Publications
Psychometrics · Quantitative Methods
Content
-
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]
-
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]
-
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]
-
Zhang, L., & Liang, X. * (2023). Bayesian Regularization in Multiple-Indicators Multiple-Causes Models. Psychological Methods. Advance Online Publication. [doi]
-
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]
-
Zhang, L., Li, X., & Zhang, Z. (2023). Variety and Mainstays of the R Developer Community. R Journal, 15(3), 5-25. [doi]
-
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]
- 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]
-
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]
-
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]
-
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]
-
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]
-
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]
-
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]
-
Zhang, X., Zhang, L., Ding, Y., & Qu, Z. * (2021). Behavioral Oscillations in Attention. Advances in Psychological Science, 29(3): 461-471. [doi]
-
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]
-
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]
-
Zhang, L., Wei, X., Lu, J., Pan, J. * (2020). Lasso Regression: From Explanation to Prediction. Advances in Psychological Science, 28(10): 1777-1788. [doi]
-
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]
-
Zhang, L., Ulitzsch, E., & Domingue, B.W. (2024). Bayesian Factor Mixture Modeling with Response Time for Detecting Careless Respondents. [doi]
-
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]
-
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]
-
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]
-
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]
-
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.
-
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]
-
Zhang, L., & Pan, J.* (2022). Latent Multiple Mediation Analysis with the Bayesian Lasso. The 15th Chinese R Conference, 25 Nov, Virtual. [slides]
-
Zhang, L., Pan, J., & Ip, E.H., (2022). Bayesian Lasso Confirmatory Factor Analysis. Utrecht University, 23 May, Virtual. [abstract] [slides]
-
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]
-
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.
-
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.
-
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.
-
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.
-
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.
-
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]
-
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]
-
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]
-
Zhang, L., & Liang, X. * (2023). Bayesian Regularization in MIMIC Models. National Council on Measurement in Education Annual Meeting, 12-15 April, Chicago, USA.
-
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.
-
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.
-
Zhang, L., & Liang, X. * (2021). Bayesian Regularization in MIMIC Models. International Meeting of the Psychometric Society, 19-23 July, Virtual. [abstract] [slides]
-
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]
-
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]
-
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]
-
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]
-
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]
-
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]
-
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]
- Computational Neuroscience and Cognitive Modelling - Chinese Version (Anderson, 2014)
Translated chapters 9-13 (Neural Networks). - Handbook of Quantitative Methods in Psychological and Behavioral Research (in Chinese)
Wrote the Bayesian Structural Equation Modeling chapter with Dr. Junhao Pan. -
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.
-
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.
Journal Articles
( * indicates correspondent author, $\dag$ indicates alphabetical order or reverse)
Preprints
Conference Presentations
(Underline: Presenter)