What Is the Latent Variable in Causal Indicator Models?

Autor/in	Howell, Roy D.
Titel	What Is the Latent Variable in Causal Indicator Models?
Quelle	In: Measurement: Interdisciplinary Research and Perspectives, 12 (2014) 4, S.141-145 (5 Seiten)Infoseite zur Zeitschrift PDF als Volltext Verfügbarkeit
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	1536-6367
DOI	10.1080/15366367.2014.980105
Schlagwörter	Stellungnahme; Statistical Analysis; Measurement; Causal Models; Data Interpretation; Predictor Variables + Suchen Sie Ihr Suchwort? Statistische Analyse; Messverfahren; Kausalanalyse; Data evaluation; Datenauswertung; Prädiktor
Abstract	Building on the work of Bollen (2007) and Bollen & Bauldry (2011), Bainter and Bollen (this issue) clarifies several points of confusion in the literature regarding causal indicator models. This author would certainly agree that the effect indicator (reflective) measurement model is inappropriate for some indicators (such as the social interaction or exposure to media violence examples they present) and that to try to model these using a reflective model would result in a misspecified model. He also agrees that clearly distinguishing between causal indicator models and composite indicators is beneficial. However, he disagrees fundamentally with regard to the interpretation of the latent variable (?1 in Figures 1 and 2 of Bainter & Bollen) in terms of its causal indicators, that is, as "the latent variable defined by causal indicators" (p. 133). In this author's view, it is not, and such an interpretation is misleading at best. He states that the latent variable in a causal indicators model is a common factor accounting for the covariance among its outcomes, and that it should not be construed in terms of the content of the causal indicators. If the outcomes included to identify the model are reflective measures such that the factor has a clear interpretation, all is well. The so-called causal indicants are simply predictors, call them what you may. If the outcomes are variables of interest whose covariance lacks a clear interpretation, there is a problem. The latent variable's meaning is indeterminate, but it cannot be inferred from the content of the causal indicators. Doing so can lead to interpretational confounding. Forming a composite with weights not determined with respect to an outcome variable or variables is a possible solution, as is using the causal indicators individually in a model without creating a composite. The use of the causal indicators individually may sacrifice parsimony, but could provide useful information and conceptual clarity (McGrath, 2005). (ERIC).
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Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2017/4/10