Principal component analysis (PCA) is a statistical technique for reducing the dimensionality of a dataset. It transforms a large set of observed variables into a smaller set of uncorrelated principal components that capture as much of the original variance as possible, which simplifies the complexity of the dataset while preserving its essential information. Researchers typically use PCA as an exploratory data analysis step, for example to understand the underlying structure of a set of indicators before or alongside estimating a PLS-SEM model. SmartPLS supports PCA as part of its exploratory data analysis capabilities for both PLS-SEM and CB-SEM projects.

SmartPLS allows researchers to build graphical PCA models directly within a project and estimates them using an eigenvalue decomposition approach (Hair et al., 2018). This approach identifies the principal components that capture the most variance in the data, helping researchers reduce a large set of observed variables to a smaller set of uncorrelated components without losing essential information. This makes SmartPLS a clear alternative to IBM SPSS Amos for exploratory PCA work.

SmartPLS does not offer alternative result types for PCA; it always reports standardized outcomes based on the indicators' correlation matrix. In this standardized case, an eigenvalue of 1 is the decision criterion for determining how many components to retain.

PCA is used for exploratory data analysis. It reduces a large set of observed variables to a smaller set of uncorrelated principal components that capture the most variance in the data, which helps researchers simplify complex datasets while preserving essential information before or alongside PLS-SEM estimation.

SmartPLS supports the creation of graphical PCA models and estimates them using eigenvalue decomposition, which identifies the principal components that capture the most variance in the data (Hair et al., 2018).

No. SmartPLS only offers standardized PCA outcomes, based on the correlation matrix of indicators. This standardized approach is the default and only option in SmartPLS for PCA.

In the standardized case, an eigenvalue of 1 is the decision criterion applied to results based on the indicators' correlation matrix.

No. PCA is an exploratory data reduction technique, whereas the PLS-SEM algorithm estimates composite-based structural equation models. SmartPLS makes PCA available alongside the PLS-SEM and CB-SEM algorithms to support exploratory data analysis within a project.