Structural Equation Models and Student Evaluation of Teaching: A PLS Path Modeling Study

Conference paper
Part of the Contributions to Statistics book series (CONTRIB.STAT.)


In Italian universities, teaching evaluation is in part based on students judgments concerning aspects related to courses and considered of preeminent interest for university management. A questionnaire is generally used to collect such data. The students judgments are expressed as a score on an ordinal scale.


Latent Variable Structural Equation Model Manifest Variable Outer Estimate Endogenous Latent Variable 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


  1. 1.
    Aiello F, Attanasio M (2004) How to transform a batch of simple indicators to make up a unique one. In: Atti della XLIII Riunione Scientifica della SIS, Padova, pp 327–338Google Scholar
  2. 2.
    Amato S, Esposito Vinzi V, Tenenhaus M (2005) A global goodness-of-fit index for PLS structural equation modeling. Technical report HEC School of Management, FranceGoogle Scholar
  3. 3.
    Bollen KA (1989) Structural equations with latent variables. Wiley, New York, NYGoogle Scholar
  4. 4.
    Balzano S, Trinchera L (2008) Structural equation models and student evaluation of teaching: a PLS approach. In: Atti del convegno DIVAGO, PalermoGoogle Scholar
  5. 5.
    Capursi V, Porcu M (2001) La didattica universitaria valutata dagli studenti: un indicatore basato su misure di distanza fra distribuzioni di giudizi. In: Atti Convegno Intermedio della Società Italiana di Statistica “Processi e Metodi Statistici di Valuatzione”, RomeGoogle Scholar
  6. 6.
    Chiandotto B, Bini M, Bertaccini B (2006) Evaluating the quality of the university educational process: an application of the ECSI model. In: Fabbris L (ed) Effectiveness of university education in Italy: employability, competences, human capital. Springer, HeidelbergGoogle Scholar
  7. 7.
    Chin WW (1998) The partial least squares approach to structural equation modeling. In: Marcoulides GA (ed) Modern methods for business research. Lawrence Erlbaum Associates, Mahwah, NJ, pp 295–336Google Scholar
  8. 8.
    CNVSU – Comitato Nazionale per la Valutazione del Sistema Universitario (2007) Note tecniche su dati ed informazioni per la Rilevazione Nuclei 2007, DOC 3/07Google Scholar
  9. 9.
    Efron B, Tibshirani RJ (1993) An introduction to the bootstrap. Chapman and Hall, New York, NYGoogle Scholar
  10. 10.
    Esposito Vinzi V, Trinchera L, Squillacciotti S, Tenenhaus M (2008) REBUSPLS: A response-based procedure for detecting unit segments in PLS path modeling. Appl Stochastic Models Bus Ind (ASMBI) 24:439–458CrossRefGoogle Scholar
  11. 11.
    Grilli L, Rampichini C (2007) Multilevel factor models for ordinal variables. Struct Equ Modeling 14(1):1–25CrossRefGoogle Scholar
  12. 12.
    Guolla M (1999) Assessing the teaching quality to student satisfaction relationship: applied customer satisfaction research in the classroom. J Mark Theory Pract 7(3):87–98Google Scholar
  13. 13.
    Hahn C, Johnson M, Herrmann A, Huber F (2002). Capturing customer heterogeneity using a finite mixture PLS approach. Schmalenbach Bus Rev 54:243–269Google Scholar
  14. 14.
    Jöreskog KG, Sörbom D (1979) Advances in factor analysis and structural equation models. Abstract Books, Cambridge, MAGoogle Scholar
  15. 15.
    Lovaglio PG (2002) La stima di variabili latenti da variabili osservate miste. Statistica LXII 2:203–213Google Scholar
  16. 16.
    Martensen A, Gronholdt L, Eskildsen JK, Kristensen K (2000) Measuring student oriented quality in higher education: application of the ECSI methodology. Sinergie Rapporti di Ricerca 9:372–383Google Scholar
  17. 17.
    Nardo M, Saisana M, Saltelli A, Tarantola S, Hoffman A, Giovannini E (2005) Handbook on constructing composite indicators: methodology and user guide. OECD statistics working paperGoogle Scholar
  18. 18.
    Ramipichini C, Grilli L, Petrucci A (2004) Analysis of university course evaluations: from descriptive measures to multilevel models. Stat Methods Appt 13(3):357–373Google Scholar
  19. 19.
    Tenenhaus M (2008) Component-based structural equation modelling. Total Qual Manage Bus Excel 19(7):871–886CrossRefGoogle Scholar
  20. 20.
    Tenenhaus M, Esposito Vinzi V, Chatelin YM, Lauro NC (2005) PLS path modeling. Comput Stat Data Anal 48:159–205CrossRefGoogle Scholar
  21. 21.
    Trinchera L (2007) Unobserved heterogeneity in structural equation models: a new approach in latent class detection in PLS path modeling. PhD thesis, DMS, University of NaplesGoogle Scholar
  22. 22.
    Trinchera L, Russolillo G (2009) Role and treatment of categorical variables in PLS path models for composite indicators. In Esposito Vinzi V, Tenenhaus M, Guan R (eds) Proceedings of the 6th international conference on partial least squares and related methods, pp 23–27, PHEI, ISBN: 978-7-121-09342-5Google Scholar
  23. 23.
    Werts CE, Linn RL, Jöreskog KG (1974) Intraclass reliability estimates: testing structural assumptions. Educ Psychol Meas 34(1):25–33CrossRefGoogle Scholar
  24. 24.
    Wold H (1982) Soft modeling: the basic design and some extensions. In: Jöreskog KG, Wold H (eds) Systems under indirect observation, Part 2. North-Holland, Amsterdam, pp 1–54Google Scholar
  25. 25.
    XLSTAT (2009) Addinsoft, Paris, France (

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© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  1. 1.Dipartimento di Scienze EconomicheUniversità degli Studi di CassinoCassinoItaly
  2. 2.Dipartimento di Studi sullo Sviluppo EconomicoUniversità di MacerataMacerataItaly

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