Content

• Book Chapters
• Journal Articles
• Conference Presentations
• Software Development

Research Topics

• Regularization and Cross-validation in Structural Equation Modeling:
  • Zhang & Liang, 2023 - accepted by Psychological Methods.
  • Zhang & Domingue, 2023 - to be presented at IMPS 2023.
  • Zhang, Liang, & Pan, 2023 - to be presented at IMPS 2023.
• Bayesian Lasso Confirmatory Factor Analysis:
  • Zhang, Pan, & Ip, 2021 - published in Structural Equation Modeling.
  • Zhang et al., 2021 - published in Structural Equation Modeling.
  • Chen et al., 2021 - published in Psychological Methods.
  • Pan, Zhang, & Ip, 2021 - presented at IMPS 2021.
  • Ip et al., 2022 - presented at IMPS 2022.
• Mediation and Moderation Analysis:
  • Feng et al., 2020 - published in Frontiers in Psychology.
  • Zhang & Pan, 2021 - presented at CMSatistics 2021.

Book Chapters

1. Computational Neuroscience and Cognitive Modelling - Chinese Version, in press (Anderson, 2014)
   Translated chapters 9-13 (Neural Networks).
2. Handbook of Quantitative Methods in Psychological and Behavioral Research (in Chinese)
   Wrote the Bayesian Structural Equation Modeling chapter with Dr. Junhao Pan.

Journal Articles

( * indicates correspondent author, + indicates co-first author)

3. Zhang, L., & Liang, X. * (Accepted). Bayesian Regularization in Multiple-Indicators Multiple-Causes Models. Psychological Methods

4. Zhang, L., Li, X., & Zhang, Z. (Accepted). Variety and Mainstays of the R Developer Community. The R Journal

5. Zheng, S., Zhang, L., Jiang, Z., & Pan, J. * (2022). The Influence of Using Inaccurate Priors on Bayesian Multilevel Estimation. Structural Equation Modeling. Advance Online Publication. [doi]

6. Wei, X., Huang, J. +, Zhang, L., Pan, D.* & Pan, J. * (2022). Evaluation and Comparison among SEM, ESEM and BSEM in Estimating Structural Models with Potentially Unknown Cross-loadings. Structural Equation Modeling, 29 (3), 327-338. [doi]

7. Zhang, L., Pan, J. * , & Ip, E.H. (2021). Criteria for Parameter Identification in Bayesian Lasso Methods for Covariance Analysis: Comparing Rules for Thresholding, p-value, and Credible Interval. Structural Equation Modeling, 28(6), 941-950. [doi]

8. Zhang, L., Pan, J. * , Dubé, L., & Ip, E.H. (2021). blcfa: An R Package for Bayesian Model Modification in Confirmatory Factor Analysis. Structural Equation Modeling, 28(4), 649-658. [doi]

9. Zeng, G., Zhang, L., Fung, S., et al. (2021). Problematic Internet Usage and Self-esteem in Chinese Undergraduate Students: The Mediation Effects of Individual Affect and Relationship Satisfaction. International Journal of Environmental Research and Public Health, 18(13), 6949. [doi]

10. Chen, J. * , Guo, Z., Zhang, L., & Pan, J. * (2021). A Partially Confirmatory Approach to Scale Development with the Bayesian Lasso. Psychological Methods, 26(2): 210-235. [doi]

11. Feng, Q., Song, Q. +, Zhang, L. +, Zheng, S., & Pan, J. * (2020). Integration of Moderation and Mediation in a Latent Variable Framework: A Comparison of Estimation Approaches for the Second-stage Moderated Mediation Model. Frontiers in Psychology, 11: 2167. [doi]

12. Liu, S., Huang, Z., Zhang, L., Pan, J., Lei, Q., Meng, Y., & Li, Z. * (2020). Plasma Neurofilament Light Chain may be a Biomarker for the Inverse Association between Cancers and Neurodegenerative Diseases. Frontiers in Aging Neuroscience, 12(10): 1-8. [doi]

In Chinese

(Advances in Psychological Science was rated as one of the most influential Chinese journals.)

13. Zheng, S., Zhang, L., Qiao, X., & Pan, J. * (2021). Intensive Longitudinal Data Analysis: Models and Application. Advances in Psychological Science, 29(11), 1948-1969.

