IDEAS home Printed from https://ideas.repec.org/p/pra/mprapa/43412.html
   My bibliography  Save this paper

Modelli a Equazioni Strutturali per la Valutazione dell'Esperienza Universitaria nell'Ateneo Fiorentino
[Structural Equation Models for the assessment of the University experience at the University of Florence]

Author

Listed:
  • Parrini, Alessandro
  • Doretti, Marco
  • Lapini, Gabriele

Abstract

Every student who has studied at the University of Florence is supposed to fill in a questionnaire prepared by the interuniversity consortium "Almalaurea". This survey concerns the general quality of the college and makes it possible to express the level of satisfaction about many aspects of the University experience. In this paper we wish to evaluate the relationship between observed variables and latent variables of interest: The structural equation models (SEM) is the methodology which suits best our needs. By means of a SEM we aim at building a model that reproduces the determinants of students’ satisfaction. Like any other statistical tool, the SEM is not suitable for causal analysis. However, under certain assumptions, it turns out that the model employed is an adequate representation of the reality under study.

Suggested Citation

  • Parrini, Alessandro & Doretti, Marco & Lapini, Gabriele, 2010. "Modelli a Equazioni Strutturali per la Valutazione dell'Esperienza Universitaria nell'Ateneo Fiorentino [Structural Equation Models for the assessment of the University experience at the University," MPRA Paper 43412, University Library of Munich, Germany.
  • Handle: RePEc:pra:mprapa:43412
    as

    Download full text from publisher

    File URL: https://mpra.ub.uni-muenchen.de/43412/1/MPRA_paper_43412.pdf
    File Function: original version
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. O'Loughlin, Christina & Coenders, Germà, 2002. "Application of the European Customer Satisfaction Index to Postal Services. Structural Equation Models versus Partial Least Squares," Working Papers of the Department of Economics, University of Girona 4, Department of Economics, University of Girona.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Tsao, Wen-Yu, 2013. "The fitness of product information: Evidence from online recommendations," International Journal of Information Management, Elsevier, vol. 33(1), pages 1-9.
    2. Coenders, Germà & Bisbe, Josep & Saris, Willem E. & Batista-Foguet, Joan M., 2003. "Moderating Effects of Management Control Systems and Innovation on Performance. Simple Methods for Correcting the Effects of Measurement Error for Interaction Effects in Small Samples," Working Papers of the Department of Economics, University of Girona 7, Department of Economics, University of Girona.
    3. Monika Oleksiak, 2009. "Satisfaction Drivers in Retail Banking: Comparison of Partial Least Squares and Covariance Based Methods," Central European Journal of Economic Modelling and Econometrics, Central European Journal of Economic Modelling and Econometrics, vol. 1(1), pages 83-102, March.
    4. Hananiel M. Gunawan & Oliandes Sondakh,, 2020. "How to Enhance Word of Mouth in the Era of E-commerce: Case study of Tokopedia," International Journal of Science and Business, IJSAB International, vol. 4(9), pages 47-59.
    5. Dongping Liu & Hai Zhang, 2021. "Developing a New Model for Understanding Teacher Satisfaction With Online Learning," SAGE Open, , vol. 11(3), pages 21582440211, July.
    6. Pradeep Kumar Mohanty & N. Senthil Kumar, 2017. "Measuring farmer’s satisfaction and brand loyalty toward Indian fertilizer brands using DEA," Journal of Brand Management, Palgrave Macmillan, vol. 24(5), pages 467-488, October.

    More about this item

    Keywords

    Structural Equation Models (SEM); Latent Variables; Customer Satisfaction;
    All these keywords.

    JEL classification:

    • C5 - Mathematical and Quantitative Methods - - Econometric Modeling
    • C3 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:pra:mprapa:43412. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Joachim Winter (email available below). General contact details of provider: https://edirc.repec.org/data/vfmunde.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.