Lijin Zhang

Publications

Psychometrics · Quantitative Methods

Content

    Journal Articles

    ( * indicates correspondent author, $\dag$ indicates alphabetical order or reverse)

  1. Chen, Q., Su, K., Feng, Y., Zhang, L., Ding, R., & Pan, J. (2024). A Tutorial on Bayesian Structural Equation Modeling: Principles and Applications. International Journal of Psychology. Advance Online Publication. [doi]

  2. Wang, E., Qian, D., Zhang, L., Li, B. S-K, Ko, B., Khoury, M., Renavikar, M., Ganesan, A., & Caruso, T.J. (2024). Acceptance of Virtual Reality in Trainees Using a Technology Acceptance Model. JMIR Medical Education. Advance Online Publication. [doi]

  3. Wang, E., Kennedy, K.M., Zhang, L., Zuniga-Hernandez, M., Titzler, J., Li, B. S-K., Arshad, F., Khoury, M., & Caruso, T.J. (2024). A Technology Acceptance Model to Predict Anesthesiologists’ Clinical Adoption of Virtual Reality. Journal of Clinical Anesthesia.. Advance Online Publication. [doi]

  4. He, E., Arshad, F., Li, B.S., Brinda, R., Ganesan, A., Zhang, L., Fehr, S., Renavikar, M., Rodriguez, S.T., Wang, E., Rosales, O., & Caruso, T.J. (2024). Awe Inducing Elements in Virtual Reality Applications: A Prospective Study of Hospitalized Children and Caregivers. Games for Health Journal. Advance Online Publication. [doi]

  5. Ahmed, I., Bertling, M., Zhang, L., Ho, A., Loyalka, P., Xue, H., Rozelle, S., & Domingue, B.W. * (2024). Heterogeneity of item-treatment interactions masks complexity and generalizability in randomized controlled trials. Journal of Research on Educational Effectiveness. Advance Online Publication. [doi]

  6. Zhang, L., & Liang, X. * (2024). Bayesian Regularization in Multiple-Indicators Multiple-Causes Models. Psychological Methods, 29(4), 679-703. Advance Online Publication. [doi]

  7. Gu, X., Zhu, X., Zhang, L., & Pan, J.* (2023). Testing Informative Hypotheses in Factor Analysis Models using Bayes Factors. Psychological Methods. Advance Online Publication. [doi]

  8. Zhang, L., Li, X., & Zhang, Z. (2023). Variety and Mainstays of the R Developer Community. R Journal, 15(3), 5-25. [doi]

  9. Wang, E. Y., Kennedy, K. M., Zhang, L., Qian, D., Forbes, T., Zuniga-Hernandez, M., Li, B. S-K., Domingue, B., & Caruso, T. J. (2023). Predicting pediatric healthcare provider use of virtual reality using a technology acceptance model. Journal of the American Medical Informatics Association Open, 6(3), ooad076. [doi]

  10. Zheng, S., Zhang, L., Jiang, Z., & Pan, J. * (2023). The Influence of Using Inaccurate Priors on Bayesian Multilevel Estimation. Structural Equation Modeling, 30 (3), 429-448. [doi]

  11. Wei, X.$\dag$, Huang, J. $\dag$, 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]

  12. 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]

  13. 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]

  14. 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]

  15. 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]

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

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

  18. Feng, Q.$\dag$, Song, Q. $\dag$, Zhang, L. $\dag$, 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]

  19. 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]

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

  21. 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]

    22. Preprints

  22. Zhang, L., Ulitzsch, E., & Domingue, B.W. (2024). Bayesian Factor Mixture Modeling with Response Time for Detecting Careless Respondents. [doi]

  23. Gilbert, J.B., Zhang, L., Ulitzsch, E., & Domingue, B.W. (2024). Polytomous Explanatory Item Response Models for Item Discrimination: Assessing Negative-Framing Effects in Social-Emotional Learning Surveys. [doi]

  24. Zhang, L., Kanopka, K., Rahal, C., Ulitzsch, E., Zhang, Z., & Domingue, B.W. (2023). The InterModel Vigorish for Model Comparison in Confirmatory Factor Analysis with Binary Outcomes. [doi]

  25. Domingue, B.W., Kanopka, K.$\dag$, Ulitzsch, E.$\dag$, & Zhang, L.$\dag$ (2023). Implied probabilities of polytomous response functions for model-based prediction and comparison. [doi]

  26. Domingue, B.W., Kanopka, K., Braginsky, M., Zhang, L. , Caffrey-Maffei, L., Kapoor, R., Liu, Y., Zhang, S., & Frank, M. (2023). The Item Response Warehouse. [doi]

    27. Conference Presentations

    (Underline: Presenter)

    Invited Talk

  27. Zhang, L., Domingue, B.W., Vogelsmeier, V., & Ulitzsch, E. (To be presented). Mixture modeling for identifying careless responding. The Norwegian Psychometrics Gathering, 19-20 Sep, Stavanger.

  28. Zhang, L., Qu, W., & Zhang, Z. (2023). Bayesian Growth Curve Modeling with Measurement Error in Time. University of Notre Dame, 31 Aug, South Bend, USA. [slides]

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

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

  31. 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]

    32. Contributed Conference Presentations

  32. Zhang, L., Ulitzsch, E., & Domingue, B.W. (To be presented). Bayesian Factor Mixture Modeling with Response Time for Detecting Careless Respondents. International Meeting of Psychometric Society, 16-19 July, Prague, Czech.

  33. Domingue, B.W., Kanopka, K., Braginsky, M., Zhang, L., Caffrey-Maffei, L., Kapoor, R., Liu, Y., Zhang, S., & Frank, M. (To be presented). The Item Response Warehouse. International Meeting of Psychometric Society, 16-19 July, Prague, Czech.

  34. Cao, C., Liang, X., Zhang, L. & Lu, M. (To be presented). The Performance of Bayesian Fit Measures in Approximate Measurement Invariance Testing in Cross-Cultural Research. International Meeting of Psychometric Society, 16-19 July, Prague, Czech.

  35. Zhang, L., Qu, W., & Zhang, Z. (To be presented). Bayesian Growth Curve Modeling with Measurement Error in Time. Annual Meeting of the International Society for Data Science and Analytics, 21-24 July, Vienna, Austria.

  36. Domingue, B.W., Kanopka, K., Ulitzsch, E., & Zhang, L. (2024). Implied Probabilities of Polytomous Response Functions for Model-Based Prediction and Comparison. National Council on Measurement in Education Annual Meeting, 11-14 April, Philadelphia, USA.

  37. Zhang, L., & Domingue, B.W. (2023). The InterModel Vigorish for Model Comparison in Confirmatory Factor Analysis with Binary Outcomes. International Meeting of Psychometric Society, July, Maryland, USA. [slides]

  38. Zhang, L., Liang, X., & Pan, J. (2023). Comparison between Bayesian and Frequentist Regularization in Factor Analysis. International Meeting of Psychometric Society, July, Maryland, USA. [slides]

  39. Zhang, L., & Domingue, B.W. (2023). The InterModel Vigorish for Model Comparison in Confirmatory Factor Analysis with Binary Outcomes. Annual Meeting of International Society for Data Science and Analytics, July 4-6, Shanghai, China. [slides]

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

  41. 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.

  42. 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.

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

  44. 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]

  45. 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]

  46. 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]

  47. 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]

  48. 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]

  49. 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]

  50. 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]

    51. Book Chapters

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

    53. Software Development

  53. 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.

  54. 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.