PLS Path Modeling with the semPLS and PLSPM Packages in R
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PLS Path Modeling with the semPLS and PLSPM Packages in R

How to make use of the unique semPLS and PLSPM packages features and capabilities to estimate path models.
3.8 (12 ratings)
Instead of using a simple lifetime average, Udemy calculates a course's star rating by considering a number of different factors such as the number of ratings, the age of ratings, and the likelihood of fraudulent ratings.
514 students enrolled
Last updated 8/2016
English
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Includes:
  • 9 hours on-demand video
  • 1 Supplemental Resource
  • Full lifetime access
  • Access on mobile and TV
  • Certificate of Completion
Description

The course PLS Path Modeling with the semPLS and PLSPM packages in R demonstrates the major capabilities and functions of the R semPLS package; and the major capabilities and functions of the R PLSPM package. Although the semPLS and plspm R packages use the same PLS algorithm as does SmartPLS, and consequently produce identical PLS model estimates (in almost all cases with a few exceptions), each of the two R packages also contains additional, useful, complementary functions and capabilities. Specifically, semPLS has some interesting plots and graphs of PLS path model estimates and also converts your model to run in covariance-based R functions (which is quite handy!). On the other hand, the PLSPM package has very complete and well-formatted PLS output that is consistent with the tables and reports required for publication, and also has very useful and unique multigroup-moderation analysis capabilities, and a unique REBUS-PLS function for discovering heterogeneity (more multi-group differences). If you are interested in knowing a lot about PLS path modeling, it is certainly a good use of your time to become familiar with both the semPLS and PLSPM packages in R.

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Curriculum For This Course
97 Lectures
08:48:25
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Importing "Other" PLS and SEM files into R
12 Lectures 01:06:57




Run sempls() function (part 1)
05:16

Run sempls() function (part 2)
04:59

Run CB-SEM sem() function with PLS Model
07:29



Run the plspm() function (part 1)
04:08

Run the plspm() function (part 2)
05:13

Summary of the R-extending Capabilities
07:38
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semPLS Package Functions and Capabilities
12 Lectures 01:18:10
Import SmartPLS model file (part 1)
05:21

Import SmartPLS model file (part 2)
06:40



Run sempls() function (part 3)
08:14

Select sempls() functions results (part 1)
05:58

Select sempls() function results (part 2)
06:59

Bootstrap a PLS Model (part 1)
07:47

Bootstrap a PLS Model (part 2)
07:56

More unique semPLS Package output (part 1)
06:02

More unique semPLS output (part 2)
06:43

More unique semPLS output (part 3)
05:59
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"Hands-On" with the PLSPM Package and R
17 Lectures 01:21:59
First Exercise to Think About (part1)
04:17

First Exercise to Think About (part 2)
04:27

First Exercise to Think About (part 3)
04:24

Spain Football PLS Model (part 1)
05:28

Spain Football and Other PLS Models
04:03

The Index of Success with Football Model (part 1)
06:33

The Index of Success with Football Model (part 2)
04:19



A Closer Look at the plspm function (part 3)
04:54

A Closer Look at the plspm function (part 4)
04:11

Setting Up Spain Football Model Arguments (part 1)
04:16

Setting Up Spain Football Model Arguments (part 2)
04:34

Run Spain Football Model and Examine Results (part 1)
05:42

Run Spain Football Model and Examine Results (part 2)
05:22

Run Spain Football Model and Examine Results (part 3)
05:49

Epilogue: 'Flipped' Loadings and/or Path Coefficients
03:59
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Evaluating PLS Models using PLSPM Package
8 Lectures 42:25
Evaluating unidimensionality (part 1)
05:32

Evaluating unidimensionality (part 2)
06:42

Evaluating unidimensionality (part 3)
06:26

Loadings Cross-Loadings Communalities (part 1)
05:00

Loadings Cross-Loadings Communalities (part 2)
04:39



Bootstrap Validation
04:57
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Multi-Group Comparisons
19 Lectures 01:36:58
Introduction to Multi-Group Comparisons section
03:22



Group Differences (slides, part 3)
04:34

Setting up Groups (part 1)
05:29

Setting up Groups (part 2)
05:45

Setting up Groups (part 3)
05:15

Groups (part 4)
03:31

Groups: Permutation Approach (part 5)
06:06

Example: College GPA (part 1)
06:18

Example: College GPA (part 2)
05:58

Example: College GPA (part 3)
05:26

Example: College GPA (part 4)
04:52

Example: College GPA (part 5)
05:40

Example: Second Life Study (part 1)
05:58

Example: Second Life Study (part 2)
05:18

Example: Second Life Study (part 3)
04:58


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Moderation Effects in PLS Path Models
9 Lectures 42:09
What is Moderation ? (slides, part 1)
05:52

What is Moderation ? (slides, part 2)
05:18

What is Moderation ? (slides, part 3)
04:07

What is Moderation ? (slides, part 4)
04:09

What is Moderation (slides, part 5)
03:59



Second Life Example in Script (part 3)
04:23

Second Life Homework Exercise
04:25
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Response-Based Unit Segmentation (REBUS)
20 Lectures 01:59:47
REBUS Explained (part 1)
04:43

REBUS Explained (part 2)
05:28

REBUS Explained (part 3)
05:09

Football Example Revisited (part 1)
05:10

Global Football Model (part 2)
06:20

Football Model (part 3)
06:35

Football Cluster Analysis
08:07



Football REBUS (part 3)
05:43

Football REBUS (part 4)
05:43

Football REBUS (part 5)
07:34

More on Moderation
06:11

Product Indicator Approach (part 1)
06:31

Product Indicator Approach (part 2)
03:53

Two-Stage Approach (part 1)
06:33

Two-Stage Approach (part 2)
06:24

Two-Stage Regression Approach
06:42

Categorical Approach (part 1)
05:25

Categorical Approach (part 2)
05:43
About the Instructor
Geoffrey Hubona, Ph.D.
4.0 Average rating
1,476 Reviews
12,600 Students
28 Courses
Associate 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. Currently, he is a visiting associate professor of MIS at Texas A&M International University. 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; 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.