Research Article

Psychometric network analysis in educational sciences research: A methodological guideline for estimation, interpretation, and critical decision-making

Servet Demir 1 *
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1 Independent Researcher, TURKEY* Corresponding Author
International Journal of Professional Development, Learners and Learning, 8(2), 2026, e2612, https://doi.org/10.30935/ijpdll/18905
Submitted: 09 March 2026, Published: 01 July 2026
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ABSTRACT

Psychometric network analysis has emerged as an alternative to the latent variable approach in modeling the structure of psychological and educational constructs. Rather than attributing the common variance among observed variables to a single latent common cause, the network perspective treats structures as systems composed of components that interact directly with one another. Although the approach has been widely applied in clinical psychology, its use in educational sciences remains limited and inconsistent. This study introduces the conceptual foundations of psychometric network analysis and, following the standard research article format, provides a structured analysis workflow for researchers working with educational data. In the methods section, the Gaussian graph model, regularized estimation, evaluation of network accuracy and stability, centrality interpretation, community detection via exploratory graph analysis (EGA), and group comparison are each addressed as decisions based on clear criteria. An application example based on a hypothetical dataset of 500 university students (measures of AI literacy, attitude, anxiety, and learning engagement) demonstrates each stage of the process. EGA recovered four dimensions, centrality emerged as a stable measure, and the network comparison test applied to a randomly assigned grouping showed the expected invariance. The findings reveal how predictive decisions, stability diagnostics, and caution in interpretation shape the possible outcomes. The discussion section addresses common pitfalls; such as overinterpreting centrality, confusing correlation with causation, and comparing groups without caution. An annotated R code is provided to support reproducible implementation.
Keywords: psychometric network analysis, Gaussian graphical model, centrality, exploratory graph analysis, educational measurement, reproducibility

CITATION (APA)

Demir, S. (2026). Psychometric network analysis in educational sciences research: A methodological guideline for estimation, interpretation, and critical decision-making. International Journal of Professional Development, Learners and Learning, 8(2), e2612. https://doi.org/10.30935/ijpdll/18905

