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A Bayesian longitudinal model for quantifying students’ preferences regarding teaching quality indicators

Author

Listed:
  • D. Fouskakis

    (National Technical University of Athens)

  • G. Petrakos

    (Panteion University of Social and Political Sciences)

  • I. Rotous

    (National Technical University of Athens)

Abstract

The aim of the paper is to estimate the posterior mean values and analyze the posterior variation in students’ prioritization of teaching quality components within a 10-year frame. The results are based on longitudinal data gathered among Greek university students during the period of the national economic crisis in Greece spanned from 2009 to 2018. The analysis consists of fitting a Bayesian hierarchical beta regression model with a Dirichlet prior on the model coefficients that correspond to twenty quality attribute measures. Using this natural way to implement the usual constraints, the model coefficients can be interpreted as weights and thus they measure the relative importance that the students give to the different attributes. By estimating the posterior means and positioning measures of all consecutive sampling instances and summarizing posterior distributions of the differences between consecutive periods in the model weights, the study identifies and evaluates the major changes and patterns in students’ perception of academic quality over the ten-year sampling period.

Suggested Citation

  • D. Fouskakis & G. Petrakos & I. Rotous, 2020. "A Bayesian longitudinal model for quantifying students’ preferences regarding teaching quality indicators," METRON, Springer;Sapienza Università di Roma, vol. 78(2), pages 255-270, August.
    • Handle: RePEc:spr:metron:v:78:y:2020:i:2:d:10.1007_s40300-020-00175-5
    DOI: 10.1007/s40300-020-00175-5
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    References listed on IDEAS

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    1. Carpenter, Bob & Gelman, Andrew & Hoffman, Matthew D. & Lee, Daniel & Goodrich, Ben & Betancourt, Michael & Brubaker, Marcus & Guo, Jiqiang & Li, Peter & Riddell, Allen, 2017. "Stan: A Probabilistic Programming Language," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 76(i01).
    2. Silvia Ferrari & Francisco Cribari-Neto, 2004. "Beta Regression for Modelling Rates and Proportions," Journal of Applied Statistics, Taylor & Francis Journals, vol. 31(7), pages 799-815.
    3. D. Fouskakis & G. Petrakos & I. Vavouras, 2016. "A Bayesian hierarchical model for comparative evaluation of teaching quality indicators in higher education," Journal of Applied Statistics, Taylor & Francis Journals, vol. 43(1), pages 195-211, January.
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    Cited by:

    1. Zhuo Chen & Kang Tian, 2022. "Optimization of Evaluation Indicators for Driver’s Traffic Literacy: An Improved Principal Component Analysis Method," SAGE Open, , vol. 12(2), pages 21582440221, June.

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