Sometimes, a single latent variable isn’t enough to fully represent a complex concept. For example, if you’re studying something like customer loyalty, it might involve several distinct but related aspects—such as repeat purchase behavior, emotional attachment, and brand advocacy. Instead of analyzing each one separately, a second-order construct lets you bring them together under one broader factor. This approach not only simplifies the model but also helps stay true to the underlying theory by reflecting how these dimensions work together in real life.

To build a second-order factor model in SmartPLS, the most common and recommended method is the Repeated Indicator Approach. In this approach, you first define your first-order constructs as usual, each with its own indicators. Then, you create a second-order construct and assign all the indicators of the first-order constructs to it. That means the same indicators will appear twice in the model: once linked to their original first-order construct, and again linked to the higher-order construct.

After setting up the paths from the second-order construct to the first-order ones (usually reflective), you can run the algorithm and proceed with model evaluation. This method is widely used because it’s easy to implement in SmartPLS and supports hierarchical relationships well. Make sure to remove any dropped or deleted indicators consistently from both levels, and always check for multicollinearity or validity issues during analysis.

To keep your model diagram clean, you can right-click on the main construct’s circle and select “Hide Indicators of Selected Construct.” This hides the duplicated indicators from the visual layout, but they will still be included in the analysis.