REFERENCES

  1. Borsboom, D., & Cramer, A. O. (2013). Network analysis: An integrative approach to the structure of psychopathology. Annual Review of Clinical Psychology, 9(1), 91-121. https://doi.org/10.1146/annurev-clinpsy-050212-185608
  2. Bringmann, L. F., Elmer, T., Epskamp, S., Krause, R. W., Schoch, D., Wichers, M., Wigman, J. T. W., & Snippe, E. (2019). What do centrality measures measure in psychological networks? Journal of Abnormal Psychology, 128(8), 892-903. https://doi.org/10.1037/abn0000446
  3. Burger, J., Isvoranu, A. M., Lunansky, G., Haslbeck, J. M. B., Epskamp, S., Hoekstra, R. H. A., Fried, E. I., Borsboom, D., & Blanken, T. F. (2023). Reporting standards for psychological network analyses in cross-sectional data. Psychological Methods, 28(4), 806-824. https://doi.org/10.1037/met0000471
  4. Choi, Y., Lim, Y., & Son, D. (2017). A semantic network analysis on the recognition of STEAM by middle school students in South Korea. Eurasia Journal of Mathematics, Science and Technology Education, 13(10), 6457-6469. https://doi.org/10.12973/ejmste/77950
  5. Christensen, A. P., Golino, H., & Silvia, P. J. (2020). A psychometric network perspective on the validity and validation of personality trait questionnaires. European Journal of Personality, 34(6), 1095-1108. https://doi.org/10.1002/per.2265
  6. Costantini, G., Epskamp, S., Borsboom, D., Perugini, M., Mõttus, R., Waldorp, L. J., & Cramer, A. O. (2015). State of the aRt personality research: A tutorial on network analysis of personality data in R. Journal of Research in Personality, 54, 13-29. https://doi.org/10.1016/j.jrp.2014.07.003
  7. Dalege, J., Borsboom, D., van Harreveld, F., van den Berg, H., Conner, M., & van der Maas, H. L. J. (2016). Toward a formalized account of attitudes: The causal attitude network (CAN) model. Psychological Review, 123(1), 2-22. https://doi.org/10.1037/a0039802
  8. Dumas, D., & Edelsbrunner, P. (2023). How to make recommendations for educational practice from correlational data using structural equation models. Educational Psychology Review, 35, Article 48. https://doi.org/10.1007/s10648-023-09770-0
  9. Epskamp, S., & Fried, E. I. (2018). A tutorial on regularized partial correlation networks. Psychological Methods, 23(4), 617-634. https://doi.org/10.1037/met0000167
  10. Epskamp, S., Borsboom, D., & Fried, E. I. (2018a). 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
  11. Epskamp, S., Cramer, A. O. J., Waldorp, L. J., Schmittmann, V. D., & Borsboom, D. (2012). qgraph: Network visualizations of relationships in psychometric data. Journal of Statistical Software, 48(4), 1-18. https://doi.org/10.18637/jss.v048.i04
  12. Epskamp, S., Waldorp, L. J., Mõttus, R., & Borsboom, D. (2018b). The Gaussian graphical model in cross-sectional and time-series data. Multivariate Behavioral Research, 53(4), 453-480. https://doi.org/10.1080/00273171.2018.1454823
  13. Eskandari, M., & Kim, Y. (2022). Semantic network analysis of the biotechnology domain in Iranian high school biology textbooks. Eurasia Journal of Mathematics, Science and Technology Education, 18(8), Article em2144. https://doi.org/10.29333/ejmste/12266
  14. Forbes, M. K., Wright, A. G. C., Markon, K. E., & Krueger, R. F. (2017). Evidence that psychopathology symptom networks have limited replicability. Journal of Abnormal Psychology, 126(7), 969-988. https://doi.org/10.1037/abn0000276
  15. Foygel, R., & Drton, M. (2010). Extended Bayesian information criteria for Gaussian graphical models. Advances in Neural Information Processing Systems, 23, 604-612.
  16. Fried, E. I., & Cramer, A. O. J. (2017). Moving forward: Challenges and directions for psychopathological network theory and methodology. Perspectives on Psychological Science, 12(6), 999-1020. https://doi.org/10.1177/1745691617705892
  17. Fried, E. I., van Borkulo, C. D., & 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
  18. Friedman, J., Hastie, T., & Tibshirani, R. (2008). Sparse inverse covariance estimation with the graphical lasso. Biostatistics, 9(3), 432-441. https://doi.org/10.1093/biostatistics/kxm045
  19. Golino, H. F., & Epskamp, S. (2017). Exploratory graph analysis: A new approach for estimating the number of dimensions in psychological research. PLoS ONE, 12(6), Article e0174035. https://doi.org/10.1371/journal.pone.0174035
  20. Golino, H., Shi, D., Christensen, A. P., Garrido, L. E., Nieto, M. D., Sadana, R., Thiyagarajan, J. A., & Martinez-Molina, A. (2020). Investigating the performance of exploratory graph analysis and traditional techniques to identify the number of latent factors: A simulation and tutorial. Psychological Methods, 25(3), 292-320. https://doi.org/10.1037/met0000255
  21. Green, T. (2016). A methodological review of structural equation modelling in higher education research. Studies in Higher Education, 41(12), 2125-2155. https://doi.org/10.1080/03075079.2015.1021670
  22. Grunspan, D. Z., Wiggins, B. L., & Goodreau, S. M. (2014). Understanding classrooms through social network analysis: A primer for social network analysis in education research. CBE—Life Sciences Education, 13(2), 167-178. https://doi.org/10.1187/cbe.13-08-0162
  23. Haslbeck, J. M. B., & Waldorp, L. J. (2020). mgm: Estimating time-varying mixed graphical models in high-dimensional data. Journal of Statistical Software, 93(8), 1-46. https://doi.org/10.18637/jss.v093.i08
