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

Overview of attention for article published in BMC Medical Research Methodology, January 2018
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  • In the top 25% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (80th percentile)

Mentioned by

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9 tweeters

Citations

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9 Dimensions

Readers on

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25 Mendeley
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Title
Defeat and entrapment: more than meets the eye? Applying network analysis to estimate dimensions of highly correlated constructs
Published in
BMC Medical Research Methodology, January 2018
DOI 10.1186/s12874-018-0470-5
Pubmed ID
Authors

Thomas Forkmann, Tobias Teismann, Jana-Sophie Stenzel, Heide Glaesmer, Derek de Beurs

Abstract

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. 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. 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. 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.

Twitter Demographics

The data shown below were collected from the profiles of 9 tweeters who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 25 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 25 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 5 20%
Student > Ph. D. Student 3 12%
Student > Doctoral Student 3 12%
Student > Master 3 12%
Student > Postgraduate 2 8%
Other 6 24%
Unknown 3 12%
Readers by discipline Count As %
Psychology 14 56%
Medicine and Dentistry 2 8%
Unspecified 1 4%
Nursing and Health Professions 1 4%
Social Sciences 1 4%
Other 2 8%
Unknown 4 16%

Attention Score in Context

This research output has an Altmetric Attention Score of 9. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 03 January 2019.
All research outputs
#2,069,531
of 14,088,520 outputs
Outputs from BMC Medical Research Methodology
#334
of 1,293 outputs
Outputs of similar age
#69,711
of 358,020 outputs
Outputs of similar age from BMC Medical Research Methodology
#1
of 1 outputs
Altmetric has tracked 14,088,520 research outputs across all sources so far. Compared to these this one has done well and is in the 85th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 1,293 research outputs from this source. They typically receive more attention than average, with a mean Attention Score of 9.2. This one has gotten more attention than average, scoring higher than 74% of its peers.
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