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I propose that strong claims about the superiority of latent variable (LV) structural modeling, compared to other causal approaches to nonexperimental data, are both overstated and premature. Focusing primarily on a recent article by Huba, Wingard, and Bentler (1981) appearing in this journal, I argue both that the authors' application of these models is seriously flawed and that the data employed to test the models are of questionable quality. I conclude that statements about powerful inferences that can be made from analyses of LV models should be moderated. A more prudent position is advocated in this paper: I argue that the evaluation and testing of LV models should proceed, but with caution and self-criticism; even highly sophisticated methods like LV modeling cannot substitute for the collection of high quality data and the clear operationalization of constructs and hypotheses.
John A. Martin (Fri,) studied this question.