Using Bayesian Network Analysis to Reveal Complex Natures of Relationships

TL;DR

This study uses Bayesian network analysis on attachment style survey data to uncover complex relationships between behaviors related to attachment, revealing clusters that align with previous research and providing new insights into relationship dynamics.

Contribution

It applies Bayesian network analysis to attachment data, identifying behavior clusters and correlating findings with prior factor analysis and partial correlation studies.

Findings

Identified 5 behavior clusters related to attachment styles.

Discovered network structure with 2 root nodes and 5 end nodes.

Clusters are consistent with previous factor analysis results.

Abstract

Relationships are vital for mankind in many aspects. According to Maslow hierarchy of needs, it is suggested that while a healthy relationship is an essential part of a human life that fundamentally determines our goals and purposes, an unsuccessful relationship can lead to suicide and other major psychological problems. However, a complete understanding of this topic still remains a challenge and the divorce rate is rising more than ever before to almost 50 percents. The objective of this research is to explore the association between each group of behaviors by performing Bayesian network analysis on a large publically available Experiences in Close Relationships Scale, a test of attachment style survey (ECR) data from openpsychometrics database. The resulting directed acyclic graph has 2 root nodes (Q02 from avoidant and Q05 from anxious attachment) and 5 end nodes (Q16, Q34, and Q36 from anxious attachment). The network can be divided into 5 clusters, 2 avoidance and 3 anxiety clusters. Furthermore, our list of items in the clusters are consistent with the findings of previous factor analysis studies and our estimated coefficients are significantly correlated with those of one partial correlation network study.