Welcome to our guide on creating your first regression model using SmartPLS! We suggest that you start with our step-by-step guide on how to create your first project and import data into SmartPLS. On these grounds continue with this tutorial, which will guide you through the following steps:

Creating a regression model
Estimating the model and evaluating the results

Throughout the tutorial, we will be using SmartPLS to perform a regression analysis and visualize the results. Make sure you have downloaded and installed the software before beginning. Let's get started!

Create your first regression model file

To create your first regression model, follow these steps:

Select your project in the Project list.
Click on REGRESSION in the Main toolbar. The following dialog will open.
In this dialog, enter a name for your model file (e.g., My first regression model) and then press the Save button.
The Model editor will then open, allowing you to begin working with your new model.

Draw the regression model

Drag and drop indicators to create constructs

To create construct in the model:

Select indicators in the Indicators list on the left
Drag and drop the indicator that will become the dependent variable in the regression model into Modeling canvas on the right
Then drag and drop the indicators that will become the independent variables in the response model onto the dependent variable in the Modeling canvas on the right

In this regression model example, drag and drop cusa to create the dependent variable. Then, drag and drop the independent variables qual_1 to qual_8 onto the dependent variable (i.e., cusa) in the Modeling canvas on the right.

You have various options to align your indicators on the Modeling canvas:

Drag elements around
Select elements and use the alignment actions in the toolbar on the right.
Right-click on the dependent variable to open a menu with additional actions, such as the ability to Align the indicators.
With ALT + SHIFT pressed, click on the dependent variable and then drag to the top, left, bottom, or right to align the indicators.
Double-click the dependent variable to open a dialog with even more settings.

Estimate the model and open the results report

After creating your model, it's time to estimate the results using the regression analysis.

Click the Calculate button in the Main toolbar.
A list of available algorithms in the software will appear, from which you should select the Regression analysis option.
The algorithm's dialog will open, allowing you to adjust certain parameters.
Just leave the default settings unchanged to start with.
Make sure the Open report checkbox is checked
Click Start calculation
The Results view will open automatically once the calculations are done.

Analyze the results

The Report navigation on the left side of the screen allows you to navigate the various regression results. The regression results provided by the SmartPLS 4 software offer a comprehensive evaluation of the model, as discussed, for example, in the textbooks by Hair et al. (2018) and Sarstedt and Mooi (2019).

You can save the report to your project for later use or export it to Excel, or HTML for sharing with others.

What’s next?

Congratulations on creating and estimating your first regression model in SmartPLS! To learn more about the process of creating and evaluating regression models, we recommend checking out the textbooks by Hair et al. (2018) and Sarstedt and Mooi (2019) and working through the step-by-step regression model example case study. These regression model example are available as ready-to-run sample regression projects in SmartPLS.

If you're interested in furthering your multivariate analysis knowledge on PLS-SEM, consider joining one of our upcoming courses on the foundations and advanced topics PLS-SEM using SmartPLS. Alternatively, the PLS-SEM Academy offers video-based trainings on PLS-SEM if these courses don't fit into your schedule.

We hope that you have success with your future projects and wish you the best of luck!

References

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

Sarstedt, M., and Mooi, E.A. (2019). A Concise Guide to Market Research: The Process, Data, and Methods Using IBM SPSS Statistics, 3rd Ed., Springer: Berlin, Heidelberg.