The MIMIC model and formative variables: problems and solutions

Nick Lee (), John W. Cadogan () and Laura Chamberlain ()
Additional contact information
Nick Lee: Aston University
John W. Cadogan: Loughborough University
Laura Chamberlain: Aston University

AMS Review, 2013, vol. 3, issue 1, No 2, 3-17

Abstract: Abstract The use of the multiple indicators, multiple causes model to operationalize formative variables (the formative MIMIC model) is advocated in the methodological literature. Yet, contrary to popular belief, the formative MIMIC model does not provide a valid method of integrating formative variables into empirical studies and we recommend discarding it from formative models. Our arguments rest on the following observations. First, much formative variable literature appears to conceptualize a causal structure between the formative variable and its indicators which can be tested or estimated. We demonstrate that this assumption is illogical, that a formative variable is simply a researcher-defined composite of sub-dimensions, and that such tests and estimates are unnecessary. Second, despite this, researchers often use the formative MIMIC model as a means to include formative variables in their models and to estimate the magnitude of linkages between formative variables and their indicators. However, the formative MIMIC model cannot provide this information since it is simply a model in which a common factor is predicted by some exogenous variables—the model does not integrate within it a formative variable. Empirical results from such studies need reassessing, since their interpretation may lead to inaccurate theoretical insights and the development of untested recommendations to managers. Finally, the use of the formative MIMIC model can foster fuzzy conceptualizations of variables, particularly since it can erroneously encourage the view that a single focal variable is measured with formative and reflective indicators. We explain these interlinked arguments in more detail and provide a set of recommendations for researchers to consider when dealing with formative variables.

Keywords: Formative variables; Measurement; Composites; Indicators; Theory; Causality; Ontology; Philosophy (search for similar items in EconPapers)
Date: 2013
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (16)

Downloads: (external link)
http://link.springer.com/10.1007/s13162-013-0033-1 Abstract (text/html)
Access to the full text of the articles in this series is restricted.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:amsrev:v:3:y:2013:i:1:d:10.1007_s13162-013-0033-1

Ordering information: This journal article can be ordered from
http://www.springer. ... gement/journal/13162

DOI: 10.1007/s13162-013-0033-1

Access Statistics for this article

AMS Review is currently edited by Manjit S. Yadav

More articles in AMS Review from Springer, Academy of Marketing Science
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().