Consistent bootstrapping is the bootstrapping procedure applied to models estimated with the consistent PLS-SEM (PLSc-SEM) algorithm rather than the standard PLS-SEM algorithm. Like standard bootstrapping, it is a nonparametric procedure that allows researchers to test the statistical significance of results such as path coefficients, Cronbach's alpha, HTMT, and R² values. Researchers use consistent bootstrapping when their model has been estimated with PLSc-SEM and they need significance tests, standard errors, or confidence intervals for the PLSc-SEM parameter estimates.

Consistent bootstrapping follows the same resampling logic as standard bootstrapping: subsamples are drawn at random (with replacement) from the original data set, and the model is re-estimated on each subsample. The key difference is that every subsample is estimated using the consistent PLS-SEM (PLSc-SEM) algorithm instead of the standard PLS-SEM algorithm, so the resulting standard errors, t-values, and confidence intervals reflect the disattenuated, factor-consistent parameter estimates that PLSc-SEM produces.

In SmartPLS, consistent bootstrapping is available whenever a model uses the PLSc-SEM algorithm. It provides the same significance-testing settings as standard bootstrapping (e.g., number of subsamples, confidence interval method, test type, significance level, and random number generator seed), applied on top of the PLSc-SEM estimation.

Bootstrapping tests the significance of results from the standard PLS-SEM algorithm. Consistent bootstrapping applies the same nonparametric resampling logic, but each subsample is estimated with the consistent PLS-SEM (PLSc-SEM) algorithm, so it is used for significance testing in PLSc-SEM models.

Use consistent bootstrapping when your model has been estimated with PLSc-SEM. Since PLSc-SEM produces different (factor-consistent) parameter estimates than standard PLS-SEM, its significance tests should also be derived from PLSc-SEM re-estimations rather than standard PLS-SEM bootstrapping.

Like standard bootstrapping, consistent bootstrapping can be used to test the statistical significance of results such as path coefficients, Cronbach's alpha, HTMT, and R² values, based on the PLSc-SEM estimates.