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