QUANTILE BASED PLS-SEM WITH WILD BOOTSTRAP

Partial Least Squares SEM (PLS-SEM) is the recommended technique for structural equation modeling (SEM), which assesses correlations between latent components concurrently, particularly for small samples and non-normal data. But because traditional PLS-SEM only calculates average correlations between constructs, it runs the risk of overlooking variances in the quantile distribution. Consequently, the creation of the Quantile PLS-SEM approach, which incorporates quantile regression, provides a means to examine correlations across the entire data distribution. To improve estimation, wild bootstrap is used to address heteroscedasticity issues and produce more reliable inferences. The purpose of this study is to develop and apply Quantile based PLS-SEM with Wild Bootstrap to analyze the gizi data status of the Indonesian population based on the Survey Status Gizi Indonesia 2024. The analysis's findings indicate that specific and sensitive interventions have a significant impact on the gizi status of different quantities.