Conceptual Foundations of PLS Path Modeling

Conceptual Foundations of PLS Path Modeling, available at Free, has an average rating of 4.8, with 90 lectures, based on 384 reviews, and has 11211 subscribers.

You will learn about 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. This course is ideal for individuals who are Graduate students, faculty, or practicing professionals who use PLS path modeling should take this course. or Anyone who wishes to learn more about PLS path modeling will benefit from taking this course. It is particularly useful for Graduate students, faculty, or practicing professionals who use PLS path modeling should take this course. or Anyone who wishes to learn more about PLS path modeling will benefit from taking this course.

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Summary

Title: Conceptual Foundations of PLS Path Modeling

Price: Free

Average Rating: 4.8

Number of Lectures: 90

Number of Published Lectures: 90

Number of Curriculum Items: 90

Number of Published Curriculum Objects: 90

Original Price: Free

Quality Status: approved

Status: Live

What You Will Learn

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.

Who Should Attend

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.

Target Audiences

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.

Conceptual Foundations of PLS Path Modelingprovides 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?

Course Curriculum

Chapter 1: Introduction to Course and Materials

Lecture 1: Introduction to Course

Lecture 2: A Word about the Course and the Materials

Chapter 2: Conceptual Basis for using PLS Path Modeling

Lecture 1: What is PLS Path Modeling ? (part 1)

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

Lecture 3: Motivations for Using PLS Path Modeling (part 1)

Lecture 4: Motivations for Using PLS Path Modeling (part 2)

Lecture 5: Motivations for Using PLS Path Modeling (part 3)

Lecture 6: Motivations for Using PLS Path Modeling (part 4)

Lecture 7: Motivations for Using PLS Path Modeling (part 5)

Lecture 8: Motivations for Using PLS Path Modeling (part 6)

Lecture 9: More notes on PLS Path Modeling (part 1)

Lecture 10: More Notes on PLS Path Modeling (part 2)

Lecture 11: Resampling: Bootstrapping, Jacknifing, etc. (part 1)

Lecture 12: Resampling: Bootstrapping, Jacknifing, etc. (part 2)

Lecture 13: Formative versus Reflective Constructs (part 1)

Lecture 14: Formative versus Reflective Constructs (part 2)

Lecture 15: Formative versus Reflective Constructs (part 3)

Lecture 16: Formative versus Reflective Constructs (part 4)

Lecture 17: Formative versus Reflective Constructs (part 5)

Lecture 18: Formative versus Reflective Constructs (part 6)

Lecture 19: Finish PLS Relationships

Chapter 3: Reliability and Validity Assessment

Lecture 1: Measurement Model Assessment (part 1)

Lecture 2: Measurement Model Assessment (part 2)

Lecture 3: Internal Consistency Reliability (part 1)

Lecture 4: Internal Consistency Reliability (part 2)

Lecture 5: Indicator Reliability (part 1)

Lecture 6: Indicator Reliability (part 2)

Lecture 7: Discriminant Validity

Lecture 8: Average Variance Extracted

Lecture 9: More on Discriminant Validity Measures

Lecture 10: Assessing Formative Indicators (part 1)

Lecture 11: Assessing Formative Indicators (part 2)

Lecture 12: Assessing Formative Indicators (part 3)

Lecture 13: Assessing Formative Indicators (part 4)

Lecture 14: Assessing Formative Indicators (part 5)

Chapter 4: What is Bootstrapping ?

Lecture 1: Bootstrapping (part 1)

Lecture 2: Bootstrapping (part 2)

Lecture 3: Bootstrapping (part 3)

Lecture 4: Bootstrapping Examples (part 4)

Lecture 5: Bootstrapping Examples (part 5)

Lecture 6: Bootstrapping Examples (part 6)

Lecture 7: Bootstrapping Concepts (part 7)

Lecture 8: Bootstrapping Examples (part 8)

Lecture 9: Bootstrapping Features (part 9)

Lecture 10: Bootstrapping Sign Changes (part 10)

Lecture 11: Bootstrapping Sign Changes (part 11)

Lecture 12: Bootstrapping Sign Changes (part 12)

Chapter 5: PLS Algorithm

Lecture 1: Introduction to PLS Algorithm

Lecture 2: PLS Algorithm Example Model

Lecture 3: PLS Algorithm

Lecture 4: PLS Algorithm Step 0: Initialization (part 1)

Lecture 5: PLS Algorithm Step 0: Initialization (part 2)

Lecture 6: PLS Algorithm Step 0: Initialization (part 3)

Lecture 7: PLS Algorithm Step 0: Initialization (part 4)

Lecture 8: PLS Algorithm Step 1: Inner Weights Estimation

Lecture 9: PLS Algorithm Step 2: Inside Approximation

Lecture 10: PLS Algorithm Step 3: Outer Weights Estimation

Lecture 11: PLS Algorithm Step 4: Outside Approximation

Lecture 12: PLS Algorithm: Stop Criterion Convergence

Lecture 13: PLS Algorithm: Final Parameters Estimation (part 1)

Lecture 14: PLS Algorithm: Final Parameters Estimation (part 2)

Lecture 15: PLS Algorithm Inner Weighting Schemes (part 1)

Lecture 16: PLS Algorithm Inner Weighting Schemes (part 2)

Chapter 6: Blindfolding

Lecture 1: What is Blindfolding ? (part 1)

Lecture 2: What is Blindfolding ? (part 2)

Lecture 3: What is Blindfolding ? (part 3)

Lecture 4: Calculating Q-Squared Predictive Relevance (part 1)

Lecture 5: Calculating Q-Squared Predictive Relevance (part 2)

Lecture 6: Calculating Q-Squared Effect Size

Chapter 7: Mediation

Lecture 1: Introduction to Mediation (part 1)

Lecture 2: Introduction to Mediation (part 2)

Lecture 3: Sobel and Testing for Mediation (part 1)

Lecture 4: Sobel, VAF, and Testing for Mediation (part 2)

Lecture 5: More Measures and Examples

Lecture 6: From Simple to More Complex Mediation

Lecture 7: More Complex Mediations

Lecture 8: Complex Mediation Example (part 1)

Lecture 9: Complex Mediation Example (part 2)

Lecture 10: Complex Mediation Example (part 3)

Lecture 11: Complex Mediation Example (part 4)

Lecture 12: Complex Mediation Example (part 5)

Lecture 13: Complex Mediation Example (part 6)

Lecture 14: Complex Mediation Example (part 7)

Lecture 15: Complex Mediation Example (part 8)

Chapter 8: Moderation

Lecture 1: Moderation Concepts Introduction (part 1)

Lecture 2: Moderation Concepts Introduction (part 2)

Lecture 3: Product Indicator Example (part 1)

Lecture 4: Product Indicator (part 2)

Lecture 5: Two-Stage Approach

Lecture 6: Group Differences Approach

Instructors

Geoffrey Hubona, Ph.D.
Associate Professor of MIS and Data Analytics

Rating Distribution

1 stars: 3 votes
2 stars: 9 votes
3 stars: 41 votes
4 stars: 154 votes
5 stars: 177 votes

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