In this commentary from Issue 14, n3, authors John Cadogan and Nick Lee applaud the paper by Aguirre-Urreta, Rönkkö, and Marakas "Measurement: Interdisciplinary Research and Perspectives", 14(3), 75-97 (2016), since their explanations and simulations work toward demystifying causal indicator models, which are often used by scholars wishing to measure latent variables. In their comment they focus on the utility of using causal indicator models to provide information on latent variables, reflecting on the conditions under which causal indicator models may provide valid conclusions about the variance of the latent variables they purportedly measure and specifying the conditions under which causal indicator models should not be used in this regard. They conclude that causal indicator models (a) are often used inappropriately and so do not provide valid information on the latent variable one wishes to model, (b) are (possibly too) unwieldy as methods of providing valid information on a focal latent variable, and (c) should probably be abandoned in favor of reflective methods for measuring constructs of interest until additional research is conducted to provide guidance on how to use them to make valid conclusions about focal latent variables.