Research Note---Statistical Power in Analyzing Interaction Effects: Questioning the Advantage of PLS with Product Indicators

Dale Goodhue (), William Lewis () and Ronald Thompson ()
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Dale Goodhue: MIS Department, Terry College of Business, University of Georgia, Athens, Georgia 30606
William Lewis: College of Administration and Business, Louisiana Tech University, P.O. Box 10318, Ruston, Louisiana 71272
Ronald Thompson: Babcock Graduate School of Management, Wake Forest University, Winston-Salem, North Carolina 27109

Information Systems Research, 2007, vol. 18, issue 2, 211-227

Abstract: A significant amount of information systems (IS) research involves hypothesizing and testing for interaction effects. Chin et al. (2003) completed an extensive experiment using Monte Carlo simulation that compared two different techniques for detecting and estimating such interaction effects: partial least squares (PLS) with a product indicator approach versus multiple regression with summated indicators. By varying the number of indicators for each construct and the sample size, they concluded that PLS using product indicators was better (at providing higher and presumably more accurate path estimates) than multiple regression using summated indicators. Although we view the Chin et al. (2003) study as an important step in using Monte Carlo analysis to investigate such issues, we believe their results give a misleading picture of the efficacy of the product indicator approach with PLS. By expanding the scope of the investigation to include statistical power, and by replicating and then extending their work, we reach a different conclusion---that although PLS with the product indicator approach provides higher point estimates of interaction paths, it also produces wider confidence intervals, and thus provides less statistical power than multiple regression. This disadvantage increases with the number of indicators and (up to a point) with sample size. We explore the possibility that these surprising results can be explained by capitalization on chance. Regardless of the explanation, our analysis leads us to recommend that if sample size or statistical significance is a concern, regression or PLS with product of the sums should be used instead of PLS with product indicators for testing interaction effects.

Keywords: interaction effects; moderator effects; regression; PLS; product indicators; statistical power; statistical accuracy; Monte Carlo simulation (search for similar items in EconPapers)
Date: 2007
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Citations: View citations in EconPapers (20)

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