University Teaching and Students’ Perception: Models of the Evaluation Process

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


The diffusion of a culture of evaluation in the Italian Universities has changed the logic and the development of several activities/procedures. As a consequence, Universities perform periodic surveys in order to assess the students’ satisfaction with respect to the main conditions of teaching and the environment where teaching takes place. In addition, several projects and groups have been involved with statistical analyses of University evaluation.


Differential Item Function Item Response Theory Latent Trait Discrimination Parameter Partial Credit Model 
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.



This chapter has been presented at the Conference: “DIVAGO: la Statistica, la Valutazione e l’Università”, University of Palermo, 10–12 July 2008. We thank discussants and referees whose constructive considerations improved the final version. The research has been realized within the 2006 PRIN-MIUR project: “Stima e verifica di modelli statistici per l’analisi della soddisfazione degli studenti universitari” and with the scientific support of CFEPSR, Portici. Authors thank University of Naples Federico II, and especially the “Nucleo di Valutazione di Ateneo” and UPSV, for providing the data set which has been partly analyzed in this chapter. This is a joint work; however, M. Iannario wrote Sects. 7.4, 7.5 and 7.6 and D. Piccolo the others.


  1. 1.
    Agresti A (2002) Categorical data analysis, 2nd edn. Wiley, New York, NYCrossRefGoogle Scholar
  2. 2.
    Amemiya T (1981) Qualitative response models: a survey. J Econ Lit XIX:1483–1536Google Scholar
  3. 3.
    Aiello, F. and Capursi, V. (2008), Using the Rasch model to assess a university service on the basis of student opinions. Applied Stochastic Models in Business and Industry, 24:459–470. doi:10.1002/asmb.730CrossRefGoogle Scholar
  4. 4.
    Andrich D (1978) A rating formulation for ordered response categories. Psychometrika 43:561–573CrossRefGoogle Scholar
  5. 5.
    Andrich D (1985) An elaboration of Guttman scaling with Rasch models for measurement. In: Tuma NB (ed) Sociological methodology. Jossey-Bass, San Francisco, pp 33–80Google Scholar
  6. 6.
    Andrich D (1988) A general form of Rasch’s extended logistic model for partial credit scoring. Appl Meas Educ I:363–378CrossRefGoogle Scholar
  7. 7.
    Balirano G, Corduas M (2006) Statistical methods for the linguistic analysis of a humorous TV sketch show. Quaderni di Statistica 8:101–124Google Scholar
  8. 8.
    Balirano G, Corduas M (2008) Detecting semeiotically-expressed humor in diasporic TV productions. HUMOR. Int J Humor Res 21:227–251CrossRefGoogle Scholar
  9. 9.
    Bartholomew DJ (1980) Factor analysis for categorical data. J R Stat Soc Ser B 42:293–321Google Scholar
  10. 10.
    Bartholomew DJ (1987) Latent variable models and factor analysis. M. Dekker, New York, NYGoogle Scholar
  11. 11.
    Bartholomew DJ, Knott M (1999) Latent variable and factor analysis, 2nd edn. Kendall’s Library of statistics, vol 7. Arnold, LondonGoogle Scholar
  12. 12.
    Bernardi L, Capursi V, Librizzi L (2004) Measurement awareness: the use of indicators between expectations and opportunities. In: Proceedings of XLII SIS meeting, Cleup, Padova, pp 315–326Google Scholar
  13. 13.
    Biggeri L (2000) Valutazione: idee, esperienze, problemi. Una sfida per gli statistici. In: Proceedings of XL SIS meeting, CS2p, Firenze, pp 31–48Google Scholar
  14. 14.
    Biggeri L, Bini M (2001) Evaluation at University and State level in Italy: need for a system of evaluation and indicators. Tertiary Educ Manage 7:149–162CrossRefGoogle Scholar
  15. 15.
    Bock RD (1972) Estimating item parameters and latent ability when responses are scored in two or more nominal categories. Psychometrika 37:29–51CrossRefGoogle Scholar
  16. 16.
    Bock RD, Moustaki I (2007) Item response theory in a general framework. In: Rao CR, Sinharay S, (eds) Psychometrics, Handbook of statistics, vol 26. Elsevier, Amsterdam, pp 469–513Google Scholar
  17. 17.
