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Strategies for evaluating the effectiveness of training programs

A R Gotsch1, B L Weidner

  • 1Department of Environmental and Community Medicine, Robert Wood Johnson Medical School, University of Medicine and Dentistry of New Jersey, Piscataway.

Occupational Medicine (Philadelphia, Pa.)
|April 1, 1994
PubMed
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Developing effective training evaluation requires careful consideration of practical and theoretical aspects. Standardized variables and robust data collection/analysis are crucial for assessing training quality and informing policy.

Area of Science:

  • Training and Development
  • Program Evaluation
  • Educational Measurement

Background:

  • Developing effective evaluation activities for training programs requires careful planning.
  • Existing literature offers insights from the training field, program research, and practical constraints.

Purpose of the Study:

  • To outline practical and theoretical considerations for developing training evaluation activities.
  • To describe evaluation choices based on field experiences, research rationale, and realistic demands.
  • To present technical issues in data collection and analysis for trainee, program, and test data.

Main Methods:

  • Review of practical and theoretical considerations in training evaluation.
  • Discussion of data collection optimization and inter-program interfacing.

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  • Presentation of data analysis options from existing training field models and methods.

    • Main Results:

    • Standardized variables should be used where possible to advance training and reporting.
    • Demographic, program, and effectiveness measures (tests and nontests) are vital for assessing training quality.
    • Behavior assessment at the worksite is recommended for a comprehensive evaluation.

    Conclusions:

    • Effective training evaluation hinges on selecting critical variables and maximizing data collection/analysis potential.
    • The value of program evaluation for policy development depends on its quality and collaborative reporting of outcomes.
    • Most evaluation issues are modifiable, allowing for technical concerns to be addressed and data strength maximized.