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Principal Component Analysis of Students Academic Performance

Author

Listed:
  • F. B. K. Twenefour

    (Department of Mathematics and Statistics, Takoradi Polytechnic, P. O. Box 256, Takoradi, Western Region, Ghana.)

  • E. N. N. Nortey

    (Department of Statistics, University of Ghana, P. O. Box LG 115, Legon, Accra, Ghana.)

  • E. M. Baah

    (Department of Mathematics and Statistics, Takoradi Polytechnic, P. O. Box 256, Takoradi, Western Region, Ghana.)

Abstract

The purpose of this study was to identify a metric for measuring students’ performance in the Department of Mathematics and Statistics of a public university in Ghana. Some of the students of the department are of the view that the current grading system used by the Department does not do a good job of distinguishing between the performances of students, as at times students of different academic performance could end up with the same Grade Point Average (GPA), a performance measure. Data for the research which covers the 2012/2013 third year students of the Department were obtained from the university’s student records unit. Principal Component Analysis (PCA) was used to analyze the data. Three principal components were retained as rules or indices for the classification of students’ performance. A derivative of the first principal component, RSI, could serve as a new performance measure for the Department as it takes into consideration differences in the raw scores of the students.

Suggested Citation

  • F. B. K. Twenefour & E. N. N. Nortey & E. M. Baah, 2015. "Principal Component Analysis of Students Academic Performance," International Journal of Business and Social Research, MIR Center for Socio-Economic Research, vol. 5(2), pages 42-54, February.
    • Handle: RePEc:mir:mirbus:v:5:y:2015:i:2:p:42-54
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    More about this item

    Keywords

    Academic performance; principal component analysis; relative score index (RSI).;
    All these keywords.

    JEL classification:

    • C1 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General
    • C3 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables
    • C4 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics
    • I2 - Health, Education, and Welfare - - Education

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