Abstract
Formative modeling of latent constructs has produced great interest and discussion among scholars in recent years. However, confusion exists surrounding researchers’ ability to validate these models, especially with covariance-based structural equation modeling (CB-SEM) techniques. With this paper, we help to clarify these issues and explain how formatively modeled constructs can be assessed rigorously by researchers using CB-SEM capabilities. In particular, we explain and provide an applied example of a mixed-modeling technique termed multiple indicators and multiple causes (MIMIC) models. Using this approach, researchers can assess formatively modeled constructs as the final, distal dependent variable in CB-SEM structural models—something previously impossible because of CB-SEM’s mathematical identification rules. Moreover, we assert that researchers can use MIMIC models to assess the content validity of a set of formative indicators quantitatively—something considered conventionally only from a qualitative standpoint. The research example we use in this manuscript involving protection-motivated behaviors (PMBs) details the entire process of MIMIC modeling and provides a set of detailed guidelines for researchers to follow when developing new constructs modeled as MIMIC structures.
DOI
10.17705/1CAIS.03611
Recommended Citation
Posey, C., Roberts, T. L., Lowry, P., & Bennett, R. J. (2015). Multiple Indicators and Multiple Causes (MIMIC) Models as a Mixed-Modeling Technique: A Tutorial and an Annotated Example. Communications of the Association for Information Systems, 36, pp-pp. https://doi.org/10.17705/1CAIS.03611
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