Health psychology research examines how the complex interactions between biological, psychological, and social factors influence health and well-being. Network analysis offers the potential for insight into structural relations among core psychological processes to inform the health psychology science and practice. However, replication of the network in larger samples to produce more stable and robust estimates of network indices is required.Ĭonclusions: The reported network reveals that the affective attitudinal variable was the most important node in the network and therefore interventions could prioritise targeting changing the emotional responses to exercise. The affective attitude item was the central node in the network. The network split into three distinct communities of items. Results: The network structure reveals the variation in relationships between the items. The EBICglasso was applied to the partial correlation matrix. Data were analysed to examine the network structure of the variables. The survey comprised items on attitudes, normative beliefs, perceived behavioural control, and intentions. Method: Participants ( n = 200) completed a TPB survey on regular exercise. This paper provides an overview of networks, how they can be visualised and analysed, and presents a simple example of how to conduct network analysis in R using data on the Theory Planned Behaviour (TPB). Network analysis provides the capacity to estimate complex patterns of relationships and the network structure can be analysed to reveal core features of the network. Networks comprise graphical representations of the relationships (edges) between variables (nodes). Objective : The present paper presents a brief overview on network analysis as a statistical approach for health psychology researchers.
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