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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 "handson" 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; plsgui.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?
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Section 1: Introduction to Course and Materials  

Lecture 1 
Introduction to Course

02:03  
Lecture 2 
A Word about the Course and the Materials

06:06  
Section 2: Conceptual Basis for using PLS Path Modeling  
Lecture 3 
What is PLS Path Modeling ? (part 1)

06:56  
Lecture 4 
What is PLS Path Modeling ? (part 2)

06:41  
Lecture 5 
Motivations for Using PLS Path Modeling (part 1)

05:07  
Lecture 6 
Motivations for Using PLS Path Modeling (part 2)

04:08  
Lecture 7 
Motivations for Using PLS Path Modeling (part 3)

05:35  
Lecture 8 
Motivations for Using PLS Path Modeling (part 4)

06:05  
Lecture 9 
Motivations for Using PLS Path Modeling (part 5)

06:49  
Lecture 10 
Motivations for Using PLS Path Modeling (part 6)

05:24  
Lecture 11 
More notes on PLS Path Modeling (part 1)

07:18  
Lecture 12 
More Notes on PLS Path Modeling (part 2)

07:57  
Lecture 13 
Resampling: Bootstrapping, Jacknifing, etc. (part 1)

08:03  
Lecture 14 
Resampling: Bootstrapping, Jacknifing, etc. (part 2)

08:37  
Lecture 15 
Formative versus Reflective Constructs (part 1)

07:30  
Lecture 16 
Formative versus Reflective Constructs (part 2)

07:49  
Lecture 17 
Formative versus Reflective Constructs (part 3)

07:16  
Lecture 18 
Formative versus Reflective Constructs (part 4)

06:58  
Lecture 19 
Formative versus Reflective Constructs (part 5)

06:57  
Lecture 20 
Formative versus Reflective Constructs (part 6)

05:33  
Lecture 21 
Finish PLS Relationships

04:40  
Section 3: Reliability and Validity Assessment  
Lecture 22 
Measurement Model Assessment (part 1)

07:14  
Lecture 23 
Measurement Model Assessment (part 2)

05:37  
Lecture 24 
Internal Consistency Reliability (part 1)

06:49  
Lecture 25 
Internal Consistency Reliability (part 2)

06:54  
Lecture 26 
Indicator Reliability (part 1)

06:37  
Lecture 27 
Indicator Reliability (part 2)

06:38  
Lecture 28 
Discriminant Validity

08:07  
Lecture 29 
Average Variance Extracted

08:37  
Lecture 30 
More on Discriminant Validity Measures

11:08  
Lecture 31 
Assessing Formative Indicators (part 1)

11:06  
Lecture 32 
Assessing Formative Indicators (part 2)

08:37  
Lecture 33 
Assessing Formative Indicators (part 3)

07:05  
Lecture 34 
Assessing Formative Indicators (part 4)

07:04  
Lecture 35 
Assessing Formative Indicators (part 5)

06:51  
Section 4: What is Bootstrapping ?  
Lecture 36 
Bootstrapping (part 1)

09:57  
Lecture 37 
Bootstrapping (part 2)

08:03  
Lecture 38 
Bootstrapping (part 3)

07:43  
Lecture 39 
Bootstrapping Examples (part 4)

11:59  
Lecture 40 
Bootstrapping Examples (part 5)

06:05  
Lecture 41 
Bootstrapping Examples (part 6)

05:07  
Lecture 42 
Bootstrapping Concepts (part 7)

08:23  
Lecture 43 
Bootstrapping Examples (part 8)

08:24  
Lecture 44 
Bootstrapping Features (part 9)

07:23  
Lecture 45 
Bootstrapping Sign Changes (part 10)

07:39  
Lecture 46 
Bootstrapping Sign Changes (part 11)

06:16  
Lecture 47 
Bootstrapping Sign Changes (part 12)

06:20  
Section 5: PLS Algorithm  
Lecture 48 
Introduction to PLS Algorithm

06:17  
Lecture 49 
PLS Algorithm Example Model

05:45  
Lecture 50 
PLS Algorithm

08:07  
Lecture 51 
PLS Algorithm Step 0: Initialization (part 1)

