Goodhue, Dale

Lewis, William

Thompson, Ronald

English

AIS Electronic Library (AISeL)

Association for Information SystemsAIS Electronic Library (AISeL)AMCIS 2003 Proceedings Americas Conference on Information Systems(AMCIS)December 2003Comparing Regression, PLS, and LISREL Using aMonte Carlo SimulationDale GoodhueUniversity of GeorgiaWilliam LewisUniversity of GeorgiaRonald ThompsonWake Forest UniversityFollow this and additional works at: http://aisel.aisnet.org/amcis2003This material is brought to you by the Americas Conference on Information Systems (AMCIS) at AIS Electronic Library (AISeL). It has been acceptedfor inclusion in AMCIS 2003 Proceedings by an authorized administrator of AIS Electronic Library (AISeL). For more information, please contactelibrary@aisnet.org.Recommended CitationGoodhue, Dale; Lewis, William; and Thompson, Ronald, "Comparing Regression, PLS, and LISREL Using a Monte Carlo Simulation"(2003). AMCIS 2003 Proceedings. 371.http://aisel.aisnet.org/amcis2003/3712842 2003 — Ninth Americas Conference on Information SystemsCOMPARING REGRESSION, PLS, AND LISREL USING AMONTE CARLO SIMULATIONDale L. GoodhueTerry College of BusinessUniversity of Georgiadgoodhue@terry.uga.eduWilliam LewisTerry College of BusinessUniversity of Georgiawlewis@terry.uga.eduRonald L. ThompsonBabcock Graduate School of ManagementWake Forest Universityron.thompson@mba.uwf.eduAbstractThis study uses Monte Carlo simulation to compare three relatively popular techniques, with findings that runcounter to extant suggestions in MIS literature.  We compare multiple regression, Partial Least Squares andLISREL under varying sample sizes (N = 40, 90, 150, and 200) and varying effect sizes (large, medium, smalland no effect).  The focus of the analysis was on determining how frequently Type I errors (non-existent effecterroneously detected) and Type II errors (true effect not detected) occur for each combination of technique,sample size, and effect size.   In addition, we tested the efficacy of bootstrapping versus jackknifing for the testof statistical significance of parameter estimates using PLS.  Initial results, using very simple models andnicely behaved and normally distributed data, suggest that: (1) PLS with jackknifing (to test statisticalsignificance) has unacceptably low power (too many Type II errors) except with a strong effect size; and (2)at small sample size and medium or small effect size, all techniques (including PLS, which is reputed to bestrong in this realm) have unacceptably low power.Keywords:  Structural equation modeling, statistical techniques, PLS, regression, LISREL, type I error, typeII error, sample size, effect size

Comparing Regression, PLS, and LISREL Using a Monte Carlo Simulation

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Comparing Regression, PLS, and LISREL Using a Monte Carlo Simulation

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