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A Propensity Score Model of Costs and Benefits Related to Army Service

Award Information
Agency: Department of Defense
Branch: Army
Contract: N/A
Agency Tracking Number: 20502
Amount: $49,997.00
Phase: Phase I
Program: SBIR
Solicitation Topic Code: N/A
Solicitation Number: N/A
Timeline
Solicitation Year: N/A
Award Year: 1993
Award Start Date (Proposal Award Date): N/A
Award End Date (Contract End Date): N/A
Small Business Information
4100 N.w. Loop 410, Suite 230
San Antonio, TX 78229
United States
DUNS: N/A
HUBZone Owned: No
Woman Owned: No
Socially and Economically Disadvantaged: No
Principal Investigator
 Benjamin A. Fairbank, Ph.
 (512) 731-0000
Business Contact
Phone: () -
Research Institution
N/A
Abstract

A major obstacle to the establishment and measurement of valid cause and effect relationships between Army service and the costs and benefits of such service is the impossibility of performing experiments with random assignment of individuals to service and non-service groups. A recent innovation in statistical theory, propensity score methodology, allows one to draw from an observational (or retrospective) study virtually the same kind of causal inferences that previously have been possible only with experimental studies. Propensity score methodology has been successfully used in medical, social science, and educational contexts. Phase I will develop a specimen propensity score model of costs and benefits of Army service, will identify and specify the data sources necessary to implement and test the model, and will determine the procedures and scope of the effort needed to test the model. Phase I will include a preliminary specification of the variables appropriate for use in the final model. Attention will be given to the question of oversampling women and minorities so that separate models may be developed for them. Not only are such separate models desirable heuristically, they are also particularly appropriate when using propensity scores. Phase II will be devoted to the elaboration, detailed specification, testing, and evaluation of the model.

* Information listed above is at the time of submission. *

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