Abstract: In accordance with Smith et al.’s (1999) paradigm of developing group attachment measurements, the current research developed the group attachment scale (GAS) based on the experience of close relationship inventory which was widely considered as standard measurement of dyadic attachment. To confirm such conjecture, GAS with several criterion scales were tested in a sample of 1571 employees. Data analysis incorporated item factor analysis (IFA) modeling instead of using traditional confirmatory factor analysis (CFA) based on linear regression. Within the framework of structural equation modeling (SEM), IFA could be formalized as a CFA model with latent response indicator which was supposed to be the underlying score externally manifested by categorical response, so it was also called as categorical confirmatory factor analysis (CCFA). The current research applied maximum likelihood estimator to fit a two-factor non-orthogonal CCFA model for GAS. In terms of univariate standardized Pearson residuals, the CCFA model fitted well to the empirical data. However, item and test information still had much to be desired. Validity analysis showed that group attachment moderately correlated with dyadic attachment, which probably indicated their common antecedent attachment theory. Besides, group attachment avoidance had stronger association than group attachment anxiety with group identification measures, but unexpectedly stronger with the sum-score of Rosenberg self-esteem scale. Possible reasons for such observations were further discussed.

Key words: group attachment, dyadic attachment, group identification, item factor analysis, Fisher information function

摘要： 从依恋理论出发, 基于Smith等(1999)的研究, 论述团体的功能以及可作为个体依恋对象的原因, 介绍团体依恋的概念, 并以亲密关系经历量表为基础编制了“团体依恋量表”(GAS)。对1571名企业员工进行GAS测量, 用组织认同、集体自尊、Rosenberg自尊量表等作为效标考察其信效度。采用项目因子分析(IFA)进行数据分析。采用极大似然法进行模型参数估计。单变量标准化pearson残差分析表明GAS的两因子模型拟合良好; 但基于项目反应理论的信度分析表明部分题目及测验总体信度有待提高。效度分析表明团体依恋与关系依恋相应维度间有中等程度相关, 显示两者之间的同源性。同时, 与团体依恋焦虑相比, 团体依恋回避与组织认同和自尊之间存在更强相关。

关键词: 团体依恋, 关系依恋, 组织认同, 项目因子分析, Fisher信息函数