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康复资本量表的评估反映的是单一领域还是多个领域?

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

作者信息

Arndt Stephan, Sahker Ethan, Hedden Suzy

机构信息

Iowa Consortium for Substance Abuse Research and Evaluation.

Department of Psychiatry, Carver College of Medicine.

出版信息

Subst Abuse Rehabil. 2017 Jul 19;8:39-43. doi: 10.2147/SAR.S138148. eCollection 2017.

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.

摘要

目的

本研究的目的是确定50项康复资本评估量表是代表单一的综合测量指标,还是多个领域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的、已去除身份标识的现有项目评估信息数据集,有1138名进入物质使用障碍治疗的客户。对领域得分进行主成分分析和迭代因子分析。多组因子分析提供了一种准验证性因子分析。

结果

该解决方案解释了总方差的75.24%,表明10个因子提供了合理的良好拟合。然而,因子结构与定义权重之间的塔克拟合系数(0.41 - 0.52)表明与假设的10领域结构拟合不佳。10个领域得分的主成分产生了一个特征值大于1(5.93)的因子,占共同方差的75.8%。一些领域有明显但较小的独特方差成分,表明其中一些领域可能需要充实。

结论

我们的研究结果表明存在一个一般因子,但有一个警告。使用10个测量指标会增加I型错误的可能性。使用一个一般测量指标可以避免这个问题,易于解释,并且可以减少项目数量。然而,那些试图最大程度预测后期康复成功的人可能需要使用完整的工具和所有10个领域。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f805/5530855/60b3ee67a969/sar-8-039Fig1.jpg

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