Conceptual clarity in measurement—Constructs, composites, and causes: a commentary on Lee, Cadogan and Chamberlain

Roy D. Howell ()
Additional contact information
Roy D. Howell: Texas Tech University

AMS Review, 2013, vol. 3, issue 1, No 3, 18-23

Abstract: Abstract In an insightful and important article, Lee et al. (2013, this issue) clearly point out the problems with so-called formative measurement. In particular, they suggest that the MIMIC model formulation, as currently conceptualized, does not provide a solution. Their central thesis is that, in a MIMIC model, the supposedly formatively measured latent variable is empirically a reflective latent variable depending entirely on the endogenous variables included. They then look at composite variables as a possible solution. This commentary seeks to reinforce their central thesis, providing additional evidence and support. I also attempt to clarify the distinction between two types of models discussed in the article as MIMIC models. I then examine the use of composite variables, focusing on potential information loss and issues concerning conceptual clarity. I conclude that composite variables should not be routinely employed in theory testing research, and their use must be clearly justified.

Keywords: Formative measurement; Reflective measurement; Composite variables (search for similar items in EconPapers)
Date: 2013
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (5)

Downloads: (external link)
http://link.springer.com/10.1007/s13162-013-0036-y 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_s13162-013-0036-y

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

DOI: 10.1007/s13162-013-0036-y

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 ().