The Outlier Detection for Ordinal Data Using Scalling Technique of Regression Coefficients

International Conference on Mathematics: Education, Theory and Application

数学;教育

Adnan, Arisman^1 ; Sugiarto, Sigit^1

Department of Mathematics, Faculty of Mathematics and Natural Sciences, Universitas Riau, Indonesia^1

关键词: ANOVA (analysis of variance);    Ordinal data;    Ordinal logistic regression;    Ordinal regression;    Outlier Detection;    R languages;    Regression coefficient;   

Others  :  https://iopscience.iop.org/article/10.1088/1742-6596/855/1/012001/pdf
DOI  :  10.1088/1742-6596/855/1/012001

学科分类：发展心理学和教育心理学

来源: IOP

【 摘 要 】

The aims of this study is to detect the outliers by using coefficients of Ordinal Logistic Regression (OLR) for the case of k category responses where the score from 1 (the best) to 8 (the worst). We detect them by using the sum of moduli of the ordinal regression coefficients calculated by jackknife technique. This technique is improved by scalling the regression coefficients to their means. R language has been used on a set of ordinal data from reference distribution. Furthermore, we compare this approach by using studentised residual plots of jackknife technique for ANOVA (Analysis of Variance) and OLR. This study shows that the jackknifing technique along with the proper scaling may lead us to reveal outliers in ordinal regression reasonably well.