Skip to main content
Professor CHEN, Jinsong

Professor CHEN, Jinsong


Associate Professor

Academic Unit of Human Communication, Learning, and Development


BSc (SCUT), MA (GWU), EdD (GWU), Postdoc (Rutgers)


[javascript protected email address]


(852) 3917 0389


Room 645, Meng Wah Complex

Research Expertise

  • Assessment, Testing and Measurement
  • Research Methods and Methodologies
  • Data Sciences and Digital Humanities
  • Educational Psychology

Prospective PhD/ EdD/ MPhil Applications

I am available to supervise PhD/EdD/MPhil students and would welcome enquiries for supervision.


My research interests center around two main areas. The first area focuses on latent variable modeling and structural equation modeling. I am currently developing a partially confirmatory approach to both frameworks with multiple research directions and potential applications. The estimation algorithm presents a significant challenge that I aim to overcome.

The second area is the integration of psychometrics with NLP. I am exploring different approaches to combine large language models with psychometrics, which can be applied in various educational and psychological domains. One potential application I am investigating is to improve STEM learning with NLP-generated educational contents such as knowledge, solutions, and examples.



  • Quantitative methods, psychometrics, and statistics 

[NEW] Join our team to advance STEM education with psychometrics and NLP. We're seeking new PhD students with experience in large language models to collaborate with world-class researchers and educators. Learn state-of-the-art knowledge and gain hands-on experience with cutting-edge technologies. Be self-motivated, collaborative, and passionate about research for education improvement. Our doctoral program offers a supportive and inclusive learning environment that fosters innovation and collaboration. Email:

Title: Advancing educational and psychological measurement with Bayesian learning: Methodological developments and practical implementations

Source: RGC General Research Fund (GRF)

Amount: HK$333,000

Period: July 2022 – July 2024


Title: A Partially Confirmatory Approach to Factor Analysis with Bayesian Learning

Source: Start-up Grant, University of Hong Kong
Amount: HK$335,000
Period: August 2020 – August 2023
Title: Integrating Psychometric Modeling with Bayesian Learning
Source: Philosophy and Social Sciences Later-Stage Program, Guangdong Province
Amount: RMB40,000
Period: December 2019 – December 2021
Title: Exploring the Penalization of Latent Variable Modeling with Bayesian LASSO
Source: Key Project, National Higher Education Quality Monitoring Data Center (Guangzhou)
Amount: RMB100,000
Period: January 2019 – December 2020
Title: Combing Educational Taxonomy with Validity Theory for Higher Education Evaluation
Source: Project for Teaching Innovation of Higher Education, Guangdong Province
Amount: RMB20,000
Period: January 2019 – December 2020
Title: Framework Research on the Quality Standards for College Student Cultivation
Source: Key Project, National Higher Education Quality Monitoring Data Center (Guangzhou)
Amount: RMB200,000
Period: December 2015–December 2019
Title: Research on the Quality of Teaching and Course Evaluation
Source: Long-term Project, National Higher Education Quality Monitoring Data Center (Guangzhou)
Amount: RMB250,000
Period: December 2015 – December 2019
Title: Experimental Project on Assessment of Child and Youth Behaviors
Source: Scientific Research Project Sponsored by Haoyuan Education Enterprise
Amount: RMB50,000
Period: October 2015 – December 2016
Title: Assessment of Children’s Developmental Ecology
Source: Scientific Research Project Sponsored by Kdnet Enterprise
Amount: RMB90,000
Period: June 2015 – December 2016
Title: Investigating a Framework for Course Evaluation Based on Student Evaluation of Teaching
Source: Key Project, Undergraduate Teaching Reform Program, Sun Yat-Sen University
Amount: RMB20,000
Period: January 2015 – January 2017
Title: Development of Computerized Adaptive Testing for Cognitive Diagnosis and Tracking and Its Applications for Basic Education
Source: Humanities and Social Sciences Common Program, Ministry of Education
Amount: RMB100,000
Period: October 2014 – October 2017
Title: Research on Integration of Cognitive Diagnosis Assessment and Evaluation
Source: Social Sciences Common Program, Guangdong Province
Amount: RMB20,000
Period: January 2014 – January 2016
Title: Applications of Cognitive Diagnosis Assessments for Basic Mathematics
Source: Start-up Grant, Sun Yat-Sen University
Amount: RMB150,000
Period: February 2013 – February 2015

 (*corresponding author)

  • Chen, J. (2022). Fully and partially exploratory factor analysis with bi-level Bayesian regularization. Behavior Research Methods. DOI: 10.3758/s13428-022-01884-7.

