Conceptual Foundations of PLS Path Modeling

Learn the concepts of the PLS algorithm, reliability and validity, bootstrapping, mediation and moderation.
Rating: 4.4 out of 5 (251 ratings)
7,843 students
Conceptual Foundations of PLS Path Modeling
Rating: 4.4 out of 5 (251 ratings)
7,843 students
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.

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.
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?

Who this course is for:
  • 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.
Curriculum
8 sections • 90 lectures • 10h 33m total length
  • Introduction to Course
  • A Word about the Course and the Materials
  • What is PLS Path Modeling ? (part 1)
  • What is PLS Path Modeling ? (part 2)
  • Motivations for Using PLS Path Modeling (part 1)
  • Motivations for Using PLS Path Modeling (part 2)
  • Motivations for Using PLS Path Modeling (part 3)
  • Motivations for Using PLS Path Modeling (part 4)
  • Motivations for Using PLS Path Modeling (part 5)
  • Motivations for Using PLS Path Modeling (part 6)
  • More notes on PLS Path Modeling (part 1)
  • More Notes on PLS Path Modeling (part 2)
  • Resampling: Bootstrapping, Jacknifing, etc. (part 1)
  • Resampling: Bootstrapping, Jacknifing, etc. (part 2)
  • Formative versus Reflective Constructs (part 1)
  • Formative versus Reflective Constructs (part 2)
  • Formative versus Reflective Constructs (part 3)
  • Formative versus Reflective Constructs (part 4)
  • Formative versus Reflective Constructs (part 5)
  • Formative versus Reflective Constructs (part 6)
  • Finish PLS Relationships
  • Measurement Model Assessment (part 1)
  • Measurement Model Assessment (part 2)
  • Internal Consistency Reliability (part 1)
  • Internal Consistency Reliability (part 2)
  • Indicator Reliability (part 1)
  • Indicator Reliability (part 2)
  • Discriminant Validity
  • Average Variance Extracted
  • More on Discriminant Validity Measures
  • Assessing Formative Indicators (part 1)
  • Assessing Formative Indicators (part 2)
  • Assessing Formative Indicators (part 3)
  • Assessing Formative Indicators (part 4)
  • Assessing Formative Indicators (part 5)
  • Bootstrapping (part 1)
  • Bootstrapping (part 2)
  • Bootstrapping (part 3)
  • Bootstrapping Examples (part 4)
  • Bootstrapping Examples (part 5)
  • Bootstrapping Examples (part 6)
  • Bootstrapping Concepts (part 7)
  • Bootstrapping Examples (part 8)
  • Bootstrapping Features (part 9)
  • Bootstrapping Sign Changes (part 10)
  • Bootstrapping Sign Changes (part 11)
  • Bootstrapping Sign Changes (part 12)
  • Introduction to PLS Algorithm
  • PLS Algorithm Example Model
  • PLS Algorithm
  • PLS Algorithm Step 0: Initialization (part 1)
  • PLS Algorithm Step 0: Initialization (part 2)
  • PLS Algorithm Step 0: Initialization (part 3)
  • PLS Algorithm Step 0: Initialization (part 4)
  • PLS Algorithm Step 1: Inner Weights Estimation
  • PLS Algorithm Step 2: Inside Approximation
  • PLS Algorithm Step 3: Outer Weights Estimation
  • PLS Algorithm Step 4: Outside Approximation
  • PLS Algorithm: Stop Criterion Convergence
  • PLS Algorithm: Final Parameters Estimation (part 1)
  • PLS Algorithm: Final Parameters Estimation (part 2)
  • PLS Algorithm Inner Weighting Schemes (part 1)
  • PLS Algorithm Inner Weighting Schemes (part 2)
  • What is Blindfolding ? (part 1)
  • What is Blindfolding ? (part 2)
  • What is Blindfolding ? (part 3)
  • Calculating Q-Squared Predictive Relevance (part 1)
  • Calculating Q-Squared Predictive Relevance (part 2)
  • Calculating Q-Squared Effect Size
  • Introduction to Mediation (part 1)
  • Introduction to Mediation (part 2)
  • Sobel and Testing for Mediation (part 1)
  • Sobel, VAF, and Testing for Mediation (part 2)
  • More Measures and Examples
  • From Simple to More Complex Mediation
  • More Complex Mediations
  • Complex Mediation Example (part 1)
  • Complex Mediation Example (part 2)
  • Complex Mediation Example (part 3)
  • Complex Mediation Example (part 4)
  • Complex Mediation Example (part 5)
  • Complex Mediation Example (part 6)
  • Complex Mediation Example (part 7)
  • Complex Mediation Example (part 8)
  • Moderation Concepts Introduction (part 1)
  • Moderation Concepts Introduction (part 2)
  • Product Indicator Example (part 1)
  • Product Indicator (part 2)
  • Two-Stage Approach
  • Group Differences Approach

Instructor
Associate Professor of Information Systems
Geoffrey Hubona, Ph.D.
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  • 25,097 Students
  • 28 Courses

Dr. Geoffrey Hubona has held full-time tenure-track, and tenured, assistant and associate professor faculty positions at 4 major state universities in the United States since 1993. Currently, he is an associate professor of MIS at Texas A&M International 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 faculty 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; an MA in Economics, also from USF; an MBA in Finance from 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 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.