How compliance with behavioural measures during the initial phase of a pandemic develops over time: A longitudinal COVID‐19 study. (10th October 2022)

• Record Type: Journal Article
• Authors: Chambon, Monique; Dalege, Jonas; Borsboom, Denny; Waldorp, Lourens J.; van der Maas, Han L. J.; van Harreveld, Frenk
• Is Part Of: British journal of social psychology. Volume 62:Part 1(2023)
• Page Start: 302
• Page End: 321
• Publication Date: 2022-10-10
• Subjects: broad attitude networks -- compliance with behavioural measures -- COVID‐19 pandemic -- longitudinal -- within‐person temporal effects; Social psychology -- Periodicals; 302.05
• DOI: 10.1111/bjso.12572
• Languages: English
• ISSNs: 0144-6665

Abstract

Abstract: In this longitudinal research, we adopt a complexity approach to examine the temporal dynamics of variables related to compliance with behavioural measures during the COVID‐19 pandemic. Dutch participants ( N = 2399) completed surveys with COVID‐19‐related variables five times over a period of 10 weeks (23 April–30 June 2020). With these data, we estimated within‐person COVID‐19 attitude networks containing a broad set of psychological variables and their relations. These networks display variables' predictive effects over time between measurements and contemporaneous effects during measurements. Results show (1) bidirectional effects between multiple variables relevant for compliance, forming potential feedback loops, and (2) a positive reinforcing structure between compliance, support for behavioural measures, involvement in the pandemic and vaccination intention. These results can explain why levels of these variables decreased throughout the course of the study. The reinforcing structure points towards potentially amplifying effects of interventions on these variables and might inform processes of polarization. We conclude that adopting a complexity approach might contribute to understanding protective behaviour in the initial phase of pandemics by combining different theoretical models and modelling bidirectional effects between variables. Future research could build upon this research by studying causality with interventions and including additional variables in the networks.