Social science researchers need to use modeling to understand complex real-life phenomena. But how does a researcher decide which of the available models is most appropriate? In this video, MARCO SARSTEDT analyzes the metrics employed by researchers in assessing PLS (Partial Least Squares) models, outlining how such assessments can be optimized. Running a Monte Carlo simulation study, Sarstedt explains the inadequacies (for PLS researchers) of commonly used metrics like R² and the Goodness of Fit index by comparison with information criteria like BIC and GM. Offering suggestions as to how these metrics should ideally be implemented, Sarstedt notes that further work is required to assess whether their advantages extend to studies which employ more complex modeling.
DOI:
https://doi.org/10.21036/LTPUB10696
Institution
Ludwig Maximilian University Munich (Ludwig-Maximilians-Universität München)
"LMU Munich is one of the leading universities in Europe. Carrying on a tradition that goes back over 500 years, LMU offers challenging study programs and provides an ideal environment for top-level research. "Introducing LMU" gives an insight into learning and teaching as well as research and life at LMU." ( Source )
Show more
Original publication
PLS-Based Model Selection: The Role of Alternative Explanations in Information Systems Research
Journal of the Association for Information Systems
Published in 2019
Beyond
A Ground-breaking Scientific Revolution
An Alarming Challenge for Society
If I Had a Second Life
A Personal Reading Recommendation