Title
PLS, Small Sample Size, and Statistical Power in MIS Research
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.
Year
DOI
Venue
2006
10.1109/HICSS.2006.381
HICSS
Keywords
Field
DocType
sample size,special ability,mis research,varying sample size,statistical power,varying effect size,medium effect,effect size,small sample size,mis literature,adequate power,knowledge management,statistical analysis,monte carlo simulation,management information system,multiple regression,management information systems,normal distribution,information analysis
Econometrics,Monte Carlo method,Computer science,Bootstrapping,Partial least squares regression,Extant taxon,Statistics,Statistical power,LISREL,Sample size determination,Linear regression
Conference
ISBN
Citations 
PageRank 
0-7695-2507-5
71
2.98
References 
Authors
4
3
Name
Order
Citations
PageRank
Dale Goodhue158360.90
William Lewis2712.98
Ron Thompson3712.98