    Bradley RA, Terry ME (1952) Rank analysis of incomplete block designs. I. Method of paired comparisons. Biometrika 39:324–345Google Scholar
  18. 18.
    Cagnone S, Gardini A, Mignani S (2004). New developments of latent variable models with ordinal data. Atti della XLII Riunione Scientifica SIS, Bari, I:1–12Google Scholar
  19. 19.
    Cappelli C, D’Elia A (2004) La percezione della sinonimia: un’analisi statistica mediante modelli per ranghi. In: Prunelle G, Fairon C, Dister A (eds) Le poids des mots - Actes de JADT2004, Presses Universitaires de Louvain, Belgium, pp 229–240Google Scholar
  20. 20.
    Capursi V, Porcu M (2001) La didattica universitaria valutata dagli studenti: un indicatore basato su misure di distanza fra distribuzioni di giudizi. In: Proceedings of SIS meeting on: “Processi e Metodi Statistici di Valutazione”, Roma, pp 17–20Google Scholar
  21. 21.
    Cerchiello P, Iannario M, Piccolo D (2010) Assessing risk perception by means of ordinal models, in: Perna C. et al. editors, Mathematical and Statistical Methods for Insurance and Finance, Springer, New York, pp 65–73Google Scholar
  22. 22.
    Chiandotto B, Bertaccini B, Bini M (2007) Evaluating the quality of the University educational process: an application of the ECSI model. In: Fabris L (2006) Effectiveness of university education in Italy: employability, competences, human capital. Springer, HeidelbergGoogle Scholar
  23. 23.
    Chiandotto B, Bertaccini B (2008) SIS-ValDidat: a statistical information system for evaluating university teaching. Quaderni di Statistica 10:157–176Google Scholar
  24. 24.
    CNVSU (2002) Proposta di un insieme minimo di domande per la valutazione della didattica da parte degli studenti frequentanti, Comitato Nazionale per la Valutazione del Sistema Universitario. MIUR Doc. 9/02,
  25. 25.
    Corduas M (2008a) A testing procedure for clustering ordinal data by CUB models. In: Proceedings of Joint SFC-CLADAG 2008 meeting, ESI, Napoli, pp 245–248Google Scholar
  26. 26.
    Corduas M (2008b) A study on University students’ opinions about teaching quality: a model based approach for clustering ordinal data. DIVAGO meeting proceedings, University of Palermo 10–12 July 2008, this bookGoogle Scholar
  27. 27.
    Corduas M (2008c) A statistical procedure for clustering ordinal data. Quaderni di Statistica 10:177–189Google Scholar
  28. 28.
    Cramer JS (2001) An introduction to the logit model for economists, 2nd edn. Timberlake Consultants Ltd., LondonGoogle Scholar
  29. 29.
    De Battisti F, Nicolini G, Salini S (2005). The Rasch model to measure service quality. J Serv Mark III:58–80Google Scholar
  30. 30.
    De Battisti F, Nicolini G, Salini S (2008). Methodological overview of Rasch model and application in customer satisfaction survey data. Dipartimento di Scienze Economiche, Aziendali e Statistiche, University of Milan, Working paper n.2008-04Google Scholar
  31. 31.
    De Battisti F, Nicolini G, Salini S (2010). The Rasch model in customer satisfaction survey. Qual Technol Quant Manage 7(1):15–34Google Scholar
  32. 32.
    D’Elia A, Piccolo D (2002) Problemi e metodi statistici nei processi di valutazione della didattica. Atti della Giornata di Studio su “Valutazione della Didattica e dei Servizi nel Sistema Universitario”, Università di Salerno, Fisciano, pp 105–127Google Scholar
  33. 33.
    D’Elia A, Piccolo D (2005) A mixture model for preference data analysis. Comput Stat Data Anal 49:917–934CrossRefGoogle Scholar
  34. 34.
    D’Elia A, Piccolo D (2006). Analyzing evaluation data: modelling and testing for homogeneity. In:Zani S, Cerioli A, Riani M, Vichi M (eds) Data analysis, classification and the forward search. Springer, Berlin, pp 299–307CrossRefGoogle Scholar
  35. 35.
