Winsorization on Linear Mixed Model (Case Study: National Exam of Senior High School in West Java)

摘要

In the case of hierarchical data is typically modeled with linear mixed model (LMM). The LMM requires the assumption of normality which is error and random effects are assumed normal distribution. However in practice, to meet the assumption of normality is difficult especially if the sample is small. Violation of the normality assumption can be caused by outliers. In this paper, we will examine the effect of outliers on the random effects and error and overcome them with the Winsorization technique. The result of application indicated that Winsorization technique with c-tuning constant iterative process produced root mean squared error, AIC, and BIC are smaller than the others. We conclude that Winsorization technique can be used to overcome outliers in linear mixed model fitting.