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A multilevel multinomial logit model for the analysis of graduates’ skills

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Abstract

The main goal of the paper is to specify a suitable multivariate multilevel model for polytomous responses with a non-ignorable missing data mechanism in order to determine the factors which influence the way of acquisition of the skills of the graduates and to evaluate the degree programmes on the basis of the adequacy of the skills they give to their graduates. The application is based on data gathered by a telephone survey conducted, about two years after the degree, on the graduates of year 2000 of the University of Florence. A multilevel multinomial logit model for the response of interest is fitted simultaneously with a multilevel logit model for the selection mechanism by means of maximum likelihood with adaptive Gaussian quadrature. In the application the multilevel structure has a crucial role, while selection bias results negligible. The analysis of the empirical Bayes residuals allows to detect some extreme degree programmes to be further inspected.

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References

  • Bellio R, Gori E (2003) Impact evaluation of job training programmes: Selection bias in multilevel models. J Appl Stat 30:893–907

    Article  MATH  MathSciNet  Google Scholar 

  • Copas BJ, Li HG (1997) Inference for non-random samples (with discussion). J Roy Stat Soc B 59:55–95

    Article  MATH  MathSciNet  Google Scholar 

  • Grilli L, Rampichini C (2003) Alternative specifications of multivariate multilevel probit ordinal response models. J Educat Behav Stat 28:31–44

    Article  Google Scholar 

  • Grilli L, Rampichini C (2005) Selection bias in random intercept models. Multilevel Modell Newslett 17:9–15

    Google Scholar 

  • Heckman JJ (1979) Sample selection bias as a specificaton error. Econometrica 47:153–161

    Article  MATH  MathSciNet  Google Scholar 

  • Hedeker D (2003) A mixed-effects multinomial logistic regression model. Stat Medi 22:1433–1446

    Article  Google Scholar 

  • Little RJA, Rubin DB (2002) Statistical analysis with missing data, 2nd edn. Wiley, New York

    MATH  Google Scholar 

  • McCullagh P, Nelder JA (1989) Generalized linear models, 2nd edn. Chapman & Hall/CRC, London

    Google Scholar 

  • McFadden D (1973) Conditional logit analysis of qualitative choice behavior. In: Frontiers in econometrics. Academic Press, New York

  • Rabe-Hesketh S, Skrondal A, Pickles A (2004) gllamm manual. U.C. Berkeley Division of Biostatistics Working Paper Series, p 160

  • Skrondal A, Rabe-Hesketh S (2003) Multilevel logistic regression for polytomous data and rankings. Psychometrika 68:267–287

    Article  MathSciNet  Google Scholar 

  • Train K (2003) Discrete choice methods with simulation. Cambridge University Press, New York

    MATH  Google Scholar 

Download references

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Authors and Affiliations

  1. Department of Statistics “G. Parenti”, University of Florence, Viale Morgagni 59, 50134, Florence, Italy

    Leonardo Grilli & Carla Rampichini

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  1. Leonardo Grilli
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  2. Carla Rampichini
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Correspondence to Leonardo Grilli.

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Grilli, L., Rampichini, C. A multilevel multinomial logit model for the analysis of graduates’ skills. Stat. Meth. & Appl. 16, 381–393 (2007). https://doi.org/10.1007/s10260-006-0039-z

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  • DOI: https://doi.org/10.1007/s10260-006-0039-z

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