Arbitrator Evaluation and Selection: A Policy Capturing Approach.

Abstract

Policy capturing models have been applied in a wide variety of decision making scenarios with good results. This study validates the model Lambert developed in a recent thesis effort in order to help determine its potential for application in the labor-management arbitration setting. The experimental design permits policy comparisons between scorers and between arbitrators. The data was collected by having graduate students read and score published arbitration cases from three arbitrators. The data is analyzed using regression and ANOVA techniques. The results indicate a cue measurement problem is present, and the scorer makes a significance difference in the resulting regression model. The regression models explained only 30 percent of the variance at best indicating prediction using discriminant analysis would not be reliable. A modified model was developed to solve the cue measurement problem, and the resulting models explained 90 percent of the variance, however, award prediction using discriminant analysis is no longer valid. The modified modeling results allowed significant policy difference to be established between the three arbitrators selected for this study. Policy capturing provides a systematic framework for analyzing the decision policies of different arbitrators. Further research is recommended using the modified model. (Author)

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Document Details

Document Type
Technical Report
Publication Date
Sep 01, 1980
Accession Number
ADA093180

Entities

People

  • Douglas R. Thorsvik

Organizations

  • Air Force Institute of Technology

Tags

Communities of Interest

  • Biomedical
  • Ground and Sea Platforms
  • Human Systems

DTIC Thesaurus Topics

  • Agreements
  • Air Force
  • Computer Programs
  • Data Analysis
  • Discriminant Analysis
  • Employment
  • Experimental Design
  • Industrial Relations
  • Management Personnel
  • New York
  • Personnel Management
  • Statistics
  • Students
  • Systems Management
  • Test And Evaluation
  • Test Methods
  • United States

Readers

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  • Regression Analysis.