Reading List 2025 - Featured Papers

1 AI in Education

  1. Cheng, Y., Zhang, C., Zhang, Z., Meng, X., Hong, S., Li, W., Wang, Z., Wang, Z., Yin, F., Zhao, J., & He, X. (2024). Exploring Large Language Model based Intelligent Agents: Definitions, Methods, and Prospects (No. arXiv:2401.03428). arXiv. https://doi.org/10.48550/arXiv.2401.03428
  2. Huang, J., Jiao, W., Lam, M. H., Li, E. J., Wang, W., & Lyu, M. R. (2024). Revisiting the Reliability of Psychological Scales on Large Language Models (No. arXiv:2305.19926). arXiv. https://doi.org/10.48550/arXiv.2305.19926
  3. Huang, J., Wang, W., Li, E. J., Lam, M. H., Ren, S., Yuan, Y., Jiao, W., Tu, Z., & Lyu, M. R. (2024). Who is ChatGPT? Benchmarking LLMs’ Psychological Portrayal Using PsychoBench (No. arXiv:2310.01386). arXiv. https://doi.org/10.48550/arXiv.2310.01386
  4. Jiang, H., Zhang, X., Cao, X., Breazeal, C., Roy, D., & Kabbara, J. (2024). PersonaLLM: Investigating the Ability of Large Language Models to Express Personality Traits (No. arXiv:2305.02547). arXiv. https://doi.org/10.48550/arXiv.2305.02547
  5. Li, Y., Huang, Y., Wang, H., Zhang, X., Zou, J., & Sun, L. (2024). Quantifying AI Psychology: A Psychometrics Benchmark for Large Language Models (No. arXiv:2406.17675). arXiv. https://doi.org/10.48550/arXiv.2406.17675
  6. Liu, Y., Bhandari, S., & Pardos, Z. A. (2024). Leveraging LLM-Respondents for Item Evaluation: A Psychometric Analysis (No. arXiv:2407.10899). arXiv. https://doi.org/10.48550/arXiv.2407.10899
  7. Olson, K., Smyth, J. D., Dykema, J., Holbrook, A. L., Kreuter, F., & West, B. T. (2020). The past, present, and future of research on interviewer effects. In Interviewer effects from a total survey error perspective (pp. 3–16). Chapman and Hall/CRC. https://www.taylorfrancis.com/chapters/edit/10.1201/9781003020219-2/past-present-future-research-interviewer-effects-kristen-olson-jolene-smyth-jennifer-dykema-allyson-holbrook-frauke-kreuter-brady-west
  8. Ongena, Y. P., & Dijkstra, W. (2021). Advances in research on survey interview interaction. International Journal of Social Research Methodology, 24(2), 177–179. https://doi.org/10.1080/13645579.2020.1824625
  9. Parker, M. J., Anderson, C., Stone, C., & Oh, Y. (2024). A Large Language Model Approach to Educational Survey Feedback Analysis. International Journal of Artificial Intelligence in Education. https://doi.org/10.1007/s40593-024-00414-0
  10. Serapio-García, G., Safdari, M., Crepy, C., Sun, L., Fitz, S., Romero, P., Abdulhai, M., Faust, A., & Matarić, M. (2023). Personality Traits in Large Language Models (No. arXiv:2307.00184). arXiv. https://doi.org/10.48550/arXiv.2307.00184
  11. Wang, P., Zou, H., Yan, Z., Guo, F., Sun, T., Xiao, Z., & Zhang, B. (2024). Not Yet: Large Language Models Cannot Replace Human Respondents for Psychometric Research. OSF. https://doi.org/10.31219/osf.io/rwy9b
  12. Xu, S., & Zhang, X. (2023). Leveraging generative artificial intelligence to simulate student learning behavior (No. arXiv:2310.19206). arXiv. https://doi.org/10.48550/arXiv.2310.19206
  13. Zou, Z., Mubin, O., Alnajjar, F., & Ali, L. (2024). A pilot study of measuring emotional response and perception of LLM-generated questionnaire and human-generated questionnaires. Scientific Reports, 14(1), 2781. https://doi.org/10.1038/s41598-024-53255-1

2 Network psychometrics

2.1 Tutorial

  1. Borsboom, D. (2002). The Structure of the DSM. Archives of General Psychiatry, 59(6), 569–570.
  2. Borsboom, D. (2008). Psychometric perspectives on diagnostic systems. Journal of Clinical Psychology, 64(9), 1089–1108. https://doi.org/10.1002/jclp.20503
  3. Borsboom, D. (2017). A network theory of mental disorders. World Psychiatry, 16(1), 5–13. https://doi.org/10.1002/wps.20375
  4. Borsboom, D. (2022). Possible futures for network psychometrics. Psychometrika, 87(1), 253–265. https://doi.org/10.1007/s11336-022-09851-z
  5. Borsboom, D., & Cramer, A. O. J. (2013). Network analysis: An integrative approach to the structure of psychopathology. Annual Review of Clinical Psychology, 9, 91–121. https://doi.org/10.1146/annurev-clinpsy-050212-185608
  6. Borsboom, D., Deserno, M. K., Rhemtulla, M., Epskamp, S., Fried, E. I., McNally, R. J., Robinaugh, D. J., Perugini, M., Dalege, J., Costantini, G., Isvoranu, A.-M., Wysocki, A. C., van Borkulo, C. D., van Bork, R., & Waldorp, L. J. (2021). Network analysis of multivariate data in psychological science. Nature Reviews Methods Primers, 1(1), Article 1. https://doi.org/10.1038/s43586-021-00055-w
  7. Epskamp, S., & Fried, E. I. (2018). A tutorial on regularized partial correlation networks. Psychological Methods, 23, 617–634. https://doi.org/10.1037/met0000167
  8. Epskamp, S. (2023). psychonetrics: Structural Equation Modeling and Confirmatory Network Analysis (Version 0.11.5) [Computer software]. https://cran.r-project.org/web/packages/psychonetrics/index.html
  9. Epskamp, S. (2020). Psychometric network models from time-series and panel data. Psychometrika, 85(1), 206–231. https://doi.org/10.1007/s11336-020-09697-3

