The DOI matches its Crossref record; 100 of the 100 references it cites were found in a registry or an index.
An Introduction to Network Psychometrics: Relating Ising Network Models to Item Response Theory Models
Open accessAbstract
In recent years, network models have been proposed as an alternative representation of psychometric constructs such as depression. In such models, the covariance between observables (e.g., symptoms like depressed mood, feelings of worthlessness, and guilt) is explained in terms of a pattern of causal interactions between these observables, which contrasts with classical interpretations in which the observables are conceptualized as the effects of a reflective latent variable. However, few investigations have been directed at the question how these different models relate to each other. To shed light on this issue, the current paper explores the relation between one of the most important network models-the Ising model from physics-and one of the most important latent variable models-the Item Response Theory (IRT) model from psychometrics. The Ising model describes the interaction between states of particles that are connected in a network, whereas the IRT model describes the probability distribution associated with item responses in a psychometric test as a function of a latent variable. Despite the divergent backgrounds of the models, we show a broad equivalence between them and also illustrate several opportunities that arise from this connection.
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Network analysis of multivariate data in psychological science
Borsboom, D., Deserno, M. K., Rhemtulla, M., Epskamp, S., Fried, E. I., McNally, R. J., Robinaugh, D. J., Perugini, M., Dalege, J., Costantini, G., et al. (2021) · Nature Reviews Methods Primers · doi:10.1038/s43586-021-00055-w · Cited by 1,371
Self-citationRobinaugh, D. J., Hoekstra, R. H. A., Toner, E. R., & Borsboom, D. (2020) · Psychological Medicine · doi:10.1017/s0033291719003404 · Cited by 861
Self-citationBrain disorders? Not really: Why network structures block reductionism in psychopathology research
Borsboom, D., Cramer, A. O. J., & Kalis, A. (2019) · Behavioral and Brain Sciences · doi:10.1017/s0140525x17002266 · Cited by 468
Self-citationA Conceptual Introduction to Bayesian Model Averaging
Hinne, M., Gronau, Q. F., van den Bergh, D., & Wagenmakers, E.-J. (2020) · Advances in Methods and Practices in Psychological Science · doi:10.1177/2515245919898657 · Cited by 318
Hallquist, M. N., Wright, A. G. C., & Molenaar, P. C. M. (2021) · Multivariate Behavioral Research · doi:10.1080/00273171.2019.1640103 · Cited by 292
Metadata
- Authors
- Marsman, M., Borsboom, D., Kruis, J., Epskamp, S., van Bork, R., Waldorp, L. J., Maas, H. L. J. V. D., & Maris, G.
- Venue
- Multivariate Behavioral Research
- Publisher
- Informa UK Limited
- Published
- 2017-11-07
- Volume
- 53
- Issue
- 1
- Pages
- 15-35
- ISSN
- 0027-3171 (ISSN-L)
- 1532-7906
- OpenAlex
- W2767326152
- Cited by
- 287
- References
- 100
- FWCI
- 26.84
- Type
- journal article
- Language
- en
- Open access
- hybrid
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