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

Dale L. Goodhue · Ron A. Thompson

232 citations0/0 files storedInformation Systems Research

No readable copy stored yet — this record is metadata only.

No open-access location is known for this record yet.

Literature note Cite in a project Citation map

Psychometric Methodologies and Testing Technology Adoption and User Behaviour Statistical Methods and Applications

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.

Paper brief

Loading…

Cite this work

Citation unavailable for this work.

BibTeX RIS CSL-JSON

No public copy is stored for this record, so only the metadata is cited.

Citations in this library

Citation map →

Stored only when both papers are in the corpus — 232 citations exist upstream.

References

1981Evaluating Structural Equation Models with Unobservable Variables and Measurement Error 1998Modern Methods for Business Research 2003A Partial Least Squares Latent Variable Modeling Approach for Measuring Interaction Effects: Results from a Monte Carlo Simulation Study and an Electronic-Mail Emotion/Adoption Study 1989The Concept of Fit in Strategy Research: Toward Verbal and Statistical Correspondence 2018The Concept of Fit in Strategy Research: Towards Verbal and Statistical Correspondence 1998Advanced Structural Equation Modeling: Issues and Techniques 1984Estimating the nonlinear and interactive effects of latent variables.2003In Pursuit of Moderation: Nine Common Errors and Their Solutions1

Cited by

2017Exploring the effect of social media information quality, source credibility and reputation on informational fit-to-task: Moderating role of focused immersion 2013PREDICTIVE VALIDITY AND FORMATIVE MEASUREMENT IN STRUCTURAL EQUATION MODELING : EMBRACING PRACTICAL RELEVANCE 2018From composites to factors: B ridging the gap between PLS and covariance‐based structural equation modelling 2017High-performance work system and employee creativity

Explore the map →

Papers that cite the same references — the relational fallback while embeddings are still backfilling.

Imported from OpenAlex (CC0) — nothing here is generated.