Premium
Practical application of the vanishing tetrad test for causal indicator measurement models: An example from health‐related quality of life
Author(s) -
Bollen Kenneth A.,
Lennox Richard D.,
Dahly Darren L.
Publication year - 2009
Publication title -
statistics in medicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.996
H-Index - 183
eISSN - 1097-0258
pISSN - 0277-6715
DOI - 10.1002/sim.3560
Subject(s) - construct (python library) , latent variable , computer science , variable (mathematics) , tetrad , quality (philosophy) , task (project management) , test (biology) , causal model , econometrics , causal structure , measure (data warehouse) , structural equation modeling , latent variable model , empirical research , risk analysis (engineering) , statistics , artificial intelligence , machine learning , mathematics , data mining , medicine , economics , epistemology , philosophy , mathematical analysis , mathematical physics , biology , paleontology , management , quantum mechanics , programming language , physics
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. Copyright © 2009 John Wiley & Sons, Ltd.