Ordinal Logistic Regression Analysis of Correlates of Crime Severity: The case of Tigray Region, Ethiopia

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2011-06

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Addis Abeba university

Abstract

The cumulative logit or the proportional odds regression model is one of the popular choices to study covariate effects on ordinal responses. This research is aimed at modeling a categorical response i.e., crime severity outcome in terms of some predictors, determines the goodness of fit as well as validity of the assumptions and selecting an appropriate and more parsimonious model there by proffer useful suggestions and recommendations. The proportional odds model was used as a tool to model the two major factors viz. socio-demographic (sex, age, education status, marital and employment status) and environmental (urban, rural) that affected the outcomes of crime. The fit, of the model was illustrated with 2,753 crime records obtained from regional police commission. This study provides some graphical and numerical methods for checking the adequacy of the proportional odds regression model. The methods focus on evaluating crime severity for specific covariate effects. The tested model showed good fit and performed differently depending on categorization of outcome, adequacy in relation to assumptions and goodness of fit. Findings of this study have shown that criminal’s age, educational background, employment status, marital status, and area of crime committed are significantly affect the outcome of crime. Criminals who are young, illiterate, employed, single household member, and rural areas of crime committed increased the odds of being in either serious or medium crime categories, than those criminals who are aged, educated, unemployed, married, and urban area of crime committed. The finding of this study indicates that the rise of crime in the region is generally as the result of direct effect of poverty. Policies and plans have to be put in place to improve young age individual education and crime prevention agencies need to issue to implement crime prevention strategies. Keywords: crime severity, ordinal logistic regression, proportional odds model

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Keywords

Crime Severity, Ordinal Logistic Regression, Proportional Odds Model

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