  • Ma W., Chen J. & Jiang Z. (2022). Attribute continuity in cognitive diagnosis models: impact on parameter estimation and its detection, Behaviormetrika. DOI: 10.1007/s41237-022-00174-y.
  • Chen, J. (2022). Partially confirmatory approach to factor analysis with Bayesian learning: A LAWBL tutorial. Structural Equation Modeling: A Multidisciplinary Journal. 29, 800-816. DOI: 10.1080/10705511.2022.2039660.
  • Chen, J. (2021). A generalized partially confirmatory factor analysis framework with mixed Bayesian Lasso methods. Multivariate Behavioral Research. DOI: 10.1080/00273171.2021.1925520.
  • Chen, J. (2021). A Bayesian regularized approach to exploratory factor analysis in one step. Structural Equation Modeling: A Multidisciplinary Journal, 28(4), 518-528. DOI: 10.1080/10705511.2020.1854763.
  • Chen, J. (2020). A partially confirmatory approach to the multidimensional item response theory with the Bayesian Lasso. Psychometrika. 85(3), 738-774. DOI: 10.1007/s11336-020-09724-3.
  • *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: 10.1037/met0000293.
  • *Chen, J., Li, L., & Zhang, D. (2020). Students with specific difficulties in Geometry: Exploring the TIMSS 2011 data with plausible values and latent profile analysis. Learning Disability Quarterly. 44(1), 11-22. DOI: 10.1177/0731948720905099.
  • Choi, J., Chen, J., & Harring, J. (2020). Logistic growth modeling with Markov chain Monte Carlo estimation. Journal of Modern Applied Statistical Methods, 18(1), eP2997. DOI: 10.22237/jmasm/1556669820.
  • Guo, Z., & *Chen, J. (2019). Teaching evaluation under the view of modern validity: Reflection and suggestions [Chinese]. Higher Education Exploration. 3, 11-15.
  • *Chen, J., & de la Torre, J. (2018). Introducing the general polytomous diagnosis modeling framework. Front. Psychol. 9:1474. DOI: 10.3389/fpsyg.2018.01474
  • Chen, J. (2018) A hierarchical taxonomy of test validity for more flexibility of validation. Front. Psychol. 9:972. DOI: 10.3389/fpsyg.2018.00972
  • Lin, Y., Chen, H., & *Chen, J. (2018). Exploring cognitive diagnosis retrofitting and further analyses of language proficiency testing: The case of the Guangzhou English achievement examination. Journal of Psychological Science [Chinese]. 41(3), 1-7.
  • Zhang, G., Zhou, X., Chen, J., Gong, Y., Sun, S., Yu, X. (2018). The Viral Impact of Emotion on Social Transmission under Control Context. Biomed J Sci & Tech Res. 7(3). DOI: 10.26717/ BJSTR.2018.07.001499.
  • Chen, H., & Chen, J. (2017). Cognitive diagnostic research on Chinese students’ English listening skills and implications on skill training. English language Teaching. 12(10), 107-115.
  • *Chen, J., & Zhou H. (2017). Test designs and modeling under the general nominal diagnosis model framework. PLOS One, 12(6): e0180016.
  • Chen, J. (2017). A residual-based approach to validate Q-matrix specifications. Applied Psychological Measurement, 41(4), 277–293.
  • Chen, J. (2017). Advancing the Bayesian approach for multidimensional polytomous and nominal IRT models: Model formulations and fit measures. Applied Psychological Measurement, 41, 3-16.
  • Chen, J. (2017). Introducing a flexible approach to test validity based on context-specific construct. Theory and Psychology, 27(5), 711-718.
  • Zhang, D., Ding, Y., Lee, S., & *Chen, J. (2017). Strategic development of multiplication problem solving: Patterns of students’ strategy choices. The Journal of Educational Research, 110, 159-170.
  • Chen, H., & *Chen, J. (2016). Retrofitting non-cognitive-diagnostic reading assessment under the generalized DINA model framework. Language Assessment Quarterly, 13, 218-230.
  • Chen, H., & *Chen, J. (2016). Exploring reading comprehension skill relationships through the G-DINA model. Educational Psychology: An International Journal of Experimental Educational Psychology, 36, 1049-1064.
  • *Chen, J., Zhang, D., & Choi, J. (2015). Estimation of the latent mediated effect with ordinal data using the limited-information and Bayesian full-information approaches. Behavior Research Methods, 47, 1260-1273.
  • *Chen, J., & de la Torre, J. (2014). A procedure for diagnostically modeling extant large-scale assessment data: The case of the Programme for International Student Assessment in Reading. Psychology, 5, 1967-1978.
  • *Chen, J., Choi, J., Weiss, B. A., & Stapleton, L. (2014). An empirical evaluation of mediation effect analysis with manifest and latent variables using Morkov chain Monte Carlo and alternative estimation methods. Structural Equation Modeling, 21, 253-262.
  • *Chen, J., & de la Torre, J. (2013). A general cognitive diagnosis model for expert-defined polytomous attributes. Applied Psychological Measurement, 37, 419-437.
  • *Chen, J., de la Torre, J., & Zhang, Z. (2013). Relative and absolute fit evaluation in cognitive diagnosis modeling. Journal of Educational Measurement, 50, 123-140.
  • Chen, H., & Chen, J. (2013). Validating G-DINA model in language test diagnosis. Journal of Psychological Science [Chinese], 36, 1470-1475.
  • Choi, J., Kim, S., Chen, J., & Dannels, A. S. (2011). A comparison of maximum likelihood and Bayesian estimation for polychoric correlation using Monte Carlo simulation. Journal of Educational and Behavioral Statistics, 36(4), 523-549.
  • Choi, J., Dunlop, M., Chen, J., & Kim, S. (2011). A comparison of different approaches for coefficient alpha for ordinal data. Journal of Educational Evaluation, 24(2), 485-506.
  • Kroopnick, H. M., Chen, J., Choi, J. M., & Dayton, C. M. (2010). Assessing classification bias in latent class analysis: Comparing resubstitution and leave-one-out methods. Journal of Modern Applied Statistical Methods, 9(1), 52-63.
  • *Chen, J., & Choi, J., (2009). A comparison of maximum likelihood and expected a posteriori estimation for polychoric correlation using Monte Carlo simulation. Journal of Modern Applied Statistical Methods, 8(1), 337-354.
  • Zhou, X.-Y., Lei, Q., Marley, S. C., & Chen, J. (2009). The existential function of babies: Babies as a buffer of death-related anxiety. Asian Journal of Social Psychology, 12(1), 40–46.

