Conceptual Foundations of PLS Path Modeling

Learn the concepts of the PLS algorithm, reliability and validity, bootstrapping, mediation and moderation.
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  • Lectures 90
  • Length 10.5 hours
  • Skill Level All Levels
  • Languages English
  • Includes Lifetime access
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About This Course

Published 3/2016 English

Course Description

Conceptual Foundations of PLS Path Modeling provides a comprehensive introduction to the most critical foundational concepts of PLS path modeling. Virtually the entire course consists of narrative lectures accompanied by powerpoint slides and some readings. The course does not teach how to use any particular specific PLS software modeling package. The course is very useful as a preliminary course to any other "hands-on" course that teaches how to use specific PLS path modeling (or related) software (such as SmartPLS 2.0 or 3.0; WarpPLS; the semPLS or plspm packages in R; ADANCO; pls-gui.com; and so on). Participants learn the conceptual basics of the following critical path modeling terms and processes: What is PLS path modeling?, formative versus reflective constructs, assessing reliability and validity, bootstrapping and blindfolding, how to estimate direct, indirect, total, mediating and moderating effects.

This course is intended for graduate students, faculty and other researchers who seek explicit and comprehensive explanations and of the foundational concepts that underlie PLS path modeling. It addresses basic issues such as: How does the PLS algorithm 'work'? What are the differences between the outer measurement and inner structural models in a path model with latent variables? What are the fundamental distinctions between formative and reflective constructs? What can one determine about direct, indirect, and total effects? About mediating and moderating effects? What do path coefficients, weights and loadings tell you about the underlying data relationships? What are latent variable ‘scores’ or values? What do the predictive levels of variance explained in the endogenous latent variables actually mean?

What are the requirements?

  • All necessary materials including many authoritative readings are included with the course.
  • This course DOES NOT TEACH ANY PLS SOFTWARE but does teach HOW TO EFFECTIVELY USE AND INTERPRET THE OUTPUT OF PLS SOFTWARE.

What am I going to get from this course?

  • Understand the critical conceptual foundations of PLS path modeling.
  • Understand exactly how the PLS path modeling algorithm calculates or "works."
  • Understand how the bootstrapping and jackknifing resampling procedures "work" to determine significance levels.
  • Know how to estimate, and the meaning of: direct, indirect, total, mediating and moderating effects.
  • Understand the distinctions between formative and reflective constructs.
  • Know how to assess the reliability and validity of an estimated PLS path model.
  • Know the differences between the outer measurement and the inner structural models.

What is the target audience?

  • Graduate students, faculty, or practicing professionals who use PLS path modeling should take this course.
  • Anyone who wishes to learn more about PLS path modeling will benefit from taking this course.

What you get with this course?

