Multicollinearity and Measurement Error in Structural Equation Models: Implications for Theory Testing

Rajdeep Grewal (), Joseph A. Cote () and Hans Baumgartner ()
Additional contact information
Rajdeep Grewal: Smeal College of Business Administration, Pennsylvania State University, University Park, Pennsylvania 16802-3007
Joseph A. Cote: Washington State University, 14204 Salmon Creek Avenue, Vancouver, Washington 98686
Hans Baumgartner: Smeal College of Business Administration, Pennsylvania State University, University Park, Pennsylvania 16802-3007

Marketing Science, 2004, vol. 23, issue 4, 519-529

Abstract: The literature on structural equation models is unclear on whether and when multicollinearity may pose problems in theory testing (Type II errors). Two Monte Carlo simulation experiments show that multicollinearity can cause problems under certain conditions, specifically: (1) when multicollinearity is extreme, Type II error rates are generally unacceptably high (over 80%), (2) when multicollinearity is between 0.6 and 0.8, Type II error rates can be substantial (greater than 50% and frequently above 80%) if composite reliability is weak, explained variance ( 2 ) is low, and sample size is relatively small. However, as reliability improves (0.80 or higher), explained variance 2 reaches 0.75, and sample becomes relatively large, Type II error rates become negligible. (3) When multicollinearity is between 0.4 and 0.5, Type II error rates tend to be quite small, except when reliability is weak, 2 is low, and sample size is small, in which case error rates can still be high (greater than 50%). Methods for detecting and correcting multicollinearity are briefly discussed. However, since multicollinearity is difficult to manage after the fact, researchers should avoid problems by carefully managing the factors known to mitigate multicollinearity problems (particularly measurement error).

Keywords: multicollinearity; measurement error; structural equation models (search for similar items in EconPapers)
Date: 2004
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (147)

Downloads: (external link)
http://dx.doi.org/10.1287/mksc.1040.0070 (application/pdf)

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:inm:ormksc:v:23:y:2004:i:4:p:519-529

Access Statistics for this article

More articles in Marketing Science from INFORMS Contact information at EDIRC.
Bibliographic data for series maintained by Chris Asher ().