Time to Intervene: A Continuous-Time Approach to Network Analysis and Centrality

Oisín Ryan () and Ellen L. Hamaker
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Oisín Ryan: Utrecht University
Ellen L. Hamaker: Utrecht University

Psychometrika, 2022, vol. 87, issue 1, No 9, 214-252

Abstract: Abstract Network analysis of ESM data has become popular in clinical psychology. In this approach, discrete-time (DT) vector auto-regressive (VAR) models define the network structure with centrality measures used to identify intervention targets. However, VAR models suffer from time-interval dependency. Continuous-time (CT) models have been suggested as an alternative but require a conceptual shift, implying that DT-VAR parameters reflect total rather than direct effects. In this paper, we propose and illustrate a CT network approach using CT-VAR models. We define a new network representation and develop centrality measures which inform intervention targeting. This methodology is illustrated with an ESM dataset.

Keywords: dynamical network analysis; continuous-time modeling; centrality; intensive longitudinal data; experience sampling methodology (search for similar items in EconPapers)
Date: 2022
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DOI: 10.1007/s11336-021-09767-0

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