"Hands-On" PLS Path Modeling with SmartPLS 2.0

Learn how to use the many features and reports inherent in SmartPLS 2.0 from a strictly "hands-on" point of view.
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  • Lectures 98
  • Length 11 hours
  • Skill Level All Levels
  • Languages English
  • Includes Lifetime access
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About This Course

Published 8/2016 English

Course Description

‘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.

What are the requirements?

  • To get the most out of this course, students should install SmartPLS 2.0 software first.

What am I going to get from this course?

  • 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.

What is the target audience?

  • 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.

What you get with this course?

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30 day money back guarantee.

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Lifetime access.

Learn on the go.
Desktop, iOS and Android.

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Certificate of completion.

Curriculum

Section 1: Introduction to Course and Materials
Introduction to Course
Preview
01:59
What is this Course all About?
Preview
06:14
First Two Sessions' Topics
01:11
Section 2: Overview of SmartPLS Features
Look and Feel of SmartPLS (part 1)
Preview
04:28
Look and Feel of SmartPLS (part 2)
Preview
06:15
Features and Functionality (part 1)
06:46
Features and Functionality (part 2)
07:56
Features and Functionality (part 3)
06:29
Features and Functionality (part 4)
06:00
Section 3: Create a Project and Draw a Model
Create Project and Import Data (part 1)
Preview
06:48
Create Project and Import Data (part 2)
07:26
Validate Data (part 1)
Preview
06:08
Validate Data (part 2)
06:17
Begin to Draw Model (part 1)
08:27
Begin to Draw Model (part 2)
06:24
Draw the Specified Model
05:20
Model Window Features (part 1)
06:27
Model Window Features (part 2)
04:51
Saving Models and Exporting Projects (part 1)
08:39
Saving Models and Exporting Projects (part 2)
07:14
Missing Data (part 1)
Preview
04:23
Missing Data (part 2)
06:52
Missing Data (part 3)
06:05
Missing Data (part 4)
06:42
Multiple Projects and Models
04:10
Dealing with Data Errors (part 1)
05:47
Dealing with Data Errors (part 2)
07:37
Exercises
07:02
Section 4: Bootstrap Algorithm Results and Reports
Introduction to the Bootstrapping Algorithm
Preview
10:44
Run the Bootstrapping Algorithm
Preview
10:10
Bootstrapping Algorithm Results
07:23
Bootstrapping Default Report (part 1)
08:40
Bootstrapping Default Report (part 2)
10:23
Calculate t-statistics with Excel
11:35
Calculate p-value and Confidence Interval with Excel
11:47
Bootstrap Algorithm Exercises
07:11
Bootstrapping Algorithm Exercise Solution (part 1)
07:04
Bootstrapping Algorithm Exercise Solution (part 2)
07:12
Bootstrapping Algorithm Exercise Solution (part 3)
04:32
Bootstrapping Algorithm Exercise Solution (part 4)
08:27
Introduction to Blindfolding (part 1)
Preview
04:42
Introduction to Blindfolding (part 2)
Preview
05:55
Blindfolding Algorithm Report (part 1)
05:58
Blindfolding Algorithm Report (part 2)
04:55
Calculating q-Squared Effect Size (part 1)
06:59
Calculating q-Squared Effect Size (part 2)
06:26
Section 5: PLS Algorithm Results and Reports
Previous Exercises Solutions (part 1)
06:41
Previous Exercise Solutions (part 2)
05:43
Previous Exercise Solutions (part 3)
07:06
PLS Algorithm Parameters (part 1)
06:59
PLS Algorithm Parameters (part 2)
06:59
PLS Algorithm Default Report (part 1)
Preview
06:24
PLS Algorithm Default Report (part 2)
Preview
08:28
PLS Algorithm Default Report (part 3)
08:54
PLS Algorithm Default Report (part 4)
09:24
PLS Algorithm Results and Missing Data
10:31
PLS Algorithm HTML Reports
09:12
PLS Algorithm Initial Weights (part 1)
08:19
PLS Algorithm Initial Weights (part 2)
08:49
PLS Algorithm Exercises
06:13
PLS Algorithm Exercise Solution (part 1)
05:59
PLS Algorithm Exercise Solution (part 2)
07:38
PLS Algorithm Exercise Solution (part 3)
05:15
PLS Algorithm Exercise Solution (part 4)
07:32
Section 6: Mediation and Indirect Effects (Note: Similar Content in Conceptual PLS Course)
What is Mediation (review, part 1)
07:07
What is Mediation (review, part 2)
06:17
Measuring (or Assessing) Mediation (part 1)
06:59
Measuring (or Assessing) Mediation (part 2)
07:33
Extensions to Simple Mediation (part 1)
05:09
Extensions to Simple Mediation (part 2)
05:13
Complex Mediation Example (part 1)
Preview
07:11
Complex Mediation Example (part 2)
Preview
09:04
Mediation Example with PLS-GUI and SmartPLS (part 1)
06:31
Mediation Example and SmartPLS (part 2)
03:08
Mediation Example and PLS-GUI (part 3)
07:13
Mediated Moderation Exercise
00:45
Section 7: Moderation and Group Differences (Note: Some redundancies with Conceptuals)
What is Moderation (part 1)
07:28
Moderation in Path Models (part 2)
08:58
What is Moderation (part 3)
05:41
Product Indicator Moderation Example (part 1)
Preview
06:29
Product Indicator Moderation Example (part 2)
05:42
Two-Stage and Group Differences Approaches to Moderation
08:01
Create and Test Moderating Effect (part 1)
Preview
07:00
Create and Test Moderating Effect (part 2)
Preview
06:13
Create and Test Moderating Effect (part 3)
07:05
Multigroup Analysis (MGA) with PLS-GUI (part 1)
07:42
MGA and Segmentation with PLS-GUI (part 2)
06:05
MGA and Segmentation with PLS-GUI (part 3)
04:32
MGA and Segmentation with PLS-GUI (part 4)
06:38
Section 8: Second Order Models
What are "Second Order" latent constructs? (slides, part 1)
05:30
What are "Second Order" latent constructs? (slides, part 2)
04:02
A Second Order Example using SmartPLS
06:24

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Instructor Biography

Geoffrey Hubona, Ph.D., Professor of Information Systems

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.

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