skip navigation


Register for Latest Research

Stay Informed
Register with NCJRS to receive NCJRS's biweekly e-newsletter JUSTINFO and additional periodic emails from NCJRS and the NCJRS federal sponsors that highlight the latest research published or sponsored by the Office of Justice Programs.

NCJRS Abstract

The document referenced below is part of the NCJRS Virtual Library collection. To conduct further searches of the collection, visit the Virtual Library. See the Obtain Documents page for direction on how to access resources online, via mail, through interlibrary loans, or in a local library.


NCJ Number: 78710 Add to Shopping cart Find in a Library
Title: Aggregation in Data Tables - Implications for Evaluating Criminal Justice Statistics (From Confirmatory and Exploratory Analysis of the Spatio and Temporal Properties of Crime Data, P 22-57, 1981, by Reginald G Golledge and Lawrence J Hubert - See NCJ-78709)
Author(s): L J Hubert; R G Golledge; C M Costanzo; T Kenney
Corporate Author: University of California, Santa Barbara
United States of America
Date Published: 1981
Page Count: 36
Sponsoring Agency: National Institute of Justice (NIJ)
Washington, DC 20531
National Science Foundation
Arlington, VA 22230
University of California, Santa Barbara
Santa Barbara, CA 93106
US Dept of Justice NIJ Pub
Washington, DC 20531
Grant Number: 79-NI-AX-0057; SOC-77-28227
Type: Statistics
Format: Document
Language: English
Country: United States of America
Annotation: Implications of aggregation in data tables for evaluating criminal justice statistics are dicussed.
Abstract: Data collected on a set of objectors (e.g., cities) over a set of attributes (e.g., time points) can be subjected to a variety of aggregation schemes. In the study's first example of two aggregation schemes, a rank transformation was used within each row and on the rank sums for each column. This process ensures that each object or row contributes equally, so that some degree of natural comparability exists between the summary statistics of the two aggregation schemes. To develop more explicit relationships in terms of formulas, however, the transformations used in most of the study were based on obtaining z-scores. Observations within rows were aligned for location and scale. This convention allowed precise connections to be developed between the two aggregation schemes in terms of summary indices and z-statistics. Matrix extensions offer flexibility in defining different relationships among the attributes, but the problem of defining a transformation on the aggregate data matrix makes it difficult to develop precise formulas for connecting the two aggregation schemes. The methods of data aggregation discussed represent both ongoing procedures used in geography for aggregating data and alternatives to those standard procedures. The two-group discussion is concerned with the concordance between two classes, even though the various summary indices were subject to modification by the degree of internal concordance. Tabular data and 15 reference listings are provided, and definitions are appended. (Author summary modified)
Index Term(s): Crime Statistics; Evaluation; Mathematical modeling; Nonbehavioral correlates of crime; Statistical analysis
Note: Available on microfiche from NCJRS as NCJ-78709.
To cite this abstract, use the following link:

*A link to the full-text document is provided whenever possible. For documents not available online, a link to the publisher's website is provided. Tell us how you use the NCJRS Library and Abstracts Database - send us your feedback.