The Outliers Influence on Adequateness of Classical and Robust Path Analysis

Abstract

Path analysis is formed from correlation and regression analysis which assumes a normal distribution. This assumption will be disturbed if there are outliers. This research aims to examine the influence of outliers on classical path analysis and robust path analysis. The data used as initial data is secondary data from Badan Pusat Statistik (BPS) publications on Indikator Kesejahteraan Rakyat, Statistik Ketenagakerjaan, Statistik Indonesia, and Survei Sosial Ekonomi Nasional. These data are used to generate data with a simulation scheme for the number of samples or n (20, 30, and 100), percentage of outliers or p (5%, 25% and 45%) and program repetitions or u (25, 50, 75, 100, 500 and 1000). Based on the combination of 3 treatments, the percentage of model adequateness was measured based on the Wald test and then an ANOVA test was carried out with 2-factor factorial design. The percentage of model adequateness of the classical path analysis model is decreasing as the number of samples increasing. In large sample sizes data, the adequateness tends to be different and increases as the percentage of outliers in the sample size data increases. The adequateness of the robust path analysis model is relatively very stable. The robust path analysis is adequate to use on small or large sample data with any percentage of outliers. The percentage of model adequateness being higher at the larger percentage of outliers. The classical path analysis model is influenced by outliers but the robust path analysis model is not influenced by outliers.