(2022) explain bootstrapping in more detail.
In addition, bootstrapping provides the standard errors for the estimates, which allow t-values to be calculated to assess the significance of each estimate. The parameter estimates (e.g., outer weights, outer loadings and path coefficients) estimated from the subsamples are used to derive the 95% confidence intervals for significance testing (e.g., original PLS-SEM results are significant when they are outside the confidence interval). This process is repeated until a large number of random subsamples has been created, typically about 10,000. The subsample is then used to estimate the PLS path model. In bootstrapping, subsamples are created with randomly drawn observations from the original set of data (with replacement). Instead, PLS-SEM relies on a nonparametric bootstrap procedure (Efron and Tibshirani, 1986 Davison and Hinkley, 1997) to test the significance of estimated path coefficients in PLS-SEM. PLS-SEM does not assume that the data is normally distributed, which implies that parametric significance tests (e.g., as used in regression analyses) cannot be applied to test whether coefficients such as outer weights, outer loadings and path coefficients are significant. Bootstrapping is a nonparametric procedure that allows testing the statistical significance of various PLS-SEM results such path coefficients, Cronbach’s alpha, HTMT, and R² values.
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