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击败与捕获:比所见更复杂?应用网络分析估计高度相关构念的维度。

Defeat and entrapment: more than meets the eye? Applying network analysis to estimate dimensions of highly correlated constructs.

机构信息

Institute of Medical Psychology and Medical Sociology, University Hospital of RWTH Aachen University, Pauwelsstraße 19, 52074, Aachen, Germany.

Department of Clinical Psychology and Psychotherapy, Ruhr-Universität Bochum, Bochum, Germany.

出版信息

BMC Med Res Methodol. 2018 Jan 25;18(1):16. doi: 10.1186/s12874-018-0470-5.

Abstract

BACKGROUND

Defeat and entrapment have been shown to be of central relevance to the development of different disorders. However, it remains unclear whether they represent two distinct constructs or one overall latent variable. One reason for the unclarity is that traditional factor analytic techniques have trouble estimating the right number of clusters in highly correlated data. In this study, we applied a novel approach based on network analysis that can deal with correlated data to establish whether defeat and entrapment are best thought of as one or multiple constructs.

METHODS

Explanatory graph analysis was used to estimate the number of dimensions within the 32 items that make up the defeat and entrapment scales in two samples: an online community sample of 480 participants, and a clinical sample of 147 inpatients admitted to a psychiatric hospital after a suicidal attempt or severe suicidal crisis. Confirmatory Factor analysis (CFA) was used to test whether the proposed structure fits the data.

RESULTS

In both samples, bootstrapped exploratory graph analysis suggested that the defeat and entrapment items belonged to different dimensions. Within the entrapment items, two separate dimensions were detected, labelled internal and external entrapment. Defeat appeared to be multifaceted only in the online sample. When comparing the CFA outcomes of the one, two, three and four factor models, the one factor model was preferred.

CONCLUSIONS

Defeat and entrapment can be viewed as distinct, yet, highly associated constructs. Thus, although replication is needed, results are in line with theories differentiating between these two constructs.

摘要

背景

失败和困局已被证明与多种障碍的发展密切相关。然而,它们是否代表两个不同的结构或一个整体的潜在变量尚不清楚。造成这种不明确性的一个原因是,传统的因素分析技术在处理高度相关的数据时难以估计正确的聚类数量。在这项研究中,我们应用了一种基于网络分析的新方法,该方法可以处理相关数据,以确定失败和困局是否最好被视为一个或多个结构。

方法

解释性图形分析用于估计由 32 个项目组成的失败和困局量表在两个样本中的维度数:一个由 480 名参与者组成的在线社区样本,以及一个由 147 名因自杀企图或严重自杀危机而住院的精神病院住院患者组成的临床样本。验证性因素分析(CFA)用于测试所提出的结构是否适合数据。

结果

在两个样本中,引导探索性图形分析表明,失败和困局项目属于不同的维度。在困局项目中,检测到两个单独的维度,分别标记为内部困局和外部困局。只有在在线样本中,失败似乎是多方面的。当比较一、二、三、四因素模型的 CFA 结果时,首选一因素模型。

结论

失败和困局可以被视为不同但高度相关的结构。因此,尽管需要复制,但结果与区分这两个结构的理论一致。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/bea2/5785844/bdab7e33425b/12874_2018_470_Fig1_HTML.jpg

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