Download A Primer on Partial Least Squares Structural Equation by Josephb F. Hair, G. Tomas M. Hult, Christian M. Ringle, PDF
By Josephb F. Hair, G. Tomas M. Hult, Christian M. Ringle, Marko Sarstedt
Read or Download A Primer on Partial Least Squares Structural Equation Modeling (PLS-SEM) PDF
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Extra info for A Primer on Partial Least Squares Structural Equation Modeling (PLS-SEM)
263-270). Amsterdam: North-Holland. Lohmoller, J. B. (1989). Latent variable path modeling with partial least squares. Heidelberg: Physica. Mateos-Aparicio, G. (2011). Partial Least Squares (PLS) methods: Origins, evolution, and application to social sciences. Communications in Statistics: Theory and Methods, 40(13), 2305-2317. Rigdon, E. E. (2012). Rethinking partial least squares path modeling: In praise of simple methods. Long Range Planning, 45(516), 341-358. CHAPTER 2 Specifying the Path Model and Collecting Data LEARNING OUTCOMES 1.
10 times the largest number of formative indicators used to measure a single construct, or 2. 10 times the largest number of structural paths directed at a particular construct in the structural model. This rule of thumb is equivalent to saying that the minimum sample size should be 10 times the maximum number of arrowheads pointing at a latent variable anywhere in the PLS path model. While the 10 times rule offers a rough guideline for minimum sample size requirements, PLS-SEM-like any statistical technique-requires researchers to consider the sample size against the background of the model and data characteristics (Hair, Ringle, & Sarstedt, 2011).
It is the fundamental building block of multivariate analysis. The variate is a linear combination of several variables that are chosen based on the research problem at hand. Measurement is the process of assigning numbers to a variable based on a set of rules. Multivariate measurement involves using several variables to indirectly measure a concept to improve measurement accuracy. The anticipated improved accuracy is based on the assumption that using several variables (indi cators) to measure a single concept is more likely to represent all the different aspects of the concept and thereby result in a more valid mea surement of the concept.