2.2 Interpreability of outcomes

How to appropriately interpret outcomes of network analysis, such as edge weights, centrality, targeted construct, scoring etc, is very important topic for explanatory network analysis.

  1. Bork, R. van, Lunansky, G., & Borsboom, D. (2024). Measurement Targets for Network Constructs in Psychopathology. Preprint. https://doi.org/10.31234/osf.io/2k34t
  2. Blanken, T. F., Van Der Zweerde, T., Van Straten, A., Van Someren, E. J. W., Borsboom, D., & Lancee, J. (2019). Introducing Network Intervention Analysis to Investigate Sequential, Symptom-Specific Treatment Effects: A Demonstration in Co-Occurring Insomnia and Depression. Psychotherapy and Psychosomatics, 88(1), 52–54. https://doi.org/10.1159/000495045
  3. Hallquist, M. N., Wright, A. G. C., & Molenaar, P. C. M. (2021). Problems with Centrality Measures in Psychopathology Symptom Networks: Why Network Psychometrics Cannot Escape Psychometric Theory. Multivariate Behavioral Research, 56(2), 199–223. https://doi.org/10.1080/00273171.2019.1640103

2.3 Accuaracy and reliability issues

The reliability and replicability problems in network analysis raise a lot of debates.

  1. Epskamp, S., Borsboom, D., & Fried, E. I. (2018). Estimating psychological networks and their accuracy: A tutorial paper. Behavior Research Methods, 50(1), 195–212. https://doi.org/10.3758/s13428-017-0862-1
  2. Forbes, M. K., Wright, A. G. C., Markon, K. E., & Krueger, R. F. (2017a). Evidence that psychopathology symptom networks have limited replicability. Journal of Abnormal Psychology, 126(7), 969–988. https://doi.org/10.1037/abn0000276
  3. Forbes, M. K., Wright, A. G. C., Markon, K. E., & Krueger, R. F. (2017b). Further evidence that psychopathology networks have limited replicability and utility: Response to Borsboom et al. (2017) and Steinley et al. (2017). Journal of Abnormal Psychology, 126(7), 1011–1016. https://doi.org/10.1037/abn0000313
  4. Forbes, M. K., Wright ,Aidan G. C., Markon ,Kristian E., & and Krueger, R. F. (2021a). On Unreplicable Inferences in Psychopathology Symptom Networks and the Importance of Unreliable Parameter Estimates. Multivariate Behavioral Research, 56(2), 368–376. https://doi.org/10.1080/00273171.2021.1886897
  5. Forbes, M. K., Wright ,Aidan G. C., Markon ,Kristian E., & and Krueger, R. F. (2021b). Quantifying the Reliability and Replicability of Psychopathology Network Characteristics. Multivariate Behavioral Research, 56(2), 224–242. https://doi.org/10.1080/00273171.2019.1616526
  6. Fried, E. I., van Borkulo ,Claudia D., & and Epskamp, S. (2021). On the Importance of Estimating Parameter Uncertainty in Network Psychometrics: A Response to Forbes et al. (2019). Multivariate Behavioral Research, 56(2), 243–248. https://doi.org/10.1080/00273171.2020.1746903
  7. Jones, P. J., Williams ,Donald R., & and McNally, R. J. (2021). Sampling Variability Is Not Nonreplication: A Bayesian Reanalysis of Forbes, Wright, Markon, and Krueger. Multivariate Behavioral Research, 56(2), 249–255. https://doi.org/10.1080/00273171.2020.1797460

2.4 Community dectection

  1. Christensen, A. P., Garrido, L. E., Guerra-Peña, K., & Golino, H. (2024). Comparing community detection algorithms in psychometric networks: A Monte Carlo simulation. Behavior Research Methods, 56(3), 1485–1505. https://doi.org/10.3758/s13428-023-02106-4
  2. Werner, M. A., de Ron, J., Fried, E. I., & Robinaugh, D. J. (2025). Iterated community detection in psychological networks. Psychological Methods. https://doi.org/10.1037/met0000744

2.5 Connection to FA

  1. Christensen, A. P., Garrido, L. E., & Golino, H. (2023). Unique Variable Analysis: A Network Psychometrics Method to Detect Local Dependence. Multivariate Behavioral Research, 58(6), 1165–1182. https://doi.org/10.1080/00273171.2023.2194606
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