Methods | Special Issue: Network Psychometrics

Relating network-instantiated constructs to psychological variables through network-derived metrics: An exploratory study

https://doi.org/10.56296/aip00024
Published: September 4, 2024
Copyright: The authors (CC BY 4.0)

Johal, S.K. & Rhemtulla, M. (2024). Relating network-instantiated constructs to psychological variables through network-derived metrics: An exploratory study. advances.in/psychology, 2, e939409. https://doi.org/10.56296/aip00024

Johal, Simran K., and Mijke Rhemtulla. "Relating network-instantiated constructs to psychological variables through network-derived metrics: An exploratory study." advances.in/psychology, vol. 2, no. 1, 2024, e939409. https://doi.org/10.56296/aip00024.

Johal, Simran K., and Mijke Rhemtulla. 2024. "Relating network-instantiated constructs to psychological variables through network-derived metrics: An exploratory study." advances.in/psychology 2 (1): e939409. https://doi.org/10.56296/aip00024.

Johal SK, Rhemtulla M. Relating network-instantiated constructs to psychological variables through network-derived metrics: An exploratory study. advances.in/psychology. 2024;2(1):e939409. doi:10.56296/aip00024.

Johal, S.K. and Rhemtulla, M. (2024) 'Relating network-instantiated constructs to psychological variables through network-derived metrics: An exploratory study', advances.in/psychology, 2(1), e939409. Available at: https://doi.org/10.56296/aip00024.

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Psychological researchers increasingly represent psychological constructs as mutual associations between variables using psychometric network models. Although the next step would be to study the relations this network-instantiated construct has with other psychological variables, it is not obvious how to best summarize a network in a way that allows researchers to model associations. We evaluated the predictive utility of five different network-derived metrics that were either estimated from or informed by individual network models across four intensive longitudinal datasets. These metrics included three network-structure variables (density, global strength, and maximum modularity coefficient), two network-informed variables (individuals’ average value of the most frequently central node, and their average value of their own most central node), and the average sum score of all nodes in the network. Our results showed that, for most outcomes, an individual’s average value of the most frequently central node was the best predictor. Therefore, network models can be used to inform which variable best represents the construct when modeling associations.

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