COMMUNITY POLICING

Crime Reduction through Community Policing

Crime Reduction through Community Policing

Using Discriminant Analysis in Policing Research

Michael L. Birzer and Delores E. Craig-Moreland

Professional Issues in Criminal Justice Vol 3(2), 2008

Research questions

The research question that has been used in this study is description, reliability, causal explanation, prediction, and controls are important to the positivist paradigm of research. This complex process has been simplified by many of the statistical techniques like ANOVA that mainly examines one or two variables at a time, however, increasing number of statistical software has given the researchers an opportunity to include more variables in the analysis.

Background and relevance of the research questions

Discriminant analysis is a multivariate statistical technique used in policing research which aims to examine whether there are significant differences between groups of objects over a set of measured variables on them to, if any, explain in what sense are given and provide procedures systematic classification of new observations of unknown origin in one of the groups analyzed.

Discriminant analysis has become a valuable tool in social sciences particularly in the field of policing as discriminant functions provide a means to classify a case into the group that it mostly resembles and help investigators understand the nature of differences between groups. For example, a police officer might be interested in predicting whether a crime, if admitted, is more likely to rise (graduate from the college) or decline (drop out or fail) based on a set of predictor variables such as community policing, correction centers, age, and so forth.

A sample of criminals whose crime outcomes are known can be used to create a discriminant function by finding a linear combination of predictor variables that best separates Groups 1 and 2. This discriminant function can be used to predict the crime outcome of a new criminal whose actual group is unknown. In addition, discriminant functions can be used to study the nature of group differences by examining which predictor variables best predict group. This entry discusses the data considerations involved in discriminant analysis, the derivation and interpretation of discriminant functions, and the process of classifying a case into a group.

Literature Reviewed

The previous researches done on policing shows that discriminant analysis truly does not explain the variables as dependent and independent, while they were also unable to describe the groups and this analysis does not suggest the effect and cause of relationships. However, logistic regression are more appropriate in this regard as it delegate causation whereas, the nature of differentiation is mainly focused by the discriminant analysis.

The major task in discriminant analysis is to find discriminant functions that maximally separate groups and make the best possible predictions about groups. The previous research shows that dicriminant analysis mainly constructs the structure matrix; it helps the investigator separating the groups explained by the process and also permits them to name the discriminant function. On the other side logistic regressions mainly used to forecast that a person belongs to a certain group, as it generates the odd ...