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- W2024285847 abstract "We are delighted to see Bainter and Bollen’s excellent paper as a focus article in Measurement. In our view, psychological researchers who use SEM rely too reflexively on reflective measurement, without sufficiently considering whether their indicators are likely to be caused by the latent construct. When causality flows from indicators to the construct, fitting a reflective will result in misfit, misspecification, incorrect parameter estimates, and fruitless discussion about the underlying factor structure of a questionnaire. This is a topic that requires greater awareness. Bainter and Bollen defend the position that causal is just as valid as reflective and that it leads to no great interpretational problems. While we wholeheartedly agree that not all indicators should be treated as reflective, we find their position potentially misleading. The relation between causal indicators and the latent variable they indicate is insufficiently restrictive to allow causal indicators to uniquely pick out a latent variable. In fact, the identification of a latent variable as the target of a instrument requires effect indicators. However, calling the causal indicator side of a a measurement model implies that the causal is in fact sufficient to identify a latent variable, obscuring the need for additional effect indicators. In our view, the debate over interpretational confounding is just one consequence of the ensuing confusion, and much would be gained by simply abandoning the terminology of in discussing models with causal indicators." @default.
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- W2024285847 date "2015-01-02" @default.
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- W2024285847 title "Calling Models With Causal Indicators “Measurement Models” Implies More Than They Can Deliver" @default.
- W2024285847 doi "https://doi.org/10.1080/15366367.2015.1016343" @default.
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