In this article, Roy Howell, and Einar Breivik, congratulate Aguirre-Urreta, M. I., Rönkkö, M., & Marakas, G. M., for their work (2016) "Omission of Causal Indicators: Consequences and Implications for Measurement," Measurement: Interdisciplinary Research and Perspectives, 14(3), 75-97. doi:10.1080/15366367.2016.1205935. They call it…

The authors begin this brief rejoinder by thanking all the authors who took time to provide comments on their work, which appeared in "Measurement: Interdisciplinary Research and Perspectives" v14 n3 2016. All commentaries appear to suggest that causal indicators cannot be used for measurement but differ in how strongly this conclusion…

In the article "Rethinking Traditional Methods of Survey Validation" Andrew Maul describes a minimalist validation methodology for survey instruments, which he suggests is widely used in some areas of psychology and then critiques this methodology empirically and conceptually. He provides a reduction ad absurdum argument by showing that…

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…

The latent factor in a causal indicator model is no more than the latent factor of the factor part of the model. However, if the causal indicator variables are well-understood and help to improve the prediction of individuals' factor scores, they can help to interpret the meaning of the latent factor. Aguirre-Urreta, Rönkkö, and Marakas (2016)…

Aguirre-Urreta, Rönkkö, and Marakas' (2016) paper in "Measurement: Interdisciplinary Research and Perspectives" (hereafter referred to as ARM2016) is an important and timely piece of scholarship, in that it provides strong analytic support to the growing theoretical literature that questions the underlying ideas behind causal and…

The authors of the focus article describe an important issue related to the use and interpretation of causal indicators within the context of structural equation modeling (SEM). In the focus article, the authors illustrate with simulated data the effects of omitting a causal indicator. Since SEMs are used extensively in the social and behavioral…

In their 2016 work, Aguirre-Urreta et al. provided a contribution to the literature on causal measurement models that enhances clarity and stimulates further thinking. Aguirre-Urreta et al. presented a form of statistical identity involving mapping onto the portion of the parameter space involving the nomological net, relationships between the…

One of the central assumptions of the causal-indicator literature is that all causal indicators must be included in the research model and that the exclusion of one or more relevant causal indicators would have severe negative consequences by altering the meaning of the latent variable. In this research we show that the omission of a relevant…

In this article, the authors extend the results of Aguirre-Urreta, Rönkkö, and Marakas (2016) concerning the omission of a relevant causal indicator by testing the validity of the assumption that causal indicators are entirely superfluous to the measurement model and discuss the implications for measurement theory. Contrary to common wisdom…