06:29  
Lecture 52 
PLS Algorithm Step 0: Initialization (part 2)

06:38  
Lecture 53 
PLS Algorithm Step 0: Initialization (part 3)

05:21  
Lecture 54 
PLS Algorithm Step 0: Initialization (part 4)

07:47  
Lecture 55 
PLS Algorithm Step 1: Inner Weights Estimation

07:42  
Lecture 56 
PLS Algorithm Step 2: Inside Approximation

09:44  
Lecture 57 
PLS Algorithm Step 3: Outer Weights Estimation

09:46  
Lecture 58 
PLS Algorithm Step 4: Outside Approximation

07:12  
Lecture 59 
PLS Algorithm: Stop Criterion Convergence

06:23  
Lecture 60 
PLS Algorithm: Final Parameters Estimation (part 1)

09:08  
Lecture 61 
PLS Algorithm: Final Parameters Estimation (part 2)

07:26  
Lecture 62 
PLS Algorithm Inner Weighting Schemes (part 1)

07:26  
Lecture 63 
PLS Algorithm Inner Weighting Schemes (part 2)

05:31  
Section 6: Blindfolding  
Lecture 64 
What is Blindfolding ? (part 1)

09:16  
Lecture 65 
What is Blindfolding ? (part 2)

06:22  
Lecture 66 
What is Blindfolding ? (part 3)

05:01  
Lecture 67 
Calculating QSquared Predictive Relevance (part 1)

05:15  
Lecture 68 
Calculating QSquared Predictive Relevance (part 2)

04:49  
Lecture 69 
Calculating QSquared Effect Size

07:59  
Section 7: Mediation  
Lecture 70 
Introduction to Mediation (part 1)

06:45  
Lecture 71 
Introduction to Mediation (part 2)

07:17  
Lecture 72 
Sobel and Testing for Mediation (part 1)

06:32  
Lecture 73 
Sobel, VAF, and Testing for Mediation (part 2)

06:29  
Lecture 74 
More Measures and Examples

06:01  
Lecture 75 
From Simple to More Complex Mediation

06:12  
Lecture 76 
More Complex Mediations

05:58  
Lecture 77 
Complex Mediation Example (part 1)

07:51  
Lecture 78 
Complex Mediation Example (part 2)

06:39  
Lecture 79 
Complex Mediation Example (part 3)

08:04  
Lecture 80 
Complex Mediation Example (part 4)

08:42  
Lecture 81 
Complex Mediation Example (part 5)

08:53  
Lecture 82 
Complex Mediation Example (part 6)

07:36  
Lecture 83 
Complex Mediation Example (part 7)

04:52  
Lecture 84 
Complex Mediation Example (part 8)

04:35  
Section 8: Moderation  
Lecture 85 
Moderation Concepts Introduction (part 1)

06:22  
Lecture 86 
Moderation Concepts Introduction (part 2)

07:07  
Lecture 87 
Product Indicator Example (part 1)

04:35  
Lecture 88 
Product Indicator (part 2)

04:43  
Lecture 89 
TwoStage Approach

07:11  
Lecture 90 
Group Differences Approach

10:12 
Dr. Geoffrey Hubona held fulltime tenuretrack, and tenured, assistant and associate professor faculty positions at 3 major state universities in the Eastern United States from 19932010. 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 fulltime assistant professor at the University of Maryland Baltimore County (19931996) in Catonsville, MD; a tenured associate professor in the department of Information Systems in the Business College at Virginia Commonwealth University (19962001) in Richmond, VA; and an associate professor in the CIS department of the Robinson College of Business at Georgia State University (20012010). He is the founder of the Georgia R School (20102014) and of RCourseware (2014Present), online educational organizations that teach research methods and quantitative analysis techniques. These research methods techniques include linear and nonlinear 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, opensource 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 codevelopment partner Dean Lim, has created a popular cloudbased PLS software application, PLSGUI.