Zhiyong (Johnny) Zhang

Zhiyong (Johnny) Zhang


Ph.D., University of Virginia 

  • Quantitative

(574) 631-2902


Office: 438 Corbett Family Hall
Lab: 430 Corbett Family Hall

Mailing Address:
390 Corbett Family Hall
Notre Dame, IN 46556

Lab for Big Data Methodology

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Our Lab for Big Data Methodology aims to develop better statistical methods and software in the areas of education, health, management and psychology. Our most recent research involves the development of new methods for social network and big data analysis. Particularly, we have contributed to the area of Bayesian methods, Network analysis, Big data analysis, Structural equation modeling, Longitudinal data analysis, Mediation analysis, and Statistical computing and programming.


In general, Zhiyong Zhang is interested in developing and applying statistical methods in the areas of developmental and health research. Methodologically, his research interests include (1) continuous and categorical dynamic factor models, nonlinear time series models, and dynamical systems analysis, (2) linear and nonlinear models for analyzing longitudinal data, and (3) Bayesian methods and statistical computing. Substantively, he is interested in the analysis of intraindividual change and interindividual differences in change of life span development, cognitive aging, and emotion.

ND PIER Affiliate

Recent Publications

* denotes student author

  • Cain, M., & Zhang, Z. (accepted). Fit for a Bayesian: An evaluation of PPP and DIC for structural equation modeling.Structural Equation Modeling.
  • Liu, H., Jin, I. K., & Zhang, Z.(accepted). Structural Equation Modeling of Social Networks: Specification, Estimation, and Application. Multivariate Behavioral Research
  • Mai, Y., Zhang, Z., & Wen, Z. (accepted). Comparing Exploratory Structural Equation Modeling and Existing Approaches for Multiple Regression with Latent Variables. Structural Equation Modeling. https://www.tandfonline.com/eprint/6u84fbxfzJPCGa6eUUgS/full
  • Yuan, K., Zhang, Z., & Deng, L. (accepted). Fit Indices for Mean Structures with Growth Curve Models. Psychological Methods.
  • Mai, Y., & Zhang, Z. (2018). Review of Software Packages for Bayesian Multilevel Modeling. Structural Equation Modeling, 25(4), 650–658. http://www.tandfonline.com/eprint/6u84fbxfzJPCGa6eUUgS/full
  • Cain, M., Zhang, Z., & Bergeman, C. S. (accepted). Time and Other Considerations in Mediation Design. Educational and Psychological Measurement
  • 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.
  • Tong, X., & Zhang, Z. (2017). Outlying Observation Diagnostics in Growth Curve Modeling. Multivariate Behavioral Research, 52(6), 768–788. http://www.tandfonline.com/eprint/43NdXgKr7Pywnv8SKYie/full
  • 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. http://www.tandfonline.com/eprint/avPtpSNV8Y4S5HwZGcc9/full
  • Cain, M., 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.
  • Liu, H., & Zhang, Z. (2017). Logistic Regression with Misclassification in Binary Outcome Variables: A Method and Software. Behaviormetrika, 44(2), 447–476.
  • 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