Structural equation modeling with LISREL: essentials and advances
Structural equation modeling with LISREL: essentials and advances
Factors of success for end-user computing
Communications of the ACM
PLS, Small Sample Size, and Statistical Power in MIS Research
HICSS '06 Proceedings of the 39th Annual Hawaii International Conference on System Sciences - Volume 08
Computational Statistics & Data Analysis
Journal of Management Information Systems
Leveraging Standard Electronic Business Interfaces to Enable Adaptive Supply Chain Partnerships
Information Systems Research
Editor's comments: PLS: a silver bullet?
MIS Quarterly
A critical look at partial least squares modeling
MIS Quarterly
MIS Quarterly
IT service climate, antecedents and IT service quality outcomes: Some initial evidence
The Journal of Strategic Information Systems
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There is a pervasive belief in the MIS research community that PLS has advantages over other techniques when analyzing small sample sizes or data with non-normal distributions. Based on these beliefs, major MIS journals have published studies using PLS with sample sizes that would be deemed unacceptably small if used with other statistical techniques. We used Monte Carlo simulation more extensively than previous research to evaluate PLS, multiple regression, and LISREL in terms of accuracy and statistical power under varying conditions of sample size, normality of the data, number of indicators per construct, reliability of the indicators, and complexity of the research model. We found that PLS performed as effectively as the other techniques in detecting actual paths, and not falsely detecting non-existent paths. However, because PLS (like regression) apparently does not compensate for measurement error, PLS and regression were consistently less accurate than LISREL. When used with small sample sizes, PLS, like the other techniques, suffers from increased standard deviations, decreased statistical power,and reduced accuracy. All three techniques were remarkably robust against moderate departures from normality, and equally so. In total, we found that the similarities in results across the three techniques were much stronger than the differences.