Your First CB-SEM Model

Throughout this tutorial, we'll use SmartPLS to perform CB-SEM analysis and visualize results. For this example, we'll use the model and dataset from Hair et al. (2018). Let's begin!

Before starting, please create a project and import the HBAT_SEM.sav data file.

Draw the Model

To create constructs in the model:

Select indicators of a specific construct from the Indicators list.
Drag and drop these indicators to the Modeling canvas.
Name the new construct and press ENTER.

To create relationships between constructs:

Select the Connect tool in the Main toolbar.
Click on the starting construct and move to the target construct.

Here are some options to align your indicators and constructs on the Modeling canvas:

Drag elements around.
Select elements and use alignment actions in the toolbar.
Right-click on a construct to open a menu with additional actions, such as Align the indicators.
Press ALT + SHIFT to align indicators.
Double-click a construct to open a dialog with more settings.

Estimate the Model and Open the Results Report

After creating your model:

Click the Calculate button in the Main toolbar.
Choose the Basic CB-SEM algorithm.
Click Start calculation with default settings and ensure the Open report checkbox is checked.

Analyze the Results

Use the Report navigation to explore CB-SEM results provided by SmartPLS 4, supporting a comprehensive evaluation of the model.

You can save the report to your project or export it to Excel or HTML for sharing.

Significance Testing of CB-SEM Results

For significance testing:

Return to the Model view.
Click Calculate and choose CB-SEM bootstrapping.
Adjust settings if needed and click Start calculation.

What’s Next?

Congratulations on creating your first CB-SEM model! To learn more, explore textbooks by Hair et al. (2018) and Kline (2023) and sample CB-SEM projects in SmartPLS.

Consider joining our courses on PLS-SEM or exploring the PLS-SEM Academy for video-based training.

References

Hair, J.F., Black, W.C., Babin, B.J., and Anderson, R.E. (2018). Multivariate Data Analysis, 8th Ed., Cengage: London.

Kline, R.B. (2023). Principles and Practice of Structural Equation Modeling (5 ed.). Guilford Press.