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Functional Principal Components Analysis on the Exemple of the Achievements of Students in the Years 2009-2017

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  • Sztemberg-Lewandowska Mirosława

    (Wroclaw University of Economics and Business, Wroclaw, Poland)

Abstract

The functional principal components analysis joins the advantages of the principal components analysis and provide analysis of dynamic data. The main difference in both methods is the type of data the PCA is based on multivariate data, whereas the FPCA on the functional data including curves and trajectories, i.e. a series of individual observations, not a single observation, as usual. The functional principal components analysis with functional data, will be used in the analysis. This method allows the analysis of dynamic data. The purpose of the article is to apply of functional principal components analysis to the problem of student’s achievements. The article was compared the level of students’ knowledge during different stages of education in 2009-2017. The analysis covers the average exam results after the II, III and IV stage of education.

Suggested Citation

  • Sztemberg-Lewandowska Mirosława, 2019. "Functional Principal Components Analysis on the Exemple of the Achievements of Students in the Years 2009-2017," Econometrics. Advances in Applied Data Analysis, Sciendo, vol. 23(4), pages 16-29, December.
    • Handle: RePEc:vrs:eaiada:v:23:y:2019:i:4:p:16-29:n:2
    DOI: 10.15611/eada.2019.4.02
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    References listed on IDEAS

    as
    1. Mirosława Sztemberg-Lewandowska, 2017. "The Achievements of Students at the Stages of Education from the Second to Fourth Using Functional Principal Component Analysis," Statistics in Transition New Series, Polish Statistical Association, vol. 18(1), pages 139-150, March.
    2. Peter Hall & Mohammad Hosseini‐Nasab, 2006. "On properties of functional principal components analysis," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 68(1), pages 109-126, February.
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    More about this item

    Keywords

    level of students’ knowledge; functional data; longitudinal data; functional principal components analysis;
    All these keywords.

    JEL classification:

    • C38 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Classification Methdos; Cluster Analysis; Principal Components; Factor Analysis

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