Reviews of Bayesian Structural Equation Modeling (SEM) and Lasso Method

Wrote two review articles and a book chapter, and presented five talks for introducing the latest development about BSEM and Lasso Method.

[Book Chapter] Advanced Psychometric Measurement and Latent Variable Modeling (in Chinese) Dr. Pan and I completed the Bayesian Structural Equation Modeling chapter.

Note: * indicates correspondent author, Advances in Psychological Science (in Chinese) is the most cited journal in Chinese Psychology.

[Journal Article] Zhang, L.J., Wei, X.Y., Lu, J.Q., Pan, J.H.* (accepted). Lasso Regression: From Explanation to Prediction. Advances in Psychological Science (in Chinese).

[Journal Article] Zhang, L.J., Lu, J.Q., Wei, X.Y., Pan, J.H.* (2019). Bayesian Structural Equation Modeling and its Current Research. Advances in Psychological Science (in Chinese), 27(11): 1812-1825.

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

[Talk] Song, Q.Y., Pan, J.H., Zhang, L.J. (2019). Bayesian Multiple-group Analysis: Approximate Measurement Invariance. The 22nd Chinese Academic Conference of Psychology, 18-20 October, Hangzhou.

[Talk] Zhang, L.J., Lu, J.Q., Wei, X.Y., Pan, J.H.* (2019). Bayesian Structural Equation Modeling and its Current Research. The 22nd Chinese Academic Conference of Psychology, 18-20 October, Hangzhou. [abstract] [slide]

[Talk] Zhang, L.J., Lu, J.Q., Wei, X.Y., Pan, J.H.* (2019, Invited). Bayesian Structural Equation Modeling and its Current Research. The 12th China-R Conference, 24-26 May, Beijing. [slide]

[Talk] Zhang, L.J., Lu, J.Q., Wei, X.Y., Pan, J.H.* (2019). Bayesian Structural Equation Modeling and its Current Research. Weekly Forum of Psychology in Sun Yat-sen University, 20 November, Guangzhou.

Bayesian Lasso Confirmatory Factor Analysis (CFA)

1. I participated in a project which proposed a partial factor analysis method between CFA and exploratory factor analysis. This project was published on Pyschological Methods. I was responsible for the simulation studies in the revision which was appraised as comprehensive by two reviewers [See the Review Comments]

2. I built an R package which extent Bayesian Lasso confirmatory factor analysis(CFA) to a model modification method. I’am writing a paper to introducing this method and will give a Talk in the International Meeting of the Psychometric Society.

3. I presented two talks about Bayesian Lasso Factor Analysis Models.

[Journal Article] Chen, J.S.*, Guo, Z.H., Zhang, L.J., Pan, J.H.* (accepted). A Partially Confirmatory Approach to Scale Development with the Bayesian Lasso. Psychological Methods. [2018 IF: 8.188]

[Journal Article] Pan, J.H., Zhang, L.J. (co-first author), Ip, E.H.* (manuscript drafted). BLCFA: An R Package for Bayesian Model Modification in Confirmatory Factor Analysis.

[Talk] Zhang, L.J., Pan, J.H.*, Ip, E.H. (To be presented in July 2020). BLCFA: An R package for Bayesian Model Modification in Confirmatory Factor Analysis. International Meeting of the Psychometric Society, 14-17 July, Online. [abstract]

[Talk] Pan, J.H., Zhang, L.J., 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. [slide]

[Talk] Pan, J.H., Zhang, L.J., 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] Zhang, L.J., Pan, J.H., Ip, E.H. (2020). BLCFA: An R package for Bayesian Model Modification in Confirmatory Factor Analysis. Retrievable from https://github.com/zhanglj37/blcfa.

My Undergraduate Graduation Thesis: How to Select Prior Variance in Bayesian Approximate Measurement Invariance (BAMI)?

1. This thesis won the excellent undergraduate graduation thesis of Sun Yat-sen University (top 5%).

2. I’am writing a paper for providing guidelines of prior selection in BAMI analysis.

3. The abstract of this article was accepted by two conferences and the presentation was award as excellent in The 22nd Chinese Academic Conference of Psychology.

[Journal Article] Zhang, L.J., Deng, Y.T., Zheng, S.F., Pan, J.H.* (manuscript drafted). How to Select Prior Variance in Bayesian Approximate Measurement Invariance ?

[Talk] Zhang, L.J., Pan, J.H.* (2019, Excellent Presenter). How to Select Prior Variance in Bayesian Approximate Measurement Invariance ? The 22nd Chinese Academic Conference of Psychology, 18-20 October, Hangzhou.[slide]

[Talk] Zhang, L.J., Pan, J.H.* (2019). How to Select Prior Variance in Bayesian Approximate Measurement Invariance ? The International Meeting of the Psychometric Society, 15-19 June, Santiago. [abstract] (accepted but wasn’t able to attend)

Sample Size Determination Guidelines for Latent Mediation Model

1. I participated in the writing, particularly in writing the software part.

2. I built an R package for Sample Size Determination in Structural Equation Modeling (e.g., Latent Mediation Analysis, Moderated Mediation Analysis, Cross-lagged modeling...etc).

[Journal Article] Sun, R.Q., Zhang, L.J., Pan, J.H.* (manuscript drafted). Sample Size Determination Guidelines for Latent Mediation Model: A Monte Carlo Study.

[Software Development] Zhang, L.J., Sun, R,Q., Pan, J.H. (2020). sampleMplus: An R Package for Sample Size Determination in Structural Equation Modeling. Retrievable from https://github.com/zhanglj37/sampleMplus.

Methods Comparison in Moderated Mediation Analysis

I participated in the writing and am responsible for the revision, including conducting the simulation study and writing.

[Journal Article] Feng, Q.Q., Song, Q.Y. (co-first author), Zhang, L.J. (co-first author), Zheng, S.F., Pan, J.H.* (under revision). Integration of Moderation and Mediation in a Latent Variable Framework: A Comparison of Estimation Methods. Frontiers in Psychology.

Compared the performance of structural equation modeling(SEM), exploratory SEM and Bayesian SEM in dealing with cross-loadings

I participated in the simulation design and article modification

[Journal Article] Wei, X.Y., Huang, J.S. (co-first author), Zhang, L.J., Pan, J.H.* (under review). Evaluation and Comparison among SEM, ESEM and BSEM in Estimating Structural Models with Potentially Unknown Cross-loadings. Behavior Research Methods.

Cooperations with Applied Researchers

1. My Supervisor and I cooperated with a laboratory in the Department of Neurology, the sixth affiliated hospital, Sun Yat-sen University. I participated in the data analysis.

2. I hosted a project to test the influence of social support on career decision-making difculties through Bayesian longitudinal structural equation modeling.

[Journal Article] Liu, S.J., Huang, Z.Z., Zhang, L.J., Pan, J.H., Lei, Q.F., Meng, Y.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. [2018 IF: 3.633]

[Poster] Zhang, L.J., Lu, J.Q., Zhang, Y.N., Pan, J.H.* (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]

[Poster] Zhang, L.J., Lu, J.Q., Zhang, Y.N., Pan, J.H.* (2018, Excellent Presenter). The Influence of Social Support on Career Decision-Making Difficulty: A Moderated Mediation Model. Psychology Academic Forum for the 21st Century, 24-26 March, Beijing.

[Book Chapter] Computational Neuroscience and Cognitive Modelling (in Chinese)

I am translating this book (Anderson, 2014) with cooperators from PsychoR team and Capital of Statistics. I am responsible for the part of Neural Networks (chapter 9-13).



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