Lateral Collinearity and Misleading Results in Variance-Based SEM: An Illustration and Recommendations

Journal of the Association for Information Systems, Vol. 13, No. 7, 2012

40 Pages Posted: 27 Sep 2012

See all articles by Ned Kock

Ned Kock

Texas A&M International University - College of Business

Gary Lynn

Stevens Institute of Technology - School of Business

Date Written: September 26, 2012

Abstract

Variance-based structural equation modeling is extensively used in information systems research, and many related findings may have been distorted by hidden collinearity. This is a problem that may extent to multivariate analyses in general, in the field of information systems as well as in many other fields. In multivariate analyses, collinearity is usually assessed as a predictor-predictor relationship phenomenon, where two or more predictors are checked for redundancy. This type of assessment addresses vertical, or “classic,” collinearity. However, another type of collinearity may also exist, called here “lateral” collinearity. It refers to predictor-criterion collinearity. Lateral collinearity problems are exemplified based on an illustrative variance-based structural equation modeling analysis. The analysis employs WarpPLS 2.0, with the results double-checked with other statistical analysis software tools. It is shown that standard validity and reliability tests do not properly capture lateral collinearity. A new approach for the assessment of both vertical and lateral collinearity in variance-based structural equation modeling is proposed and demonstrated in the context of the illustrative analysis.

Suggested Citation

Kock, Ned and Lynn, Gary S., Lateral Collinearity and Misleading Results in Variance-Based SEM: An Illustration and Recommendations (September 26, 2012). Journal of the Association for Information Systems, Vol. 13, No. 7, 2012. Available at SSRN: https://ssrn.com/abstract=2152644

Ned Kock

Texas A&M International University - College of Business ( email )

5201 University Blvd.
Laredo, TX 78041-1900
United States

Gary S. Lynn (Contact Author)

Stevens Institute of Technology - School of Business ( email )

Hoboken, NJ 07030
United States

Here is the Coronavirus
related research on SSRN

Paper statistics

Downloads
608
Abstract Views
2,712
rank
45,072
PlumX Metrics