Abstract
The PLS path analysis algorithm has been used in numerous research papers in various branches of the social sciences. Prior literature has conflated the generation of coefficients without abnormally terminating, as equivalent to estimating with small samples. This research shows that: (1) PLS path estimates are biased and highly dispersed with small samples; (2) sample sizes must grow very large to control this bias and dispersion with dispersion ∝1/log (sample size) and bias ∝1/(sample size) ; and finally (3) the power of PLS hypothesis tests is low at most effect levels, leading PLS software to generate a disproportionate number of false positives.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
