Course Syllabus
Course Title
Regression in SmartPLS 4.0: Practical Analysis, Results Assessment, and Reporting
Course Description
This course teaches practical regression-based analysis in SmartPLS 4. Students learn how to prepare data, build graphical regression models, run single and multiple linear regression, use bootstrapping, assess results, handle common reporting issues, and write professional results sections. The course also includes a PLS-SEM pathway for learners who use latent constructs and need measurement and structural model assessment.
The course includes a paper-based case-study track using the 2026 Journal of Marketing Analytics SmartPLS tutorial on multiple linear and logistic regression. This track is designed for students who want to understand and reproduce the published workflow in a guided classroom format.
Target Learners
- Thesis and dissertation students
- Business, management, transportation, education, and social science researchers
- Students using SmartPLS 4 for quantitative research
- Researchers preparing journal manuscripts using regression or PLS-SEM
Learning Outcomes
After completing the course, learners should be able to:
- Import data into SmartPLS 4 and verify variable coding.
- Draw regression and PLS path models.
- Run linear regression and regression bootstrapping.
- Choose between normal, HC3, and HC4 standard errors.
- Assess coefficient size, direction, and statistical significance.
- Interpret R-square, adjusted R-square, residual diagnostics, and QQ plots.
- Assess reflective measurement models using reliability, AVE, and discriminant validity.
- Assess structural models using VIF, path coefficients, R-square, f-square, and bootstrapping.
- Prepare tables and written reporting for thesis and journal papers.
Assessment Plan
| Component | Weight |
|---|---|
| Assignment 1: Data preparation | 15% |
| Assignment 2: Linear regression model | 20% |
| Assignment 3: Regression assessment and bootstrapping | 25% |
| Assignment 4: PLS-SEM assessment | 20% |
| Capstone SmartPLS report | 20% |
Weekly Schedule
| Week | Topic | Practical Task |
|---|---|---|
| 1 | SmartPLS regression workflow | Install/open SmartPLS and inspect interface |
| 2 | Data preparation | Import CSV and verify variables |
| 3 | Model building | Draw regression model |
| 4 | Linear regression | Run single and multiple regression |
| 5 | Regression assessment | Assess coefficients, R-square, diagnostics |
| 6 | Bootstrapping | Run 10,000 bootstrap subsamples for final reporting |
| 7 | Reporting | Prepare regression table and interpretation |
| 8 | PLS-SEM measurement model | Assess loadings, reliability, AVE, HTMT |
| 9 | PLS-SEM structural model | Assess VIF, paths, R-square, f-square |
| 10 | Prediction | Run or interpret PLSpredict outputs |
| 11 | Reviewer problems | Fix common reporting and interpretation issues |
| 12 | Capstone | Submit full SmartPLS result report |
Paper-Based Practical Track
In addition to the weekly modules, students complete the paper-based HBAT-style case:
| Session | Activity | Output |
|---|---|---|
| P1 | Read paper explainer and six-stage workflow | One-page summary |
| P2 | Import HBAT-style dataset | Dataset screenshot and codebook |
| P3 | Run multiple linear regression | Linear regression assessment table |
| P4 | Check assumptions and diagnostics | Assumption checklist |
| P5 | Run logistic regression | Logistic fit and coefficient table |
| P6 | Write final results | Complete paper-style results section |
Required Software
- SmartPLS 4
- Spreadsheet software for viewing CSV files
- Optional: Python for external regression benchmark
Core Rule
Do not report SmartPLS output mechanically. Every result must be connected to the research question, model specification, statistical criterion, and practical interpretation.