Specifications for an Operational Two-Tiered Classification System for the Army, Volume 2: Appendixes

Abstract

The broad objective of the present study is to design an improved two-tiered classification system and to compare its classification efficiency to the current operational aptitude area (AA) system. The total data set includes about 260,000 recruits serving in 170 different entry-level MOS during 1987-1989. The set includes all available ASVAB/Skill Qualification Test (SQT) data for MOS with adequate sample sizes collected by ARI during this time frame. The proposed system to be evaluated in this study would use an invisible or black-box first tier in which separate assignment variables (AVs) are computed for up to 150 job families. The first tier AVs are to be used in assigning recruits to entry-level MOS. The second tier is used in recruiting, counseling and administration. The proposed system to be evaluated in the visible second tier uses up to 17 families. It is proposed that the aptitude area scores of the visible system be recorded on each soldier's personnel record. The principal finding of the present study is that the unbiased overall mean predicted performance (MPP) of the 150 job family structure is .195 compared to the MPP for the existing operational system of .023, a gain of more than eight fold. The unbiased overall MPP for the 17 job families is .146. The 17 family system is obtained by shredding the existing AA families within the boundaries of the operational classification families to maximize the Horst index. Findings continue to support an early differential assignment theory (DAT) principle that maximum MPP is obtainable by using AVs for all jobs having adequate or stable validity data. The results clearly demonstrate that considerable classification efficiency is potentially obtainable from the existing ASVAB if it is used in accordance with DAT principles.

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Document Details

Document Type
Technical Report
Publication Date
Aug 01, 2000
Accession Number
ADA385348

Entities

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  • Cecil Johnson
  • Joseph Zeidner
  • Susan Weldon
  • Yefim Vladimirsky

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  • George Washington University

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