skip navigation

PUBLICATIONS

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: 90476 Add to Shopping cart Find in a Library
Title: Increasing the Statistical Power of Empirically Derived Taxonomies in Criminal Justice Research - Final Report
Author(s): C Edelbrock
Corporate Author: University of Pittsburgh
United States of America
Date Published: 1982
Page Count: 116
Sponsoring Agency: National Institute of Justice (NIJ)
Washington, DC 20531
National Institute of Justice/
Rockville, MD 20849
NCJRS Photocopy Services
Rockville, MD 20849-6000
University of Pittsburgh
Pittsburgh, PA 15213
US Dept of Justice NIJ Pub
Washington, DC 20531
Grant Number: 81-IJ-CX-0059
Sale Source: National Institute of Justice/
NCJRS paper reproduction
Box 6000, Dept F
Rockville, MD 20849
United States of America

NCJRS Photocopy Services
Box 6000
Rockville, MD 20849-6000
United States of America
Document: PDF
Language: English
Country: United States of America
Annotation: Computer evaluations of inverse factor analysis, nonhierarchical clustering technique, and centroid clustering were completed on 20 mulitvariate normal mixtures. A strong relationship was found between level of coverage and both accuracy of clustering solutions and the statistical power of cluster-based classifications.
Abstract: For each of the three clustering procedures, 100-percent coverage resulted in less than optimal accuracy in recovering underlying populations from the computer-generated mixtures. For all three methods, the accuracy of clustering solutions was substantially increased by leaving 11-25 percent of the subjects unclassified. Results suggest that accuracy of clustering solutions can be increased in the range of 55-85-percent coverage. For all the methods tested, increasing coverage about 85 percent had deleterious effects on clustering accuracy. Graphs, tables, and 75 references are included. Technical data are appended. (Author abstract modified)
Index Term(s): Factorial research design
To cite this abstract, use the following link:
http://www.ncjrs.gov/App/publications/abstract.aspx?ID=90476

*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.