  24. Hevey, D. (2018). Network analysis: A brief overview and tutorial. Health Psychology and Behavioral Medicine, 6(1), 301-328. https://doi.org/10.1080/21642850.2018.1521283
  25. Hofman, A. D., Kievit, R., Stevenson, C., Molenaar, D., Visser, I., & van der Maas, H. (2018). The dynamics of the development of mathematics skills: A comparison of theories of developing intelligence. OSF Preprints. https://doi.org/10.31219/osf.io/xa2ft
  26. Isvoranu, A., & Epskamp, S. (2023). Which estimation method to choose in network psychometrics? Deriving guidelines for applied researchers. Psychological Methods, 28(4), 925-946. https://doi.org/10.1037/met0000439
  27. Isvoranu, A., Epskamp, S., Waldorp, L. J., & Borsboom, D. (2022). Network psychometrics with R: A guide for behavioral and social scientists. Routledge. https://doi.org/10.4324/9781003111238
  28. Jones, P. J., Ma, R., & McNally, R. J. (2021). Bridge centrality: A network approach to understanding comorbidity. Multivariate Behavioral Research, 56(2), 353-367. https://doi.org/10.1080/00273171.2019.1614898
  29. Kadum, B. M. M., & Karpudewan, M. (2026). Psychometric network analysis of gendered patterns in experimental self-efficacy among Malaysian pre-university chemistry students. Chemistry Education Research and Practice. https://doi.org/10.1039/d6rp00083e
  30. Karakaya-Ozyer, K., & Aksu-Dunya, B. (2018). A review of structural equation modeling applications in Turkish educational science literature, 2010-2015. International Journal of Research in Education and Science, 4(1), 279-291. https://doi.org/10.21890/ijres.383177
  31. Li, J., & Bai, B. (2025). Mapping children’s school readiness: Insights from network analysis. Early Child Development and Care, 195(15-16), 1651-1677. https://doi.org/10.1080/03004430.2025.2608074
  32. Peng, P., & Kievit, R. A. (2020). The development of academic achievement and cognitive abilities: A bidirectional perspective. Child Development Perspectives, 14(1), 15-20. https://doi.org/10.1111/cdep.12352
  33. Rad, D., & Roman, A. (2025). Mapping the motivational architecture of ai literacy: A network analysis of teachers’ multidimensional work motivation. Journal of Psychological and Educational Research, 33(2), 203-222.
  34. Robinaugh, D. J., Hoekstra, R. H. A., Toner, E. R., & Borsboom, D. (2020). The network approach to psychopathology: A review of the literature 2008-2018 and an agenda for future research. Psychological Medicine, 50(3), 353-366. https://doi.org/10.1017/s0033291719003404
  35. Sachisthal, M. S. M., Jansen, B. R. J., Peetsma, T. T. D., Dalege, J., van der Maas, H. L. J., & Raijmakers, M. E. J. (2019). Introducing a science interest network model to reveal country differences. Journal of Educational Psychology, 111(6), 1063-1080. https://doi.org/10.1037/edu0000327
  36. Sáez-Delgado, F., Mella-Norambuena, J., & López-Angulo, Y. (2024). Psychometric properties of the socioemotional skills instrument for teachers using network approach: English and Spanish version. Frontiers in Psychology, 15. https://doi.org/10.3389/fpsyg.2024.1421164
  37. Schmittmann, V. D., Cramer, A. O., Waldorp, L. J., Epskamp, S., Kievit, R. A., & Borsboom, D. (2013). Deconstructing the construct: A network perspective on psychological phenomena. New Ideas in Psychology, 31(1), 43-53. https://doi.org/10.1016/j.newideapsych.2011.02.007
  38. Shaffer, D. W., Collier, W., & Ruis, A. R. (2016). A tutorial on epistemic network analysis: Analyzing the structure of connections in cognitive, social, and interaction data. Journal of Learning Analytics, 3(3), 9-45. https://doi.org/10.18608/jla.2016.33.3
  39. Steinley, D., Hoffman, M., Brusco, M. J., & Sher, K. J. (2017). A method for making inferences in network analysis: Comment on Forbes, Wright, Markon, and Krueger (2017). Journal of Abnormal Psychology, 126(7), 1000-1010. https://doi.org/10.1037/abn0000308
  40. Teo, T. (2011). Using structural equation modeling (SEM) in educational research: Practices and issues. International Journal of Applied Educational Studies, 10(1), 49-65.
  41. Tomei, G., Pieroni, M. F., & Tomba, E. (2022). Network analysis studies in patients with eating disorders: A systematic review and methodological quality assessment. International Journal of Eating Disorders, 55(12), 1641-1669. https://doi.org/10.1002/eat.23828
  42. van Borkulo, C. D., van Bork, R., Boschloo, L., Kossakowski, J. J., Tio, P., Schoevers, R. A., Borsboom, D., & Waldorp, L. J. (2023). Comparing network structures on three aspects: A permutation test. Psychological Methods, 28(6), 1273-1285. https://doi.org/10.1037/met0000476
  43. van der Maas, H. L. J., Dolan, C. V., Grasman, R. P. P. P., Wicherts, J. M., Huizenga, H. M., & Raijmakers, M. E. J. (2006). A dynamical model of general intelligence: The positive manifold of intelligence by mutualism. Psychological Review, 113(4), 842-861. https://doi.org/10.1037/0033-295x.113.4.842
  44. Williams, D. R., & Rast, P. (2020). Back to the basics: Rethinking partial correlation network methodology. British Journal of Mathematical and Statistical Psychology, 73(2), 187-212. https://doi.org/10.1111/bmsp.12173
  45. Yin, H., & Huang, S. (2021). Applying structural equation modelling to research on teaching and teacher education: Looking back and forward. Teaching and Teacher Education, 107, Article 103438. https://doi.org/10.1016/j.tate.2021.103438