Dr. Miguel Aguirre-Urreta, assistant professor of Information Systems and Quantitative Sciences, recently had his paper, "Statistical Inference with PLSc Using Bootstrap Confidence Intervals," accepted for publication in MIS Quarterly. The paper is co-authored by Mikko Rönkkö from the University of Jyväskylä in Finland.
The research examines the performance of bootstrapped confidence intervals when applied to Consistent PLS (PLSc), the latest iteration of the PLS approach to statistical analysis. The paper is an offshoot of an earlier work and was originally submitted to the journal in 2014.
MIS Quarterly is one of the leading journals in the Information Systems discipline, with a 2016 impact factor of 7.268 and a 5-year impact factor of 12.222.
Partial least squares (PLS) is one of the most popular statistical techniques in use in the Information Systems field. When applied to data originating from a common factor model, as is often the case in the discipline, PLS will produce biased estimates. A recent development, consistent PLS (PLSc) has been introduced to correct for this bias. In addition, the common practice in PLS of comparing the ratio of an estimate to its standard error to a t distribution for the purposes of statistical inference has also been challenged. We contribute to the practice of research in the IS discipline by providing evidence of the value of employing bootstrap confidence intervals in conjunction with PLSc, which is a more appropriate alternative than PLS for many of the research scenarios that are interest in the field. Such evidence is direly needed before a complete approach to the estimation of a SEM that relies on both PLSc and bootstrap CIs can be widely adopted. We also provide recommendations for researchers on the use of confidence intervals with PLSc.