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Police Integrity Lost: A Study of Law Enforcement Officers Arrested

Award Information

Award #
Funding Category
Congressional District
Funding First Awarded
Total funding (to date)

Description of original award (Fiscal Year 2011, $261,480)

The proposed study seeks to provide a wealth of data on a phenomena that relates directly to police integrity-data that police executives do not have access to because they currently do not exist in any useable format. The first goal is to determine the nature and extent of police crime in the United States. The objective for this goal is to determine the incidence and prevalence of officers arrested. A second goal is to determine what factors influence how an agency responds to arrests of its officers. Objectives for this goal are to determine whether certain factors influence agency response and employment outcomes: a) severity of crimes for which officers are arrested; b) level of urbanization for each employing agency; c) geographic location for each employing agency; d) length of service and age of arrested officers; and, e) criminal case outcomes. A final goal is to foster police integrity by exploring whether officer arrests correlate with other forms of police misconduct. Objectives for this goal are to determine whether known arrested officers were also named as a defendant in any 42 U.S.C. §1983 civil actions during their careers, and to inform practitioners and policymakers of strategies that could better identify problem officers and those at risk for engaging in police misconduct and its correlates. The proposed project is a quantitative content analysis study of archived records consisting of news articles reporting several thousand arrests of police officers in the United States during the years 2005-2011. The primary information source is the Google News search engine and its Google Alerts email update service. The study will also triangulate data sources using the PACER system of federal court records to investigate correlates of police integrity and misconduct. Data will be recorded on a 184 variable coding instrument and will be analyzed using SPSS. Univariate analyses will report descriptive statistics. Bivariate analyses will measure association using Chi-Square to assess relationships and Cramer's V to assess strength of the relationships. Logistic regression will be used for multivariate analyses to determine the influence of predictor variables on employment and case outcomes.

Date Created: September 15, 2011