Module 1: SmartPLS Regression Workflow
Purpose
SmartPLS 4 supports several analysis routes. Before clicking calculate, researchers must decide whether their project needs observed-variable regression, logistic regression, path analysis, or PLS-SEM.
Learning Objectives
After this module, learners should be able to:
- Explain the difference between linear regression and PLS-SEM in SmartPLS.
- Identify the dependent variable, independent variables, and controls.
- Decide whether the analysis goal is prediction, explanation, or theory testing.
- Select the correct SmartPLS workflow for the research question.
Regression in SmartPLS
SmartPLS regression is used when the dependent variable is measured directly and is continuous. Examples:
- Satisfaction score predicted by usefulness, trust, and service quality.
- Commute time predicted by distance, income, age, and travel mode.
- Exam score predicted by study hours, attendance, and sleep.
SmartPLS can run single and multiple linear regression models and report coefficients in standardized and unstandardized form.
PLS-SEM Structural Paths
PLS-SEM is used when variables are modeled as latent constructs measured by indicators. Examples:
- Perceived usefulness measured by several survey items.
- Trust measured by three or more indicators.
- Satisfaction measured as a construct with reflective items.
In PLS-SEM, structural paths are regression-like relationships among latent constructs, but results must be assessed in two stages:
- Measurement model assessment
- Structural model assessment
Decision Guide
| Research Situation | SmartPLS Route |
|---|---|
| One continuous observed dependent variable | Linear regression |
| Binary dependent variable coded 0/1 | Logistic regression |
| Latent constructs measured by indicators | PLS-SEM |
| Mediation or moderation among observed variables | Path analysis or PROCESS-style model |
| Necessary but not sufficient conditions | Necessary condition analysis |
Practical Workflow
- Define the research question.
- Identify the dependent variable.
- Identify predictors and controls.
- Prepare and import the data.
- Draw the model in SmartPLS.
- Run the algorithm.
- Assess model results.
- Run bootstrapping for final inference.
- Export tables and figures.
- Write results with criteria and interpretation.
Practice
Write a one-paragraph plan:
- Research question
- Dependent variable
- Independent variables
- Control variables
- Analysis route in SmartPLS
- Why this route is appropriate
Next Module
Continue to Module 2: Data Preparation.