Abstract

There is a pervasive belief in the Management Information Systems (MIS) field that Partial Least Squares (PLS) has special abilities that make it more appropriate than other techniques, such as multiple regression and LISREL, when analyzing small sample sizes. We conducted a study using Monte Carlo simulation to compare these three relatively popular techniques for modeling relationships among variables under varying sample sizes (N = 40, 90, 150, and 200) and varying effect sizes (large, medium, small and no effect). The focus of the analysis was on comparing the path estimates and the statistical power for each combination of technique, sample size, and effect size. The results suggest that PLS with bootstrapping does not have special abilities with respect to statistical power at small sample sizes. In fact, for simple models with normally distributed data and relatively reliable measures, none of the three techniques have adequate power to detect small or medium effects at small sample sizes. These findings run counter to extant suggestions in MIS literature.

Keywords

Sample size determinationBootstrapping (finance)Statistical powerStatisticsMonte Carlo methodLISRELSample (material)Computer scienceExtant taxonPartial least squares regressionData miningEconometricsStructural equation modelingMathematics

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Year
2006
Type
article
Pages
202b-202b
Citations
299
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Dale L. Goodhue, William W. Lewis, Ron Thompson (2006). PLS, Small Sample Size, and Statistical Power in MIS Research. , 202b-202b. https://doi.org/10.1109/hicss.2006.381

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DOI
10.1109/hicss.2006.381