The Consistency of Blindfolding in the Path Analysis Model with Various Number of Resampling

摘要

The use of regression analysis has not been able to deal with the problems of complex relationships with several response variables and the presence of intervening endogenous variables in a relationship. Analysis that is able to handle these problems is path analysis. In path analysis there are several assumptions, one of which is the assumption of residual normality. If the normality residual assumptions are not met, then estimating the parameters can produce a biased estimator, a large and not consistent range of estimators. Unmet residual normality problems can be overcome by using resampling. Therefore in this study, a simulation study was conducted to apply resampling with the blindfold method to the condition that the normality assumption is not met with various levels of resampling in the path analysis. Based on the simulation results, different levels of closeness occur consistently at different resampling quantities. At a low level of closeness, it is consistent with the resampling magnitude of 1000. At a moderate level, a consistent level of resampling of 500 occurs. At a high level of closeness, it is consistent with the amount of resampling 1400.