Criteria for Selection of First-Line Supervisors.

Abstract

This thesis identified criteria with predictive validity for the selection of successful first-time, first-line supervisors. Through meta-analysis it is possible to generalize the validity of predictors across cumulative studies. A validity generalization model, which corrects for artifactual variance, was used to process data from the findings of many previous studies based upon the job performance measurement criterion of first-line supervisors. Analysis revealed two predictors, How Supervise? and the General Mental Ability Measures, with substantial validity for predicting successful performance of first-line supervisors. The Bennett Mechanical Comprehension Test and the Otis Mental Ability Test also showed relatively high predictive validity; however, neither form of the Leadership Opinion Questionnaire evidenced predictive validity for successful job performance by first-line supervisors. The Wonderlic Personnel Test, although not highly predictive, may be useful in the absence of the other predictors. These findings may be of value to middle managers during the selection process in identifying promotion candidates with potential for successful performance as first-time, first-line supervisors.

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Document Details

Document Type
Technical Report
Publication Date
Sep 01, 1985
Accession Number
ADA160870

Entities

People

  • F. A. Burke

Organizations

  • Air Force Institute of Technology

Tags

DTIC Thesaurus Topics

  • Air Force
  • Civilian Personnel
  • Classification
  • Comprehension
  • Data Analysis
  • Databases
  • Leadership
  • Management Personnel
  • Measurement
  • Organizational Structure
  • Personnel Management
  • Personnel Selection
  • Psychology
  • Questionnaires
  • Statistics
  • Supervisors
  • Test And Evaluation

Fields of Study

  • Psychology

Readers

  • Instructional Design and Training Evaluation.
  • Psychometric Testing or Psychological Assessment.

Technology Areas

  • AI & ML
  • AI & ML - Bayesian Inference
  • AI & ML - Neural Networks