang, Z. (2014). Webbugs: Conducting bayesian statistical analysis online. Journal of Statistical Software, 61(7). https://doi.org/10.18637/jss.v061.i07
[36] Song, H. and 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
[35] Lu, Z. (L. ) and Zhang, Z. (2014). Robust growth mixture models with non-ignorable missingness: Models, estimation, selection, and application. Computational Statistics & Data Analysis, 71, 220–240. https://doi.org/10.1016/j.csda.2013.07.036
[34] Tong, X. and Zhang, Z. (2014). Abstract: Semiparametric bayesian modeling with application in growth curve analysis. Multivariate Behavioral Research, 49(3), 299–299. https://doi.org/10.1080/00273171.2014.912928
[33] Tong, X. , Zhang, Z. and Yuan, K.-H. (2014). Evaluation of test statistics for robust structural equation modeling with nonnormal missing data. Structural Equation Modeling: A Multidisciplinary Journal, 21(4), 553–565. https://doi.org/10.1080/10705511.2014.919820
[32] Yuan, K.-H., Tong, X. and Zhang, Z. (2014). Bias and efficiency for sem with missing data and auxiliary variables: Two-stage robust method versus two-stage ml. Structural Equation Modeling: A Multidisciplinary Journal, 22(2), 178–192. https://doi.org/10.1080/10705511.2014.935750
[31] Zhang, Z. , Hamagami, F. , Grimm, K. J. and McArdle, J. J. (2014). Using r package rampath for tracing sem path diagrams and conducting complex longitudinal data analysis. Structural Equation Modeling: A Multidisciplinary Journal, 22(1), 132–147. https://doi.org/10.1080/10705511.2014.935257
[30] Bernard, K. , Peloso, E. , Laurenceau, J. ‐P. , Zhang, Z. and Dozier, M. (2014). Examining change in cortisol patterns during the 10‐week transition to a new child‐care setting. Child Development, 86(2), 456–471. https://doi.org/10.1111/cdev.12304
[29] Hardy, S. A. , Zhang, Z. , Skalski, J. E. , Melling, B. S. and Brinton, C. T. (2014). Daily religious involvement, spirituality, and moral emotions. Psychology of Religion and Spirituality, 6(4), 338–348. https://doi.org/10.1037/a0037293
[28] Serang, S. , Zhang, Z. , Helm, J. , Steele, J. S. and Grimm, K. J. (2014). Evaluation of a bayesian approach to estimating nonlinear mixed-effects mixture models. Structural Equation Modeling: A Multidisciplinary Journal, 22(2), 202–215. https://doi.org/10.1080/10705511.2014.937322
[27] Grimm, K. , Zhang, Z. , Hamagami, F. and Mazzocco, M. (2013). Modeling nonlinear change via latent change and latent acceleration frameworks: Examining velocity and acceleration of growth trajectories. Multivariate Behavioral Research, 48(1), 117–143. https://doi.org/10.1080/00273171.2012.755111
[26] Zhang, Z. , Lai, K. , Lu, Z. and Tong, X. (2013). Bayesian inference and application of robust growth curve models using student’stdistribution. Structural Equation Modeling: A Multidisciplinary Journal, 20(1), 47–78. https://doi.org/10.1080/10705511.2013.742382
[25] Zhang, Z. and Wang, L. (2013). Methods for mediation analysis with missing data. Psychometrika, 78(1), 154–184. https://doi.org/10.1007/s11336-012-9301-5
[24] Grimm, K. J. , Kuhl, A. P. and Zhang, Z. (2013). Measurement models, estimation, and the study of change. Structural Equation Modeling: A Multidisciplinary Journal, 20(3), 504–517. https://doi.org/10.1080/10705511.2013.797837
[23] 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
[22] Zhang, Z. (2013). 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
[21] Philip, E. J. , Merluzzi, T. V. , Zhang, Z. and Heitzmann, C. A. (2012). 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
[20] Zhang, Z. , McArdle, J. J. and 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
[19] Tong, X. and Zhang, Z. (2012). Diagnostics of robust growth curve modeling using student’stdistribution. Multivariate Behavioral Research, 47(4), 493–518. https://doi.org/10.1080/00273171.2012.692614
[18] Zhang, Z. and Wang, L. (2012). A note on the robustness of a full bayesian method for nonignorable missing data analysis. Brazilian Journal of Probability and Statistics, 26(3). https://doi.org/10.1214/10-bjps132
[17] Yuan, K.-H. and 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
[16] Yuan, K.-H. and Zhang, Z. (2012). Structural equation modeling diagnostics using r package semdiag and eqs. Structural Equation Modeling: A Multidisciplinary Journal, 19(4), 683–702. https://doi.org/10.1080/10705511.2012.713282
