Applying the Internal Referencing Strategy to the Evaluation of Transfer of Training in Field Settings.

Abstract

Training evaluation is the determination of the effectiveness of a training program. While training researchers and practitioners generally prefer the use of an experimental research design to infer training effectiveness, practical constraints inherent in field settings often require that they employ a less rigorous quasi-experimental design. This paper evaluates a managerial training program in a field setting using the Internal Referencing Strategy (IRS), a quasi-experimental research design that infers training effectiveness when trainee pretest-posttest change on training-relevant test items is greater than pretest-posttest change on training-irrelevant test items. One hundred and eighty-two managers experienced a managerial training course and provided pretest and posttest data. Trainee learning was assessed using a 20-item multiple choice knowledge test and trainee transfer of training was assessed using a 15-item behavioral questionnaire. An analysis of the evaluative data provided some evidence of learning, and demonstrated transfer of training after items exhibiting ceiling effects were excluded from the analysis. Implications regarding the use of the IRS approach in training evaluation are discussed.

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Document Details

Document Type
Technical Report
Publication Date
Jun 18, 1997
Accession Number
ADA327448

Entities

People

  • Daniel J. Watola

Organizations

  • Air Force Institute of Technology

Tags

Communities of Interest

  • Biomedical

DTIC Thesaurus Topics

  • Air Force
  • Applied Psychology
  • Crime
  • Data Analysis
  • Education
  • Experimental Design
  • Human Resources
  • Management Personnel
  • Management Training
  • Performance Appraisals
  • Personnel Management
  • Psychology
  • Resource Management
  • Students
  • Test And Evaluation
  • Trainees
  • Training

Fields of Study

  • Education

Readers

  • Instructional Design and Training Evaluation.
  • Systems Analysis and Design

Technology Areas

  • AI & ML