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A factor analysis of the important people inventory

机译:重要人物清单的因素分析

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Aims:For well over a decade, the Important People Inventory (IP, Clifford and Longabaugh, 1991; Clifford et al., 1992) has been used to collect a wide range of valuable information regarding network support for alcohol use. However, because of psychometric limitations and varied adaptations of the IP, the following study performed factor analyses to develop a more structurally consistent model of the scaleas compared to the existing model.Methods A first principal components analysis (Varimax rotation) was run on the indices of the IP using data from a national investigation of residents within a recovery community (N = 897). Next, a second principal components analysis was run using data collected from participants recruited from inpatient treatment settings (N = 150).Results Results indicated a nine-index, three-factor model, which explained about two thirds of the common variance. These three factors included: Support for Drinking from Network Members (3 items), Drinking Behaviours of Network Members (3 items), and General Social Support (3 items).ConclusionsResults of both studies suggest that the IP fits a multi-component structure. It is recommended that Drinking Behaviours of Network Members be examined for predictive validity and that General Social Support be removed from the scale or have additional items added.
机译:目的:十多年来,重要人物清单(IP,Clifford和Longabaugh,1991; Clifford等,1992)已被用来收集有关酒精使用网络支持的各种有价值的信息。然而,由于心理测验的局限性和IP的适应性变化,以下研究进行了因子分析,以建立与现有模型相比结构更一致的量表模型。方法对指标进行首次主成分分析(Varimax旋转)使用来自对恢复社区内居民的全国调查得出的数据(N = 897)。接下来,使用从住院治疗设置(N = 150)招募的参与者收集的数据进行第二次主成分分析。结果结果显示九指数三因素模型,该模型解释了三分之二的常见差异。这三个因素包括:网络成员的饮酒支持(3个项目),网络成员的饮酒行为(3个项目)和一般社会支持(3个项目)。结论两项研究的结果都表明IP符合多部分结构。建议检查网络成员的饮酒行为,以预测其有效性,并从体重秤中删除“一般社会支持”或添加其他项目。

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