‘Hands-On’ PLS Path Modeling with SmartPLS is an 8-session course that provides extensive, detailed knowledge about how to use SmartPLS 2.0 software and how to interpret the extensive outputted 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 examples of how to model, interpret, and report various PLS path modeling scenarios estimated using SmartPLS 2.0 software. These path modeling scenarios include formative, reflective, second-order, mediating, moderating, group differences, second order and heterogeneous examples.
This course provides detailed instruction on the use of the currently available (and free) SmartPLS 2.0 software to perform PLS path modeling. The course 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 of many sessions to reinforce the highlighted material. Solutions to these exercises are typically reviewed at the beginning of the subsequent session.
SmartPLS 2.0 outputs prolific information and data in the four algorithms’ "default reports". The course 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 PLS path modeling, and especially if you use SmartPLS 2.0 path modeling software, you will likely find this course to be useful to your purposes.
Dr. Geoffrey Hubona held full-time tenure-track, and tenured, assistant and associate professor faculty positions at 3 major state universities in the Eastern United States from 1993-2010. In these positions, he taught dozens of various 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 (1993); an MA in Economics (1990), also from USF; an MBA in Finance (1979) from George Mason University in Fairfax, VA; and a BA in Psychology (1972) from the University of Virginia in Charlottesville, VA. He was a full-time assistant professor at the University of Maryland Baltimore County (1993-1996) in Catonsville, MD; a tenured associate professor in the department of Information Systems in the Business College at Virginia Commonwealth University (1996-2001) in Richmond, VA; and an associate professor in the CIS department of the Robinson College of Business at Georgia State University (2001-2010). He is the founder of the Georgia R School (2010-2014) and of R-Courseware (2014-Present), online educational organizations that teach research methods and quantitative analysis techniques. These research methods techniques include linear and non-linear modeling, multivariate methods, data mining, programming and simulation, and structural equation modeling and partial least squares (PLS) path modeling. Dr. Hubona is an expert of the analytical, open-source R software suite and of various PLS path modeling software packages, including SmartPLS. He has published dozens of research articles that explain and use these techniques for the analysis of data, and, with software co-development partner Dean Lim, has created a popular cloud-based PLS software application, PLS-GUI.