"Hands-On" PLS Path Modeling with SmartPLS 2.0 (Udemy.com)
Learn how to use the many features and reports inherent in SmartPLS 2.0 from a strictly "hands-on" point of view.
Created by: Geoffrey Hubona, Ph.D.
Produced in 2016
What you will learn
- Use all of the various features and reports associated with the four primary estimating algorithms built into SmartPLS 2.0 software.
- Understand how to specify, model, estimate and interpret PLS path model parameters for direct, indirect, total, group difference, mediating and moderating, and second-order effects.
- At the end of my course, students will be proficient in the use and interpretation of path modeling results estimated using SmartPLS 2.0 software.
Quality Score
Overall Score : 66 / 100
Course Description
0 software andhow to interpret the extensiveoutputted information. Participants learn the mechanics of performing a variety of tasks associated with PLS path modeling "from the ground up" and are provided with comprehensive examplesof how to model, interpret, and reportvarious PLS path modeling scenariosestimated using SmartPLS 2.
0 software. Thesepath modeling scenarios include formative, reflective, second-order, mediating, moderating, group differences, second orderand heterogeneous examples.
Thiscourse provides detailed instruction on the use of the currently available (and free)SmartPLS 2.
0 software to perform PLS path modeling. Thecourse provides detailed explanations of the computational processes of the PLS algorithm, bootstrapping, and blindfolding. "Hands-On"demonstrates live, on-screen demonstrations of the various features and functions of SmartPLS 2.
0 software. It is intended for graduate students, faculty and other researchers who seek explicit and comprehensive explanations and demonstrations of the use of all of the features, functions, and output reports in SmartPLS 2.
0 software. Take-home exercises are provided at the end ofmany sessionsto reinforce thehighlighted material. Solutionsto these exercises aretypically reviewed at the beginning of the subsequent session.
SmartPLS 2.
0 outputs prolific information and data in the four algorithms "default reports". Thecourse address the questions: What does all of the information and data in these four default reports mean? How is the default reports data interpreted? What are the formative-reflective construct distinctions in the outputted data? Which information does one need to report in submitted manuscripts and papers? How can the non-reported data be reused in subsequent analyses? What can one determine about: direct, indirect, and total effects? Group differences? Second-order constructs?
Path coefficients, weights and loadings? Latent variable scores or values? Predictive levels of variance explained in the endogenous latent variables?
Everything is provided with this course . . . all slides, PLS models, software, exercises and solutions . . . anything you see in any of the course videos is included. If you use and/or practice PLSpath modeling, and especially ifyou use SmartPLS2.
0 path modeling software, you will likely find this course to be useful to your purposes.
Who this course is for:
Anyone associate with and using PLS path modeling in graduate school, as faculty, or as practicing quantitative analysts and/or data scientists will benefit by taking this course.
Please note that SmartPLS 2.
0 software does not run well on a Mac computer.
Instructor Details
- 3.3 Rating
6 Reviews
Geoffrey Hubona, Ph.D.
Dr. Geoffrey Hubona hasheld full-time tenure-track, andtenured, assistant andassociate professorfacultypositions at 4 major state universities in the United States since 1993. Currently, he is anassociate professor of MIS at Texas A&MInternational University where he teaches for-credit courses on Business Data Visualization (undergrad), Advanced Programming using R (graduate), and Data Mining and Business Analytics (graduate).In previous academic facultypositions, he taughtdozens ofvarious statistics, business information systems, and computer science courses to undergraduate, master's and Ph.D. students. He earned a Ph.D. in Business Administration(Information Systems and Computer Science) from the University of South Florida (USF)in Tampa, FL; an MA in Economics, alsofrom USF; an MBA in Financefrom George Mason University in Fairfax, VA; and a BA in Psychology from the University of Virginia in Charlottesville, VA. He is the founder of the Georgia R School (2010-2014) and of R-Courseware (2014-Present), online educationalorganizations that teachresearch methods and quantitative analysis techniques. These research methods techniquesincludelinear and non-linear modeling, multivariate methods, data mining, programming and simulation, and structural equation modeling and partial least squares (PLS) path modeling.
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