An Evaluation of the Factors Used to Predict Writing Ability at the Air Force Institute of Technology.

Abstract

A study of 574 students at the Air Force Institute of Technology compared performance, education, and experience factors (the later two as stated by the students themselves) to a locally developed estimate of true writing ability (WGPA). This exploratory research was additionally intended to assess the effectiveness of AFITs current writing student skill diagnostic and instructional system. Direct (essay evaluation) and indirect (objective test) evaluations of AFIT student writing ability were analyzed for their predictive impact. The statistical analysis procedures used in this study included the factor analysis of a survey, ANOVA, the adjustment of multiple correlations due to measurement error and range attenuation, and the performance of a regression analysis using the raw data and the adjusted correlation matrix. The results of this study indicate AFIT's direct evaluation portion (essay examination) is useful for determining writing ability; the indirect portion (objective test) did not significantly contribute to the model. Due to the combination of independent variables chosen for the predictive model, the study was unable to identify the immediate benefits of the written communications review course on AFIT performance.

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Document Details

Document Type
Technical Report
Publication Date
Sep 01, 1996
Accession Number
ADA319925

Entities

People

  • Darrin E. Farr

Organizations

  • Air Force Institute of Technology

Tags

Communities of Interest

  • Human Systems
  • Materials and Manufacturing Processes

DTIC Thesaurus Topics

  • Air Force
  • Analysis Of Variance
  • Business Administration
  • Data Science
  • Databases
  • Factor Analysis
  • Information Science
  • Instructors
  • Mathematics
  • Predictive Modeling
  • Regression Analysis
  • Social Sciences
  • Statistical Analysis
  • Statistics
  • Students
  • Surveys
  • Test And Evaluation

Fields of Study

  • Education

Readers

  • Computational Modeling and Simulation
  • Regression Analysis.
  • STEM Education