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Dissertation/Thesis Abstract

Developing an automated task-based Minimum Qualification system to lower erroneous rejection and adverse impact
by Tietze, Brandon D., M.A., California State University, Long Beach, 2012, 93; 1522262
Abstract (Summary)

Entrance or Minimum Qualification (MQ) systems are designed to screen out patently unqualified job applicants based on work and education history. However, MQs based on arbitrarily set standards may erroneously reject candidates who do in fact possess the necessary knowledge, skills, and abilities required for successful job performance. Research suggests that the most reliable way to link work history to future job performance is by measuring the amount of times an applicant has previously performed a task. Using 427 job applicants and their corresponding exam scores, the current study explored whether different outcomes occur for the same applicants under a modified task-based MQ system compared with a traditional duration-based MQ system. Results indicate that the task-based system produces less adverse impact at the screening stage, less erroneous rejection, and a larger number of applicants who pass the job knowledge test. Findings reveal implications for MQ development and usage.

Indexing (document details)
Advisor: Warren, Christopher
School: California State University, Long Beach
School Location: United States -- California
Source: MAI 51/05M(E), Masters Abstracts International
Subjects: Organization Theory, Organizational behavior
Publication Number: 1522262
ISBN: 978-1-267-97757-1
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