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Curriculum

Section 1: Introduction to Course and Materials
Introduction to Course
02:03
A Word about the Course and the Materials
06:06
Section 2: Conceptual Basis for using PLS Path Modeling
What is PLS Path Modeling ? (part 1)
06:56
What is PLS Path Modeling ? (part 2)
06:41
Motivations for Using PLS Path Modeling (part 1)
05:07
Motivations for Using PLS Path Modeling (part 2)
04:08
Motivations for Using PLS Path Modeling (part 3)
05:35
Motivations for Using PLS Path Modeling (part 4)
06:05
Motivations for Using PLS Path Modeling (part 5)
06:49
Motivations for Using PLS Path Modeling (part 6)
05:24
More notes on PLS Path Modeling (part 1)
07:18
More Notes on PLS Path Modeling (part 2)
07:57
Resampling: Bootstrapping, Jacknifing, etc. (part 1)
08:03
Resampling: Bootstrapping, Jacknifing, etc. (part 2)
08:37
Formative versus Reflective Constructs (part 1)
07:30
Formative versus Reflective Constructs (part 2)
07:49
Formative versus Reflective Constructs (part 3)
07:16
Formative versus Reflective Constructs (part 4)
06:58
Formative versus Reflective Constructs (part 5)
06:57
Formative versus Reflective Constructs (part 6)
05:33
Finish PLS Relationships
04:40
Section 3: Reliability and Validity Assessment
Measurement Model Assessment (part 1)
07:14
Measurement Model Assessment (part 2)
05:37
Internal Consistency Reliability (part 1)
06:49
Internal Consistency Reliability (part 2)
06:54
Indicator Reliability (part 1)
06:37
Indicator Reliability (part 2)
06:38
Discriminant Validity
08:07
Average Variance Extracted
08:37
More on Discriminant Validity Measures
11:08
Assessing Formative Indicators (part 1)
11:06
Assessing Formative Indicators (part 2)
08:37
Assessing Formative Indicators (part 3)
07:05
Assessing Formative Indicators (part 4)
07:04
Assessing Formative Indicators (part 5)
06:51
Section 4: What is Bootstrapping ?
Bootstrapping (part 1)
09:57
Bootstrapping (part 2)
08:03
Bootstrapping (part 3)
07:43
Bootstrapping Examples (part 4)
11:59
Bootstrapping Examples (part 5)
06:05
Bootstrapping Examples (part 6)
05:07
Bootstrapping Concepts (part 7)
08:23
Bootstrapping Examples (part 8)
08:24
Bootstrapping Features (part 9)
07:23
Bootstrapping Sign Changes (part 10)
07:39
Bootstrapping Sign Changes (part 11)
06:16
Bootstrapping Sign Changes (part 12)
06:20
Section 5: PLS Algorithm
Introduction to PLS Algorithm
06:17
PLS Algorithm Example Model
05:45
PLS Algorithm
08:07
PLS Algorithm Step 0: Initialization (part 1)
06:29
PLS Algorithm Step 0: Initialization (part 2)
06:38
PLS Algorithm Step 0: Initialization (part 3)
05:21
PLS Algorithm Step 0: Initialization (part 4)
07:47
PLS Algorithm Step 1: Inner Weights Estimation
07:42
PLS Algorithm Step 2: Inside Approximation
09:44
PLS Algorithm Step 3: Outer Weights Estimation
09:46
PLS Algorithm Step 4: Outside Approximation
07:12
PLS Algorithm: Stop Criterion Convergence
06:23
PLS Algorithm: Final Parameters Estimation (part 1)
09:08
PLS Algorithm: Final Parameters Estimation (part 2)
07:26
PLS Algorithm Inner Weighting Schemes (part 1)
07:26
PLS Algorithm Inner Weighting Schemes (part 2)
05:31
Section 6: Blindfolding
What is Blindfolding ? (part 1)
09:16
What is Blindfolding ? (part 2)
06:22
What is Blindfolding ? (part 3)
05:01
Calculating Q-Squared Predictive Relevance (part 1)
05:15
Calculating Q-Squared Predictive Relevance (part 2)
04:49
Calculating Q-Squared Effect Size
07:59
Section 7: Mediation
Introduction to Mediation (part 1)
06:45
Introduction to Mediation (part 2)
07:17
Sobel and Testing for Mediation (part 1)
06:32
Sobel, VAF, and Testing for Mediation (part 2)
06:29
More Measures and Examples
06:01
From Simple to More Complex Mediation
06:12
More Complex Mediations
05:58
Complex Mediation Example (part 1)
07:51
Complex Mediation Example (part 2)
06:39
Complex Mediation Example (part 3)
08:04
Complex Mediation Example (part 4)
08:42
Complex Mediation Example (part 5)
08:53
Complex Mediation Example (part 6)
07:36
Complex Mediation Example (part 7)
04:52
Complex Mediation Example (part 8)
04:35
Section 8: Moderation
Moderation Concepts Introduction (part 1)
06:22
Moderation Concepts Introduction (part 2)
07:07
Product Indicator Example (part 1)
04:35
Product Indicator (part 2)
04:43
Two-Stage Approach
07:11
Group Differences Approach
10:12

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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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