Comparison of Parameter Estimator Efficiency Levels of Path Analysis with Bootstrap and Jack Knife (Delete-5) Resampling Methods on Simulation Data

Abstract

In practice, the assumptions of normality are often not met, this causes the estimation of the resulting parameters to be less efficient. Problems with the assumption that normality is not met can be overcome by resampling. The use of resampling allows data to be applied free from distribution assumptions. In this study, a simulation study was carried out by applying bootstrap resampling and jackknife resampling (delete-5) on path analysis assuming that the normality of the alignment was not met and the resampling amount set at 1000 with the degree of closeness between variables consisting of low closeness, medium closeness, high closeness and closeness level representing the level low to high closeness. Based on the simulation results, the resampling 1000 magnitude is able to overcome the problem of the assumptions of unmet normality. In addition, a comparison between bootstrap and jackknife resampling for conditions of side normality assumptions is not fulfilled and the closeness of the relationship between low, medium, high and closeness variables representing low to high closeness levels, the estimation results of path analysis parameters obtained by resampling jackknife are more efficient than resampling bootstrap.