CB-SEM Bootstrapping Issues

CB-SEM may not generate all requested bootstrap samples, resulting in a smaller number of samples used for calculations than specified. This is due to the reliance on convergence, where the model reaches a stable solution. Some bootstrap samples may not achieve convergence, especially when encountering problematic data patterns or model misspecification. In such cases, the algorithm terminates prematurely, excluding the subsample from the bootstrap results and reducing the count of bootstrap samples.

It's worth noting that obtaining fewer bootstrap samples than desired does not necessarily invalidate the results. Researchers should thoroughly assess the available samples and carefully consider the reasons for the reduced count to ensure the validity and reliability of their findings.