Accounting for sampling weights in PLS path modeling: Simulations and empirical examples
By Jan-Michael Becker, Ida Rosnita Ismail. 2016
76 citations recorded by the source corpus.
Subfields: Information Systems and Management, Organizational Behavior and Human Resource Management
View Paper: https://www.semanticscholar.org/paper/fdc36b1117b0c905e9a32f215d2cf5b8882744b0
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Information Systems and Management
- Florian Schuberth
- Ana Castillo López
- Jose Benitez
- Benedita Araujo
- Tiago Oliveira
- Jörg Henseler
- Carlos Tam
- Christian M. Ringle
In Organizational Behavior and Human Resource Management
- Florian Schuberth
- Ana Castillo López
- Jose Benitez
- Tiago Oliveira
- Carlos Tam
- Benedita Araujo
- Jörg Henseler
- Marko Sarstedt
Citations in the corpus
- How to perform and report an impactful analysis using partial least squares: Guidelines for confirmatory and explanatory IS research (2020, background): “It can incorporate sampling weights known as weighted partial least squares (WPLS, see [50]), deal with correlated measurement errors within a block of indicators [51], and address multicollinearity among the constructs in the structural model [52].”
- Why do people share their travel experiences on social media? (2020, methodology): “We applied post-stratification weights, i.e. the weighted PLS algorithm (Becker & Ismail, 2016), based on gender and age.”