    Dobson AJ, Barnett AG (2008) An introduction to generalized linear models, 3rd edn. Chapman & Hall/CRC, Boca Raton, FLGoogle Scholar
  36. 36.
    Everitt BS (1984) An introduction to latent variable models. Chapman & Hall, New York, NYGoogle Scholar
  37. 37.
    Fabbris L (ed) (2006) Effectiveness of university education in Italy: Employability, competences, human capital. Springer, HeidelbergGoogle Scholar
  38. 38.
    Fischer GH (2007) Rasch models. In: Rao CR, Sinharay S (eds) Psychometrics, Handbook of statistics, vol 26. Elsevier, Amsterdam, pp 515–585Google Scholar
  39. 39.
    Franses PH, Paap R (2001) Quantitative models in marketing research. Cambridge University Press, CambridgeCrossRefGoogle Scholar
  40. 40.
    Greene WH (2000) Econometric analysis, 4th edn. Prentice Hall International, Inc., Englewood Cliffs, NJGoogle Scholar
  41. 41.
    Hensher DA, Rose JM, Greene WH (2005) Applied choice analysis. A primer. Cambridge University Press, CambridgeCrossRefGoogle Scholar
  42. 42.
    Iannario M (2007) A statistical approach for modelling Urban Audit Perception Surveys. Quaderni di Statistica 9:149–172Google Scholar
  43. 43.
    Iannario M (2010) On the identifiability of a mixture model for ordinal data, METRON, LXVIII:87–94Google Scholar
  44. 44.
    Iannario M (2008b) A class of models for ordinal variables with covariates effects. Quaderni di Statistica 10:53–72Google Scholar
  45. 45.
    Iannario M (2010) Modelling shelter choices in ordinal surveys, submittedGoogle Scholar
  46. 46.
    Iannario M, Piccolo D (2010) A new statistical model for the analysis of customer satisfaction. Qual Technol Quant Manage 7(2):149–168Google Scholar
  47. 47.
    Johnson MS, Sinharay S, Bradlow ET (2007) Hierarchical item response theory models. In: Rao CR, Sinharay S (eds) Psychometrics, Handbook of statistics, vol 26. Boston: Elsevier, pp 587–606Google Scholar
  48. 48.
    Jöreskog KG, Moustaki I (2001) Factor analysis of ordinal variables: a comparison of three approaches. Multivariate Behav Res 36:347–387CrossRefGoogle Scholar
  49. 49.
    Lewis C (2007) Selected topics in classical test theory. In: Rao CR, Sinharay S (eds) Psychometrics, Handbook of statistics, vol 26. Boston: Elsevier, pp 29–43Google Scholar
  50. 50.
    Lord FM (1980) Applications of item response theory to practical testing problems. Lawrence Erlbaum Associates, Hillsdale, NJGoogle Scholar
  51. 51.
    Lord FM, Novick MR (1968) Statistical theory of mental test scores. Addison-Wesley, Reading, MAGoogle Scholar
  52. 52.
    Luce RD (1959) Individual choice behavior. Wiley, New York, NYGoogle Scholar
  53. 53.
    King G, Tomz M, Wittenberg J (2000) Making the most of statistical analyses: improving interpretation and presentation. Am J Pol Sci 44:341–355CrossRefGoogle Scholar
  54. 54.
    Masters GN (1982) A Rasch model for partial credit scoring. Psychometrika 47:149–174CrossRefGoogle Scholar
  55. 55.
    McCullagh P (1980) Regression models for ordinal data (with discussion). J R Stat Soc Ser B 42:109–142Google Scholar
  56. 56.
    McCullagh P, Nelder JA (1989) Generalized linear models, 2nd edn. Chapman and Hall, LondonGoogle Scholar
  57. 57.
    McFadden D (1974) Conditional logit analysis of qualitative choice behavior. In: Zarembka P (ed) Frontiers of econometrics. Academic Press, New York, NY, pp 105–142Google Scholar
  58. 58.
    Mignani S, Cagnone S (2008) University formative process: quality of teaching versus performance indicators. Quaderni di Statistica 10:191–203Google Scholar
  59. 59.
