非指向性データのためのソーシャルリレーションズモデルの紹介

The study of dyadic data has become essential to understanding family relationships. While directed dyadic data capture a person's relationship to a partner, resulting in two scores per dyad, undirected dyadic data measure something that is common to two people using a single score, for example, the distance two people stand from each other while conversing. This article introduces a modification of the family social relations model specifically developed for undirected data, a new framework to analyze undirected data for distinguishable dyad members. The model allows researchers to determine the contribution of individual-, dyadic-, and family-level components on undirected measures. We illustrate the model using data on shared family meals among dyad members in 99 two-parent two-child families. The analysis reveals that factors at all three levels of analysis determine how often two family members share a meal, but characteristics of the family as a group are most important. We also introduce an innovative approach to estimating the family factor, one that allows different dyadic scores to be affected differently by the family climate. Finally, we introduce an online app that implements the analysis of the family social relations model for undirected data, minimizing the need for confirmatory factor analysis skills. (PsycInfo Database Record (c) 2026 APA, all rights reserved).