Criminal Justice Analysis - Data Synthesis, Module 2

• NCJ Number: 81174
• Author(s): S Hirshorn
• Date Published: Unknown
• Length: 0 pages
• Grant Number(s): 78-MU-AX-0030
• Sponsoring Agency: US Dept of Justice
• Publication Format: Film
• Language: English
• Country: United States of America
• Note: *This document is currently unavailable from NCJRS. 2 videotapes, total running time 1 hour and 43 minutes, color, 1 inch. Part of the Criminal Justice Analysis Course held at Washburn University of Topeka.

Annotation

In this working classroom session of the course on criminal justice analysis, the foundation for measuring and obtaining data for specific variables is established by distinguishing procedures for measurement, planning a data collection effort, identifying the sources of data, and assessing hypotheses.

Abstract

Measurement is the process of assigning observable quantitative or qualitative indicators. It begins with detailing what is to be measured, when, and how. The criteria for good rules of measurement include singular number, positive occurrence, and exhaustive and mutually exclusive assignment. The concepts of reliability and validity determine the accuracy of measurement. Sources of error may be conceptual, technical, or management threats to data validity and reliability. Data sources are discussed as being primary or secondary. The former must be obtained by the analyst through surveys, field work, content analysis, experiments, historical research, or simulation modeling. Secondary sources have been previously collected by others and are accessible in readily usable form from the National Crime Panel, Uniform Crime Reports, census reports, and other sources. Security clearances or authorizations may be required to gain access to some of these sources. A data collection plan should be drawn up before embarking on the actual project. First, the analyst must identify the most pertinent hypotheses for the subsequent analysis, using criteria such as comprehensiveness, measurability, and importance of the hypotheses. The hypotheses must be testable, tractable, plausible, understandable, and communicable in order for data collection to proceed. This lecture is followed by a briefing on the nature of the major exercise involving all aspects of the course and culminating in the oral presentation of a product of analysis. The interrelated tasks in this exercise involve specifying the problem, assessing hypotheses, interpreting data, preparing the portfolio, and preparing and presenting a briefing. For the instructor's guide to the course, see NCJ 81170. For other videotaped sessions, see NCJ 81171-3, 81175-82, and 81186.