Comparing Linear Discriminant Function With Logistic Regression for the Two-Group Classification Problem
ABSTRACT The performances of predictive discriminant analysis (PDA) and logistic regression (LR) for the 2-group classification problem were compared. The authors used a fully crossed 3-factor experimental design (sample size, group proportions, and equal or unequal covariance matrices) and 2 data patterns. When the 2 groups had equal covariance matrices, PDA and LR performed comparably for the conditions of both equal and unequal group proportions. When the 2 groups had unequal covariance matrices (4:1, as implemented in this study) and very different group proportions, PDA and LR differed somewhat with regard to the classification error rates of the 2 groups, but the classification error rates of the 2 methods for the total sample remained comparable. Sample size played a relatively minor role in the classification accuracy of the 2 methods, except when LR was used under relatively small sample-size conditions.
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ABSTRACT: This study aims to advance our understanding of the assessment of numerous factors associated with recidivism among females and males involved in the juvenile justice system. In particular, this study examined the reliability (i.e., inter-rater) and validity (i.e., construct, criterion, and predictive) of the Santa Barbara Assets and Risks Assessment (SB ARA) with a population of first time juvenile offenders (n=423). The results of this study provide preliminary evidence that the SB ARA has adequate reliability and validity properties. Notably, the SB ARA provided prediction of recidivism for both females and males. The analyses also revealed a different set of indicators that predicted recidivism for females and males, thus, providing evidence supporting the position that there are some unique and some common indicators predicting recidivism for females and males. It is proposed that the SB ARA provides an exemplar in assessing both assets and risks among many salient developmental dimensions, is appropriate to use with males and females, and provides a better understanding of youths served by juvenile justice professionals.Education and Treatment of Children 11/2004; 27(4):353-373.
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ABSTRACT: In this study, the high risk areas of Sichuan Province with debris flow, Panzhihua and Liangshan Yi Autonomous Prefecture, were taken as the studied areas. By using rainfall and environmental factors as the predictors and based on the different prior probability combinations of debris flows, the prediction of debris flows was compared in the areas with statistical methods: logistic regression (LR) and Bayes discriminant analysis (BDA). The results through the comprehensive analysis show that (a) with the mid-range scale prior probability, the overall predicting accuracy of BDA is higher than those of LR; (b) with equal and extreme prior probabilities, the overall predicting accuracy of LR is higher than those of BDA; (c) the regional predicting models of debris flows with rainfall factors only have worse performance than those introduced environmental factors, and the predicting accuracies of occurrence and nonoccurrence of debris flows have been changed in the opposite direction as the supplemented information.Geomorphology 11/2013; · 2.58 Impact Factor
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ABSTRACT: All effect sizes are sensitive to design flaws and the failure to meet analytic assumptions. But some effect sizes appear to be more robust to assumption violations (e.g., homogeneity of variance). The present study extended prior Monte Carlo research by exploring the robustness of group overlap I indices at the relatively small sample sizes used in some research. I effects are statistically appealing because these indices can be applied across (a) both univariate and multivariate analyses and (b) conditions of either variance homogeneity or variance heterogeneity.Educational and Psychological Measurement 01/2007; 67(1):59-72. · 1.07 Impact Factor