AIM This study presents a measure of Social Recovery Capital (SRC) derived from the Important People and Activities instrument (IPA). METHODS The sample comprised young adults who participated in the Collaborative Study on the Genetics of Alcoholism, a high-risk family study of alcohol use disorder (N = 2472). Exploratory and confirmatory factor analysis identified influential items and factor structure, adjusting for family relatedness. The final scale was tested for reliability and validity. RESULTS Factor analysis retained 10 items loading on three factors (Network Abstinence Behaviors, Basic Network Structure and Network Importance) that together explained 42% of the variance in SRC. The total model showed adequate fit (Comparative Fit Index = 0.95; Tucker Lewis Index = 0.93; Root Mean Square Error of Approximation = 0.06; Standardized Root Mean Squared Residual = 0.05) and acceptable reliability (α = 0.60; McDonald's ω = 0.73) and correlated with validation measures mostly in the weak to moderate range. Due to variable factor scores for reliability and validity, we only recommend using the total score. CONCLUSION The SRC-IPA is a novel measure of SRC derived from the IPA that captures social network data and has applications in research and clinical work. Secondary data analyses using the SRC-IPA in studies that collected the IPA can further demonstrate the interaction of SRC with a wide variety of clinical indicators and demographic characteristics, making it a valuable addition to other measures of SRC.

中文翻译：

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从重要的人和活动工具中导出社会恢复资本的衡量标准：构建和心理测量特性。


AIM 这项研究提出了一种源自重要人物和活动工具 (IPA) 的社会恢复资本 (SRC) 衡量标准。方法 样本由参加酒精中毒遗传学合作研究的年轻人组成，这是一项酒精使用障碍高风险家庭研究（N = 2472）。探索性和验证性因素分析确定了有影响的项目和因素结构，并调整了家庭相关性。最终量表进行了信度和效度测试。结果因素分析保留了三个因素（网络禁欲行为、基本网络结构和网络重要性）的 10 个项目，这三个因素共同解释了 SRC 中 42% 的方差。总模型显示出足够的拟合度（比较拟合指数 = 0.95；Tucker Lewis 指数 = 0.93；近似均方根误差 = 0.06；标准化均方根残差 = 0.05）和可接受的可靠性（α = 0.60；麦当劳 ω = 0.73）与验证措施的相关性大多在弱到中等范围内。由于信度和效度的因素分数存在差异，我们仅建议使用总分。结论 SRC-IPA 是一种源自 IPA 的新型 SRC 测量方法，可捕获社交网络数据并在研究和临床工作中得到应用。在收集 IPA 的研究中使用 SRC-IPA 进行二次数据分析可以进一步证明 SRC 与各种临床指标和人口特征的相互作用，使其成为 SRC 其他指标的有价值的补充。