    Monari P, Mignani S (2008). Modalità per la valutazione e il monitoraggio del processo formativo, dalla didattica all’apprendimento: l’esperienza dell’Universit‘a di Bologna. Meeting at Università di Napoli Federico II, 6th March 2008, available at
  60. 60.
    Moustaki I (2000) A latent variable model for ordinal data. Appl Psychol Meas 24:211–223CrossRefGoogle Scholar
  61. 61.
    Moustaki I (2003) A general class of latent variable model for ordinal manifest variables with covariates effects on the manifest and latent variables. Br J Math Stat Psychol 56:337–357CrossRefGoogle Scholar
  62. 62.
    Moustaki I, Knott M (2000) Generalized latent trait models. Psychometrika 65:391–411CrossRefGoogle Scholar
  63. 63.
    Nelder JA, Wedderburn RWM (1972) Generalized linear models. J R Stat Soc Ser A 135:370–384CrossRefGoogle Scholar
  64. 64.
    Penfield RD, Camilli G (2007) Differential item functioning and item bias. In: Rao CR, Sinharay S (eds) Psychometrics, Handbook of statistics, vol 26. Elsevier, Amsterdam, pp 125–167Google Scholar
  65. 65.
    Petrucci A, Rampichini C (2000) Indicatori statistici per la valutazione della didattica universitaria. In: Civardi M, Fabbris L (eds) Valutazione della didattica con sistemi computer-assisted. Cleup, PadovaGoogle Scholar
  66. 66.
    Piccolo D (2003) On the moments of a mixture of uniform and shifted binomial random variables. Quaderni di Statistica 5:85–104Google Scholar
  67. 67.
    Piccolo D (2006) Observed information matrix for MUB models. Quaderni di Statistica 8:33–78Google Scholar
  68. 68.
    Piccolo D, D’Elia A (2008) A new approach for modelling consumers’ preferences. Food Qual Prefer 19:247–259CrossRefGoogle Scholar
  69. 69.
    Piccolo D, Iannario M (2008) A package in R for CUB models inference, Version 1.1, available at
  70. 70.
    Rao CR, Sinharay S (eds) (2007) Psychometrics. Handbook of Statistics, vol 26. North-Holland, AmsterdamGoogle Scholar
  71. 71.
    Rasch G (1960) Probabilistic models for some intelligence and attainment tests. Nielson Lydiche, CopenhagenGoogle Scholar
  72. 72.
    Reckase MD (2007) Multidimensional item response theory. In: Rao CR, Sinharay S (eds) Psychometrics, Handbook of statistics, vol 26. Elsevier, Amsterdam, pp 607–642Google Scholar
  73. 73.
    Reeve BB (2002) An introduction to modern measurement theory. National Cancer Institute, USAGoogle Scholar
  74. 74.
    Samejima F (1969) Estimation of latent trait ability using a response pattern of graded scores. Psychometrika Monogr Suppl 17:1–139Google Scholar
  75. 75.
    Sijtsma K, Hemker BT (2000) A taxonomy of IRT Models for ordering persons and items using simple sum scores. J Educ Behav Stat 25:391–415Google Scholar
  76. 76.
    Sijtsma K, Meijer RR (2007) Non parametric item response theory and special topics. In: Rao CR, Sinharay S (eds) Psychometrics, Handbook of statistics, vol 26. Elsevier, Amsterdam, pp 719–746Google Scholar
  77. 77.
    Thissen D, Steinberg L (1986) A taxonomy of item response models. Psychometrika 51:567–577CrossRefGoogle Scholar
  78. 78.
    Thurstone LL (1927) A law of comparative judgement. Psychol Rev 34:273–286CrossRefGoogle Scholar
  79. 79.
    Train KE (2003) Discrete choice methods with simulation. Cambridge University Press, CambridgeCrossRefGoogle Scholar
  80. 80.
    von Davier M, Rost J (2007) Mixture distribution item response models. In: Rao CR, Sinharay S (eds) Psychometrics, Handbook of statistics, vol 26. Elsevier, Amsterdam, pp 643–661Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  1. 1.Dipartimento di Scienze StatisticheUniversità di Napoli Federico IINapoliItaly
  2. 2.Dipartimento di Scienze StatisticheUniversità di Napoli “Federico II”NapoliItaly

Personalised recommendations