Interpretational confounding is due to misspecification, not to type of indicator: comment on Howell, Breivik, and Wilcox (2007).
R. D. Howell, E. Breivik, and J. B. Wilcox (2007) have argued that causal (formative) indicators are inherently subject to interpretational confounding. That is, they have argued that using causal (formative) indicators leads the empirical meaning of a latent variable to be other than that assigned to it by a researcher. Their critique of causal (formative) indicators rests on several claims: (a) A latent variable exists apart from the model when there are effect (reflective) indicators but not when there are causal (formative) indicators, (b) causal (formative) indicators need not have the same consequences, (c) causal (formative) indicators are inherently subject to interpretational confounding, and (d) a researcher cannot detect interpretational confounding when using causal (formative) indicators. This article shows that each claim is false. Rather, interpretational confounding is more a problem of structural misspecification of a model combined with an underidentified model that leaves these misspecifications undetected. Interpretational confounding does not occur if the model is correctly specified whether a researcher has causal (formative) or effect (reflective) indicators. It is the validity of a model not the type of indicator that determines the potential for interpretational confounding.
Bollen
Kenneth A
KA
Department of Sociology, University of North Carolina, Chapel Hill, NC 27599-3210, USA. bollen@unc.edu
D015984:Causality; D003625:Data Collection / Q000706:statistics & numerical data*; D003627:Data Interpretation, Statistical; D006801:Humans; D012107:Research Design / Q000706:statistics & numerical data; D012942:Social Sciences / Q000706:statistics & numerical data*