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Research of higher engineering education quality on the base of students Interviewing data by nonlinear principal components analysis (NLPCA)

Author

Listed:
  • Isakin, Maksim

    () (Higher School of Economics, Russia)

  • Teplykh, Grigory

    () (Higher School of Economics, Russia)

Abstract

The paper explores a suitability of higher education quality measurement from student’s point of view, and analyses results of interviewing of students from engineering specialties in Perm universities. Nonlinear Principal Components Analysis (NLPCA) in interpretation of Gifi system was used as the tool for data processing. It takes into account a dissimilar statistical nature of questionnaire indicators. The method can be very promising for various socio-economic researches.

Suggested Citation

  • Isakin, Maksim & Teplykh, Grigory, 2011. "Research of higher engineering education quality on the base of students Interviewing data by nonlinear principal components analysis (NLPCA)," Applied Econometrics, Publishing House "SINERGIA PRESS", vol. 21(1), pages 70-96.
  • Handle: RePEc:ris:apltrx:0014
    as

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    File URL: http://pe.cemi.rssi.ru/pe_2011_1_70-96.pdf
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    References listed on IDEAS

    as
    1. Voss, Roediger & Gruber, Thorsten & Szmigin, Isabelle, 2007. "Service quality in higher education: The role of student expectations," Journal of Business Research, Elsevier, vol. 60(9), pages 949-959, September.
    2. Francesco Lagona & Fabio Padovano, 2007. "A nonlinear principal component analysis of the relationship between budget rules and fiscal performance in the European Union," Public Choice, Springer, vol. 130(3), pages 401-436, March.
    Full references (including those not matched with items on IDEAS)

    More about this item

    Keywords

    higher education quality; engineering education; questioning; method of principal components; nonlinear principal components analysis; NLPCA; Gifi system;

    JEL classification:

    • C43 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Index Numbers and Aggregation
    • C61 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Optimization Techniques; Programming Models; Dynamic Analysis
    • C63 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Computational Techniques
    • C83 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Survey Methods; Sampling Methods
    • C89 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Other
    • I21 - Health, Education, and Welfare - - Education - - - Analysis of Education
    • I23 - Health, Education, and Welfare - - Education - - - Higher Education; Research Institutions

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