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A Simplified Latent Variable Structural Equation Model with Observable Variables Assessed on Ordinal Scales

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Statistical Models for Data Analysis

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Abstract

The communication is related to a wide empirical research promoted by the Università Cattolica del Sacro Cuore of Milan (UCSC) aimed at acquiring an insight into the real work possibilities of its graduates in the last seven years, as well as the appreciation and satisfaction of the firms which offered them a job position. The group of 1,264 firms which have a special connection with UCSC, regarding new job appointments, was considered and they were given a questionnaire, using web for sending and answering. The analysis of the 203 complete answers was conducted by having recourse to a structural equation model with latent variables.

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References

  • Bollen, K. A. (1989). Structural equations with latent variables. New York: Wiley.

    MATH  Google Scholar 

  • Browne, M. W., & Cudeck, R. (1993). Alternative ways of assessing model fit. In K. A. Bollen, & J. S. Long (Eds.) Testing structural equation models (pp. 136–162). Newbury Park: Sage.

    Google Scholar 

  • Chiandotto, B. (2004). Sulla misura della qualità della formazione universitaria. Studi e note di economia, 3, 27–61.

    Google Scholar 

  • Farrell, A. M. (2010). Insufficient discriminant validity: A comment on Bove, Pervan, Beatty and Shiu (2009). Journal of Business Research, 63, 324–327.

    Article  Google Scholar 

  • Fornell, C., & Larcker, D. F. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research, 18(1), 39–50.

    Article  Google Scholar 

  • Green, S. B., & Yang, Y. (2009). Reliability of summed item scores using structural equation modeling: an alternative to coefficient alpha. Psychometrika, 74(1), 155–167.

    Article  MathSciNet  MATH  Google Scholar 

  • Jöreskog, K. G. (2005). Structural equation modeling with ordinal variables using LISREL. http://www.ssicentral.com/lisrel/techdocs/ordinal.pdf. Cited 14 May 2012.

  • Jöreskog, K. G. (1994). On the estimation of polychoric correlations and their asymptotic covariance matrix. Psychometrika, 59, 381–389.

    Article  MathSciNet  MATH  Google Scholar 

  • Muthén, B. O. (1984). A general structural equation model with dichotomous, ordered, categorical, and continuous latent variable indicators. Psychometrika, 49, 115–132.

    Article  Google Scholar 

  • Raykov, T. (2002). Scale reliability evaluation with LISREL 8.50. http://www.ssicentral.com/lisrel/techdocs/reliabil.pdf. Cited 14 May 2012.

  • Raykov, T. (2002). Analytic estimation of standard error and confidence interval for scale reliability. Multivariate Behavioral Research, 37(1), 89–103.

    Article  Google Scholar 

  • Rosseel, Y. (2012). lavaan: An R package for structural equation modeling. Journal of Statistical Software, 48(2), 1–36.

    Google Scholar 

  • Sijtsma, K. (2009). On the use, the misuse, and the very limited usefulness of Cronbach’s alpha. Psychometrika, 74(1), 107–120.

    Article  MathSciNet  MATH  Google Scholar 

  • Winship, C., & Mare, R. D. (1984). Regression models with ordinal variables. American Sociological Review, 49, 512–525.

    Article  Google Scholar 

  • Zanella, A., Boari, G., Baldi, P. L., Cantaluppi, G., & Facchinetti, D. (2011). Soddisfazione delle aziende nei confronti dei collaboratori laureati all’Università Cattolica del Sacro Cuore di Milano. In B. V. Frosini (Ed.) La valutazione della ricerca e la valutazione della didattica (pp. 147–175). Milano: Vita e Pensiero.

    Google Scholar 

  • Zanella, A., & Cantaluppi, G. (2006). Some remarks on a test for assessing whether Cronbach’s coefficient α exceeds a given theoretical value. Statistica Applicata, 18, 251–275.

    Google Scholar 

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Authors and Affiliations

  1. Dipartimento di Scienze statistiche, Università Cattolica del Sacro Cuore, Milano, Italy

    Angelo Zanella, Giuseppe Boari, Andrea Bonanomi & Gabriele Cantaluppi

Authors
  1. Angelo Zanella
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  2. Giuseppe Boari
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  3. Andrea Bonanomi
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  4. Gabriele Cantaluppi
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Corresponding author

Correspondence to Giuseppe Boari .

Editor information

Editors and Affiliations

  1. Department of Economics, and Management, University of Pavia, Via San Felice 7, Pavia, 27100, Italy

    Paolo Giudici

  2. Department of Economics, and Business, University of Catania, Corso Italia 55, Catania, 95129, Italy

    Salvatore Ingrassia

  3. , Department of Statistics, University of Rome "La Sapienza", Piazzale Aldo Moro 5, Rome, 00185, Italy

    Maurizio Vichi

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© 2013 Springer International Publishing Switzerland

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Zanella, A., Boari, G., Bonanomi, A., Cantaluppi, G. (2013). A Simplified Latent Variable Structural Equation Model with Observable Variables Assessed on Ordinal Scales. In: Giudici, P., Ingrassia, S., Vichi, M. (eds) Statistical Models for Data Analysis. Studies in Classification, Data Analysis, and Knowledge Organization. Springer, Heidelberg. https://doi.org/10.1007/978-3-319-00032-9_43

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