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Practical application of the vanishing tetrad test for causal indicator measurement models: An example from health‐related quality of life

Overview of attention for article published in Statistics in Medicine, March 2009
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Title
Practical application of the vanishing tetrad test for causal indicator measurement models: An example from health‐related quality of life
Published in
Statistics in Medicine, March 2009
DOI 10.1002/sim.3560
Pubmed ID
Authors

Kenneth A. Bollen, Richard D. Lennox, Darren L. Dahly

Abstract

Researchers are often faced with the task of trying to measure abstract concepts. The most common approach is to use multiple indicators that reflect an underlying latent variable. However, this 'effect indicator' measurement model is not always appropriate; sometimes the indicators instead cause the construct of interest. While the notion of 'causal indicators' has been known for some time, it is still too often ignored. However, there are limited means to determine whether a possible indicator should be treated as a cause or an effect of the latent construct of interest. Perhaps the best empirical way is to use the vanishing tetrad test (VTT), yet this method is still often overlooked. We speculate that one reason for this is the lack of published examples of its use in practice, written for an audience without extensive statistical training. The goal of this paper was to help fill this gap in the literature-to provide a basic example of how to use the VTT. We illustrated the VTT by looking at multiple items from a health related quality of life instrument that seem more likely to cause the latent variable rather than the other way around.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 46 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Spain 2 4%
Brazil 2 4%
Ireland 1 2%
United States 1 2%
Unknown 40 87%

Demographic breakdown

Readers by professional status Count As %
Researcher 12 26%
Professor 5 11%
Student > Doctoral Student 4 9%
Other 3 7%
Student > Ph. D. Student 3 7%
Other 10 22%
Unknown 9 20%
Readers by discipline Count As %
Psychology 7 15%
Social Sciences 6 13%
Medicine and Dentistry 6 13%
Business, Management and Accounting 5 11%
Environmental Science 3 7%
Other 8 17%
Unknown 11 24%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 1. 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 13 April 2019.
All research outputs
#19,256,319
of 24,520,935 outputs
Outputs from Statistics in Medicine
#2,833
of 4,015 outputs
Outputs of similar age
#89,303
of 97,458 outputs
Outputs of similar age from Statistics in Medicine
#19
of 21 outputs
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