14. Zhang, X., Zhang, L., Ding, Y., & Qu, Z. * (2021). Behavioral Oscillations in Attention. Advances in Psychological Science, 29(3): 461-471. [doi]

15. Zhang, L., Wei, X., Lu, J., Pan, J. * (2020). Lasso Regression: From Explanation to Prediction. Advances in Psychological Science, 28(10): 1777-1788. [doi]

16. Zhang, L., Lu, J., Wei, X., & Pan, J. * (2019). Bayesian Structural Equation Modeling and its Current Research. Advances in Psychological Science, 27(11): 1812-1825. [doi]

Preprints

17. Ahmed, I., Bertling, M., Zhang, L., Ho, A., Loyalka, P., Xue, H., Rozelle, S., Domingue, B.W. * (2023). Heterogeneity of item-treatment interactions masks complexity and generalizability in randomized controlled trials. [doi]

Conference Presentations

(Underline: Presenter)

Invited Talk

18. Zhang, L., & Pan, J.* (2022). Latent Multiple Mediation Analysis with the Bayesian Lasso. The 15th Chinese R Conference, 25 Nov, Virtual. [slides]

19. Zhang, L., Pan, J., & Ip, E.H., (2022). Bayesian Lasso Confirmatory Factor Analysis. Utrecht University, 23 May, Virtual. [abstract] [slides]

20. Zhang, L., Lu, J., Wei, X., & Pan, J. * (2019). Bayesian Structural Equation Modeling and its Current Research. The 12th Chinese R Conference, 24-26 May, Beijing. [slides]

Contributed Conference Presentations

21. Zhang, L., & Domingue, B.W. (To be presented). The InterModel Vigorish for Model Comparison in Confirmatory Factor Analysis with Binary Outcomes. International Meeting of Psychometric Society, July, Maryland, USA.

22. Zhang, L., Liang, X., & Pan, J. (To be presented). Comparison between Bayesian and Frequentist Regularization in Factor Analysis. International Meeting of Psychometric Society, July, Maryland, USA.

23. Zhang, L., & Liang, X. * (2023). Bayesian Regularization in MIMIC Models. National Council on Measurement in Education Annual Meeting, 12-15 April, Chicago, USA.

24. Ip, E.H., Sandberg, J., Zhang, L., & Pan, J.* (2022). Matched-pair Binary Item Response Analysis Using Bayesian Adaptive Lasso Factor Model. International Meeting of the Psychometric Society, 11-15 July, Bologna, Italy.

25. Zhang, L., & Pan, J. * (2021). How to Select Prior Variance in Bayesian Approximate Measurement Invariance. The 6th Eastern Chapter of International Society for Bayesian Analysis Conference, 17 November, Virtual.

26. Zhang, L., & Liang, X. * (2021). Bayesian Regularization in MIMIC Models. International Meeting of the Psychometric Society, 19-23 July, Virtual. [abstract] [slides]

27. Zhang, L., Pan. J * , & Ip, E.H. (2021). Comparison between Different Parameters Identification Criteria using the Bayesian Lasso. International Meeting of the Psychometric Society,, 19-23 July, Virtual. [abstract] [slides]

28. Pan. J, Zhang, L., & Ip, E.H. * (2021). Bayesian Covariance Adaptive Lasso Factor Analysis Models with Ordinal Data. International Meeting of the Psychometric Society, 19-23 July, Virtual. [abstract]

29. Zhang, L., Pan, J. * , & Ip, E.H. (2020). blcfa: An R package for Bayesian Model Modification in Confirmatory Factor Analysis. International Meeting of the Psychometric Society, 14-17 July, Virtual. [abstract] [slides]

30. Zhang, L., Wei, X., Lu, J., & Pan, J. * (2019). Lasso Regression: From Explanation to Prediction. The 22nd Chinese Academic Conference of Psychology, 18-20 October, Hangzhou. [abstract] [slides]

31. Zhang, L., Lu, J., Zhang, Y., & Pan, J. * (2019). The Influence of Social Support on Career Decision-Making Difficulty: Bayesian Modeling Based on Longitudinal Data. The 22nd Chinese Academic Conference of Psychology, 18-20 October, Hangzhou. [abstract][poster]

32. Pan, J., Zhang, L., & Ip, E.H. * (2018). Bayesian Lasso Factor Analysis Models with Ordered Categorical Data. The 13th Cross-Straits Conference on Educational and Psychological Testing, 22-25 October, Taiwan. [slides]

33. Pan, J., Zhang, L., & Ip, E.H. * (2017). Bayesian Lasso Factor Analysis Models with Ordered Categorical Data. The 20th Chinese Academic Conference of Psychology, 3-5 November, Chongqing. [abstract]

Software Development

34. Zhang, L., Pan, J., & Ip, E.H. (2020). blcfa: An R Package for Bayesian Model Modification in Confirmatory Factor Analysis. Retrievable from https://github.com/zhanglj37/blcfa.

35. Zhang, L., Sun, R., & Pan, J. (2020). sampleMplus: An R Package for Sample Size Determination in Structural Equation Modeling. Retrievable from https://github.com/zhanglj37/sampleMplus.