Latent Variables and Indices: Herman Wold’s Basic Design and Partial Least Squares
By Theo K. Dijkstra. 2010
239 citations recorded by the source corpus.
Subfields: Analytical Chemistry, Food Science, Statistics and Probability
View Paper: https://www.semanticscholar.org/paper/ef79bb2257b685526776b0db1f83c86fdf725092
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Analytical Chemistry
- Marko Sarstedt
- Joseph F. Hair
- Jörg Henseler
- Christian M. Ringle
- Theo K. Dijkstra
- Kai Oliver Thiele
- G. Tomas M. Hult
- Jun-Hwa (Jacky) Cheah
In Food Science
- Christian M. Ringle
- Marko Sarstedt
- Joseph F. Hair
- Jörg Henseler
- Jun-Hwa (Jacky) Cheah
- Hiram Ting
- Pauline Ash Ray
- Geoffrey S. Hubona
In Statistics and Probability
- Christian M. Ringle
- Joseph F. Hair
- Jörg Henseler
- Marko Sarstedt
- Jun-Hwa (Jacky) Cheah
- Hiram Ting
- Russell Reams
- Donna Smith
Citations in the corpus
- Consistent Partial Least Squares Path Modeling (2015, methodology): “…covariance matrix of the measurement errors Θ i is diagonal with non-negative diagonal elements, and we have for the covariance between y in and y jn 1 To keep the paper self-contained, we collect here the main algebraic results underlying PLSc (for a more elaborate explication, see Dijkstra 2010).”
- Goodness-of-fit indices for partial least squares path modeling (2013, background): “Dijkstra (1981, 2010) provides a general analysis of such equations and ensuing convergence issues. Wold’s (1982) basic PLS path modeling algorithm, which was later extended by Lohmöller (1989), includes the following three stages: (1) the iterative approximation of latent variable scores, (2) the e”
- ASSESSING REFLECTIVE MODELS IN MARKETING RESEARCH: A COMPARISON BETWEEN PLS AND PLSc ESTIMATES (2018, methodology): “In other words, PLSc corrects inter-construct correlations for attenuation so that the estimates of path coefficients and loadings become consistent as compared to the traditional PLS estimation (Dijkstra 2010; Dijkstra & Henseler 2015a, b).”
- PLS-SEM: Looking Back and Moving Forward (2014, background): “For example, McDonald (1996) proposed several criticisms to which Dijkstra (2010) persuasively replies.”
- To use or not to use ChatGPT in higher education? A study of students’ acceptance and use of technology (2023, unknown): “Another reliability coe ffi cient ρ A, based on Dijkstra (2010), was also used to provide an exact and consistent alternative.”
- An Update and Extension to SEM Guidelines for Admnistrative and Social Science Research (2011, background): “Researchers can calculate the expected degree of bias and determine the likely impact of investing in a larger sample size (Dijkstra 2010; McDonald 1996).”
- Linear indices in nonlinear structural equation models: best fitting proper indices and other composites (2011, background): “It has been shown elsewhere (Dijkstra 2008) that no matter what covariances the indicators happen to have, proper indices”
- Partial least squares structural equation modeling (PLS-SEM): A useful tool for family business researchers (2014, methodology): “The PLS-SEM method has been designed as a predictionoriented approach to SEM that relaxes the demands on data and specification of relationships set by CB-SEM (Dijkstra, 2010; Jöreskog & Wold, 1982; Rigdon, 2012).”
- PLS-SEM: Indeed a Silver Bullet (2011, background): “He also believed that estimation and description were emphasized too much and prediction too little (Dijkstra 2010).”
- On the Emancipation of PLS-SEM: A Commentary on Rigdon (2012) (2014, background): “For example, in their analysis of more than 1,000 articles published in theMIS Quarterly and Information Systems Research journals between 1990 and 2006, Shmueli and Koppius (2010) identified only 52 empirical papers that focus on prediction.”