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: 97521 Add to Shopping cart Find in a Library
Title: Predictive Attribute Analysis - A Technical Report on the Validity and Reliability of the Method
Author(s): N F Walker
Corporate Author: New York State
Division of Criminal Justice Services
Office of Policy Analysis Research and Statistical Service
Uni
Date Published: 1985
Page Count: 117
Sponsoring Agency: National Institute of Justice/
Rockville, MD 20849
NCJRS Photocopy Services
Rockville, MD 20849-6000
New York State
Albany, NY 12203
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: This report describes Predictive Attribute Analysis (PAA), a quasi-statistical technique for the sequential subdivision of groups on the basis of characteristics of those groups that predict a criterion and discusses methodological issues that bear on its use.
Abstract: An overview of the literature on the PAA method is provided, and steps comprising PAA are identified. Advantages and disadvantages of the PAA method are cited, and theoretical and empirical issues relating to the method's validity and reliability are identified. Methodological and statistical issues are also addressed. Attention is focused on models and parameters, interaction effects, model detection by PAA, and subgroup classification. Validation methods are explored, and the cross-validation method and the bootstrap method for resampling are both analyzed. Some characteristics of the two-way contingency tables that are dichotomous in both variables are discussed, and considerations involved in the use of binary-coded information are detailed. Also, statistical measures of association and prediction that are appropriate for two-way contingency tables are addressed; ways that these different statistical measures can effect the results of a PAA are delineated. Alternatives to PAA are analyzed, and empirical studies of the PAA method are reviewed. The development and implementation of the mainframe computer program required for the design and conduct of a series of simulation studies are described. Simulation analyses are examined, and appropriate applications for PAA are identified. Finally, the choice of a PAA design is reviewed. Thirty-eight references and an appendix are included.
Index Term(s): Computer languages; Computer program models; Computer software; Prediction
To cite this abstract, use the following link:
http://www.ncjrs.gov/App/publications/abstract.aspx?ID=97521

*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.