The DOI matches its Crossref record; 40 of the 40 references it cites were found in a registry or an index.
How well do network models predict observations? On the importance of predictability in network models
Open accessAbstract
Network models are an increasingly popular way to abstract complex psychological phenomena. While studying the structure of network models has led to many important insights, little attention has been paid to how well they predict observations. This is despite the fact that predictability is crucial for judging the practical relevance of edges: for instance in clinical practice, predictability of a symptom indicates whether an intervention on that symptom through the symptom network is promising. We close this methodological gap by introducing nodewise predictability, which quantifies how well a given node can be predicted by all other nodes it is connected to in the network. In addition, we provide fully reproducible code examples of how to compute and visualize nodewise predictability both for cross-sectional and time series data.
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Cited by
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
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Metadata
- Authors
- Haslbeck, J. M. B., & Waldorp, L. J.
- Venue
- Behavior Research Methods
- Published
- 2017-07-17
- Volume
- 50
- Issue
- 2
- Pages
- 853-861
- ISSN
- 1554-351X (ISSN-L)
- 1554-3528
- OpenAlex
- W2543011599
- Cited by
- 630
- References
- 40
- FWCI
- 22.42
- Type
- journal article
- Language
- en
- Open access
- hybrid
- Funders
- University of Amsterdam
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- Haslbeck, J. M. B., & Waldorp, L. J. (2018). How well do network models predict observations? On the importance of predictability in network models. Behavior Research Methods. https://doi.org/10.3758/s13428-017-0910-x
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