Computer programs distributed

  • EAPPCC: A Matlab program for estimating polychoric correlation matrices
  • GCDM: An Ox program for the general cognitive diagnosis modeling framework with dichotomous and polytomous attributes and dichotomous responses
  • GRDM: An Ox program for the graded response diagnosis modeling framework
  • GNDM: An Ox program for the general nominal diagnosis modeling framework
  • LAWBL: Latent (variable) analysis with Bayesian learning (R package version 1.3.0). Retrieved from

Keynotes or invited talks

  • 时间: 2015.12; 内容: 全球華人教育資訊與評估學術研討會暨中國測驗學會年會;主办单位:台中教育大学;地点:台湾;身份:报告嘉宾
  • Time: 2015.12; Content: Public Lecture & Academic Workshop on Cognitive Diagnosis Modeling; Hosted by: The Hong Kong Institute of Education; Venue: Hong Kong; Role: Speaker
  • Time: 2017.03; Content: Triangle Framework for Educational and Psychological Testing and Measurement; Hosted by: The University of Hong Kong; Venue: Hong Kong; Role: Speaker
  • 时间: 2018.01; 内容: 第二届全国心理学专业《心理统计学》《心理测量学》任课教师培训班;主办单位:中国心理学会;地点:广州;身份:课程报告人
  • Time: 2018.05; Content: Improving diagnostic assessments with Bayesian regularized item response models; Hosted by: Shanghai International Studies University; Venue: Shanghai; Role: Speaker
  • 时间: 2018.06; 内容:智慧评价与学习学术研讨会;主办单位:江西师范大学;地点:南昌;身份:报告嘉宾
  • Time: 2018.12; Content: Partially Confirmatory Approach to Scale Development with Bayesian Lasso; Hosted by: the Education University of Hong Kong; Venue: Hong Kong; Role: Speaker
  • 时间: 2019.1 内容: 第五届中国教育财政学术研讨会暨2019年中国教育发展战略学会教育财政专业委员会年会; 主办单位:北京大学;地点:北京;身份:特邀嘉宾

Service to the faculty/university/community

  • Quantitative Research Methods consultant, 2021-present
  • Service to the Confirmation or Thesis Examining Committees, 2020-present
  • Service to the Information Management programs, 2020-present

Knowledge Exchange Activities

  1. 2021-12-01 to 2022-03-01: Open Courseware: Structural Equation Modeling I - A First Course, KE Website and Mobile Device
  2. 2021-08-27 to 2021-08-27: A Partially Confirmatory Approach to Latent Variable Modeling with Bayesian Learning, Public Event (Delivering a Talk, Workshop, Exhibition, Performance, etc.)
  3. 2021-06-16 to 2021-06-16: A Partially Confirmatory Approach to Factor Analysis with Bayesian Learning, Public Event (Delivering a Talk, Workshop, Exhibition, Performance, etc.)
  4. 2021-06-01 to 2021-09-01: Open Courseware: Statistical and Psychometric Analyses Using R, KE Website and Mobile Device
  5. 2020-09-01 to 2021-12-01: Open Courseware: Factor Analysis, KE Website and Mobile Device