Testing complex models with small sample sizes: A historical overview and empirical demonstration of what Partial Least Squares (PLS) can offer differential psychology

By H. Willaby, Daniel S. J. Costa, B. Burns, C. MacCann, R. Roberts. 2015

393 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Experimental and Cognitive Psychology, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/0d9de3b27d503d36af3d206e2873ca7ed83309ab

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Citations in the corpus

Sampling weight adjustments in partial least squares structural equation modeling: guidelines and illustrations (2020, methodology): “method is applied in a variety of disciplines – including agricultural science, engineering, environmental science, medicine, and psychology (e.g. Avkiran, 2018; Willaby et al., 2015) – its main area of application is in business research, particularly information systems (Cheah et al.”
ASSESSING REFLECTIVE MODELS IN MARKETING RESEARCH: A COMPARISON BETWEEN PLS AND PLSc ESTIMATES (2018, background): “…et al., 2011), family business (Sarstedt et al., 2014b), management information systems (Ringle et al., 2012), marketing (Hair et al., 2012a), operations management (Peng & Lai 2012), strategic management (Hair et al., 2012b), psychology (Willaby et al., 2015) and tourism (do Valle & Assaker 2016).”
On Comparing Results from CB-SEM and PLS-SEM: Five Perspectives and Five Recommendations (2017, methodology): “When Wold (1975), for example, described his “Nonlinear Iterative Partial Least Squares” method, a precursor to PLS-SEM, he wrote about the composites in his model serving as proxies for the group of observed variables that formed each composite.”
On the adoption of partial least squares in psychological research: Caveat emptor (2015, methodology): “The final area where Willaby et al. (2015) claim an advantage of PLS over SEM is formative measurement.”
How can SMEs successfully navigate VUCA environment: The role of agility in the digital transformation era (2022, methodology): “The adoption of the PLS-SEM technique was useful because it helps to yield accurate results in exploratory studies (Hair et al., 2019) and – at the same time – it allows the testing of the model without sample restrictions on the survey (Willaby et al., 2015).”
Structural model robustness checks in PLS-SEM (2020, background): “PLS-SEM’s impact extends into various other fields of scientific inquiry such as engineering (e.g. Durdyev et al., 2018), medicine (e.g. Menni et al., 2018), and psychology (e.g. Willaby et al., 2015).”