A MULTIPLE LINEAR REGRESSION ANALYSIS OF OFFICER CAREER ATTITUDES,

Abstract

A survey covering demographic, sociological, and attitudinal information was completed by a random sample of 5,000 junior Air Force officers. Their responses were evaluated in terms of their relationship to a criterion of expressed career intention. Multiple linear regression analyses were used to measure the unique contribution to prediction of some of the survey items beyond that provided by certain 'baseline' variables believed to predict career intent. A second analysis was completed on a subsample of officers grouped by source of commission and subdivided by length of commissioned service, regular or reserve status, nonrated or rated flying status, and science-engineering or nonscience and nonengineering groupings. Data illustrate that career intent can be more meaningfully evaluated in terms of membership variables than by gross source of commission grouping normally employed. Six survey items offered the greatest unique contribution to the prediction of the criterion: (1) family attitude toward an Air Force career; (2) factors influencing for and against a career, (3) effect of the offer of a regular commission; (4) challenge of the Air Force job versus a civilian job; (5) the importance and possibility of achieving certain incentives and rewards as part of an Air Force career; and (6) the officers' feelings about frequent change of residence. (Author)

Document Details

Document Type
Technical Report
Publication Date
Oct 01, 1965
Accession Number
AD0627651

Entities

People

  • Ernest C. Tupes
  • Lyde D. Kaapke
  • Ray W. Alvord

Tags

DTIC Thesaurus Topics

  • Air Force
  • Computing-Related Activities
  • Coverings
  • Data Science
  • Engineering
  • Information Science
  • Interdisciplinary Science
  • Linear Regression Analysis
  • Mathematical Analysis
  • Mathematics
  • Motivation
  • Regression Analysis
  • Statistical Samples
  • Surveys

Readers

  • Military Leadership and Professional Education.
  • Organizational Psychology.
  • Regression Analysis.