[15] Wang, L. and Zhang, Z. (2011). Estimating and testing mediation effects with censored data. Structural Equation Modeling: A Multidisciplinary Journal, 18(1), 18–34. https://doi.org/10.1080/10705511.2011.534324
[14] Lu, Z. L. , Zhang, Z. and 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
[13] Hardy, S. A. , White, J. A. , Zhang, Z. and 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
[12] Zhang, Z. , Browne, M. W. and Nesselroade, J. R. (2011). Higher-order factor invariance and idiographic mapping of constructs to observables. Applied Developmental Science, 15(4), 186–200. https://doi.org/10.1080/10888691.2011.618099
[11] Tong, X. , Zhang, Z. and 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
[10] Lu, Z. L. , Zhang, Z. J. and Lubke, G. (2010). Abstract: Bayesian inference for growth mixture models with nonignorable missing data. Multivariate Behavioral Research, 45(6), 1028–1029. https://doi.org/10.1080/00273171.2010.534381
[9] Winter, W. C. , Hammond, W. R. , Green, N. H. , Zhang, Z. and Bliwise, D. L. (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
[8] Zhang, Z. and 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
[7] Hamaker, E. L. , Zhang, Z. and van der Maas, H. L. J. (2009). Using threshold autoregressive models to study dyadic interactions. Psychometrika, 74(4), 727–745. https://doi.org/10.1007/s11336-009-9113-4
[6] Zhang, Z. , Hamaker, E. L. and Nesselroade, J. R. (2008). Comparisons of four methods for estimating a dynamic factor model. Structural Equation Modeling: A Multidisciplinary Journal, 15(3), 377–402. https://doi.org/10.1080/10705510802154281
[5] Wang, L. , Zhang, Z. , McArdle, J. J. and Salthouse, T. A. (2008). Investigating ceiling effects in longitudinal data analysis. Multivariate Behavioral Research, 43(3), 476–496. https://doi.org/10.1080/00273170802285941
[4] Zhang, Z. , McArdle, J. J. , Wang, L. and Hamagami, F. (2008). A sas interface for bayesian analysis with winbugs. Structural Equation Modeling: A Multidisciplinary Journal, 15(4), 705–728. https://doi.org/10.1080/10705510802339106
[3] Zhang, Z. , Davis, H. P. , Salthouse, T. A. and Tucker-Drob, E. M. (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
[2] Zhang, Z. , Hamagami, F. , Lijuan Wang, L. , Nesselroade, J. R. and Grimm, K. J. (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
[1] Zhang, Z. and Nesselroade, J. R. (2007). Bayesian estimation of categorical dynamic factor models. Multivariate Behavioral Research, 42(4), 729–756. https://doi.org/10.1080/00273170701715998
Refereed Conference Papers
[2] Qu, W. and Zhang, Z. (2020). An application of aspect-based sentiment analysis on teaching evaluation. In New Developments in Data Science and Data Analytics (pp. 89–104). ISDSA Press. https://doi.org/10.35566/isdsa2019c6
[1] Zhang, Z. , Ye, M. , Huang, Y. and Sun, N. (2018). A longitudinal social network clustering method based on tie strength. In 2018 IEEE International Conference on Big Data (Big Data) (pp. 1691–1698). IEEE. https://doi.org/10.1109/bigdata.2018.8621925
Other Publications
[7] Ming, S. , Zhang, H. , Zhang, Z. and Wang, L. (2022, apr). Bmemlavaan: Mediation analysis with missing data and non-normal data. The R Foundation. https://doi.org/10.32614/cran.package.bmemlavaan
[6] Zhang, Z. and Qu, W. (2021, feb). Kurtosis. Oxford University Press. https://doi.org/10.1093/obo/9780199828340-0276
[5] Qu, W. and Zhang, Z. (2020, feb). Mnonr: A generator of multivariate non-normal random numbers. The R Foundation. https://doi.org/10.32614/cran.package.mnonr
[4] Zhang, Z. and Mai, Y. (2018, apr). Webpower: Basic and advanced statistical power analysis. The R Foundation. https://doi.org/10.32614/cran.package.webpower
[3] Zhang, Z. , McArdle, J. J. , Hamagami, F. and Grimm, K. J. (2012, oct). RAMpath: Structural equation modeling using the reticular action model (RAM) notation. The R Foundation. https://doi.org/10.32614/cran.package.rampath
[2] Zhang, Z. and Wang, L. (2011, apr). Bmem: Mediation analysis with missing data using bootstrap. The R Foundation. https://doi.org/10.32614/cran.package.bmem
[1] Zhang, Z. and Yuan, K.-H. (2011, oct). Rsem: Robust structural equation modeling with missing data and auxiliary variables. The R Foundation. https://doi.org/10.32614/cran.package.rsem
Presentations
[1] Yuan, K.-H. and Zhang, Z. (2015). Methods and software for statistical power analysis with non-normal data. Symposium presented at the 27th Annual Convention of the Association for Psychological Science, New York, NY.