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NCJRS Abstract

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NCJ Number: 140144 Find in a Library
Title: Modeling of Intervention Effects (From Drug Abuse Prevention Intervention Research: Methodological Issues, P 159-182, 1991, Carl G. Leukefeld and William J. Bukoski, eds. - see NCJ-140135)
Author(s): P M Bentler
Corporate Author: US Dept of Health and Human Services
Alcohol, Drug Abuse, and Mental Health Admin
United States of America
Date Published: 1991
Page Count: 24
Sponsoring Agency: National Institute of Justice/
Rockville, MD 20849
National Institute on Drug Abuse
Bethesda, MD 20892-9561
NCJRS Photocopy Services
Rockville, MD 20849-6000
Superintendent of Documents, GPO
Washington, DC 20402
US Dept of Health and Human Services
Rockville, MD 20857
Grant Number: DA-00017; DA-01070
Sale Source: Superintendent of Documents, GPO
Washington, DC 20402
United States of America

National Institute of Justice/
NCJRS paper reproduction
Box 6000, Dept F
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United States of America

NCJRS Photocopy Services
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United States of America
Document: PDF
Type: Survey
Language: English
Country: United States of America
Annotation: Structural equation modeling of drug prevention intervention effects can isolate true effects from observed effects, differentiate pretest differences from treatment effects, control for missing data, and evaluate the effectiveness of treatment.
Abstract: While there has long been a general consensus that structural modeling could distinguish observed from true effects, until 1978 procedures for isolating true effects were believed to depend on estimates of reliability or error variance and were typically made from extraexperimental information; now, appropriate latent variable design can effectively isolate the relevant effects of interest. The author uses the example of the evaluative study of the quasi- experimental Head Start program to illustrate these points. Another study is used to illustrate how structural modeling can help analyze intervention data in terms of pretest versus treatment effects. Because nearly all prevention/treatment studies have a serious problem of attrition, structural modeling can help control for missing data, particularly when the missing data contain a few predominantly patterns of missing data. Structural modeling can also be used to identify indicators of program participation in order to assess intervention effectiveness. 2 figures, 14 references, and 1 appendix
Main Term(s): Drug prevention programs; Modeling techniques
Index Term(s): Program design; Program evaluation; Research methods
Note: NIDA Research Monograph 107
To cite this abstract, use the following link:
http://www.ncjrs.gov/App/publications/abstract.aspx?ID=140144

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