Does the Assessment of Recovery Capital scale reflect a single or multiple domains?

IF 5.1 Q1 SUBSTANCE ABUSE Substance Abuse and Rehabilitation Pub Date : 2017-07-19 eCollection Date: 2017-01-01 DOI:10.2147/SAR.S138148
Stephan Arndt, Ethan Sahker, Suzy Hedden
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引用次数: 16

Abstract

Objective: The goal of this study was to determine whether the 50-item Assessment of Recovery Capital scale represents a single general measure or whether multiple domains might be psychometrically useful for research or clinical applications.

Methods: Data are from a cross-sectional de-identified existing program evaluation information data set with 1,138 clients entering substance use disorder treatment. Principal components and iterated factor analysis were used on the domain scores. Multiple group factor analysis provided a quasi-confirmatory factor analysis.

Results: The solution accounted for 75.24% of the total variance, suggesting that 10 factors provide a reasonably good fit. However, Tucker's congruence coefficients between the factor structure and defining weights (0.41-0.52) suggested a poor fit to the hypothesized 10-domain structure. Principal components of the 10-domain scores yielded one factor whose eigenvalue was greater than one (5.93), accounting for 75.8% of the common variance. A few domains had perceptible but small unique variance components suggesting that a few of the domains may warrant enrichment.

Conclusion: Our findings suggest that there is one general factor, with a caveat. Using the 10 measures inflates the chance for Type I errors. Using one general measure avoids this issue, is simple to interpret, and could reduce the number of items. However, those seeking to maximally predict later recovery success may need to use the full instrument and all 10 domains.

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恢复资本评估量表反映的是单一领域还是多个领域?
目的:本研究的目的是确定50项恢复资本评估量表是否代表一个单一的一般测量,或者多个领域是否可能在心理测量学上对研究或临床应用有用。方法:数据来自一个横断面去识别现有项目评估信息数据集,包含1138名进入物质使用障碍治疗的来访者。采用主成分分析法和迭代因子分析法对领域得分进行分析。多组因子分析提供了准验证性因子分析。结果:该解占总方差的75.24%,表明10个因素提供了相当好的拟合。然而,Tucker的因子结构和定义权重之间的同余系数(0.41-0.52)表明,对假设的10域结构的拟合很差。10域得分的主成分产生1个特征值大于1的因子(5.93),占共同方差的75.8%。少数域具有可感知的但小而独特的方差成分,这表明少数域可能需要富集。结论:我们的研究结果表明,有一个普遍的因素,有一个警告。使用这10个度量会增加出现I类错误的几率。使用一种通用的测量方法避免了这个问题,易于解释,并且可以减少项目的数量。然而,那些寻求最大限度地预测后期恢复成功的人可能需要使用完整的工具和所有10个域。
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发文量
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审稿时长
16 weeks
期刊最新文献
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