Multiple Indicators and Multiple Causes (MIMIC) Models as a Mixed-Modelling Technique: A Tutorial and an Annotated Example
Communications of the Association for Information Systems, vol. 36(11)
32 Pages Posted: 17 Nov 2014 Last revised: 5 Apr 2015
Date Written: September 10, 2014
Formative modelling of latent constructs has produced great interest and discussion among scholars in recent years. However, confusion exists surrounding the ability of researchers to validate these models especially with covariance-based structural equation modelling (CB-SEM) techniques. This manuscript helps to clarify these issues and explains how formatively modelled constructs can be assessed rigorously by researchers using CB-SEM capabilities. In particular, we explain and provide an applied example of a mixed-modelling technique termed multiple indicators and multiple causes (MIMIC) models. Using this approach, researchers can assess formatively modelled constructs as the final, distal dependent variable in structural models, which modelling is traditionally impossible due to the mathematical identification rules of CB-SEM. 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. Our research example used in this manuscript involving protection-motivated behaviors (PMBs) details the entire process of MIMIC modelling and provides a set of detailed guidelines for researchers to follow when developing new constructs modelled as MIMIC structures.
Keywords: Methodology, Formative Construct Validation, MIMIC Modelling, Covariance-based SEM, Protection-Motivated Behaviors
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