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Quality & Quantity

, Volume 47, Issue 3, pp 1545–1560 | Cite as

Item response theory and the measurement of deprivation: evidence from Luxembourg data

  • Monica Raileanu SzelesEmail author
  • Alessio Fusco
Article

Abstract

Item response theory (IRT) has recently been proposed as a framework to measure deprivation. It allows a latent measure of deprivation to be derived from a set of dichotomous items indicating deprivation, and the determinants of deprivation to be analysed. We investigate further the use of IRT models in the field of deprivation measurement. First, the paper emphasises the importance of item selection and the Mokken Scale Procedure is applied to select the items to be included in the scale of deprivation. Second, we apply the one- and the two-parameter probit IRT models for dichotomous items to two different sets of items, in order to highlight different empirical results. Finally, we introduce a graphical tool, the Item Characteristic Curve (ICC), and analyse the determinants of deprivation in Luxembourg. The empirical illustration is based on the fourth wave of the “Liewen zu Lëtzebuerg” Luxembourg socioeconomic panel (PSELL-3).

Keywords

Item response theory Deprivation Latent trait Mokken scale PSELL3 

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References

  1. Alkire S.: Valuing Freedoms. Sen’s Capability Approach and Poverty Reduction. Oxford University Press, Oxford (2001)Google Scholar
  2. Atkinson, A.B.: Poverty. In: Durlauf, S.N., Blume, L. (eds.) The New Palgrave Dictionary of Economics. Palgrave Macmillan (2008)Google Scholar
  3. Atkinson A.B, Marlier E.: Income and Living Conditions in Europe. OPOCE, Luxembourg (2010)Google Scholar
  4. Baker F.B.: Item Response Theory: Parameter Estimation Techniques. Marcel Dekker, New York (1992)Google Scholar
  5. Cappellari L., Jenkins S.P.: Summarizing Multiple Deprivation Indicators. In: Jenkins, S.P, Micklewright, J. (eds) Inequality and Poverty Re-examined, pp. 166–184. Oxford University Press, Oxford (2007)Google Scholar
  6. Chiappero Martinetti E.: A multidimensional assessment of well-being based on Sen’s functioning approach. Riv. Int. Sci. Soc. 108, 207–231 (2000)Google Scholar
  7. De Boeck P., Wilson M.: Explanatory Item Response Models: A Generalized Linear and Nonlinear Approach. Springer, New York (2004)Google Scholar
  8. Dickes, P.: Pauvreté et conditions d’existence. Théories, modèles et mesures. Document PSELL. 8, Walferdange CEPS/INSTEAD, Luxemburg (1989)Google Scholar
  9. Fusco A.: La pauvreté, un concept multidimensionnel. L’Harmattan, Paris (2007)Google Scholar
  10. Fusco, A., Dickes, P.: The Rasch model and multidimensional poverty measurement. In: Kakwani, N., Silber, J. (eds.) Quantitative Approaches to Multidimensional Poverty Measurement, pp. 49–62. Houndmills Palgrave Macmillan (2008)Google Scholar
  11. Gailly B., Hausman P.: Désavantages relatifs a une mesure objective de la pauvreté. In: Sarpellon, G. (eds) Understanding Poverty, pp. 192–216. Franco Angeli, Milan (1984)Google Scholar
  12. Guio, A.C.: Material Deprivation in the EU. Statistics in Focus, Population and Social Conditions. Living Conditions and Welfare 21, Eurostat (2005)Google Scholar
  13. Guio, A.C., Fusco, A., Marlier, E.: An EU approach to Material Deprivation EU-SILC and Eurobarometer data. IRISS Working Paper/CEPS/INSTEAD 19, Luxembourg (2009)Google Scholar
  14. Hardouin J.B.: Rasch analysis: estimation and tests with raschtest. Stata J. 7, 1–23 (2007)Google Scholar
  15. Hardouin, J.B.: Non Parametric Item Response Theory Using Stata. mimeo (2009)Google Scholar
  16. Hardouin, J.B.: Manual for the SAS macro-programs LoevH and MSP and the Stata modules LoevH and MSP. http://anaqol.org/biblio/msp.pdf (2005)
  17. Hemker B.T., Sijtsma K., Molenaar I.W.: Selection of unidimensional scales from a multidimensional item bank in the polytomous Mokken IRT model. Appl. Psych. Meas. 19, 337–352 (1995)CrossRefGoogle Scholar
  18. Jenkins, S., Micklewright, J.: Inequality and Poverty Re-examined. Oxford University Press, Oxford (2007)Google Scholar
  19. Kakwani N., Silber J.: Many Dimensions of Poverty. Palgrave Macmillan, Houndmills, London (2008a)CrossRefGoogle Scholar
  20. Kakwani N., Silber J.: Quantitative Approaches To Multidimensional Poverty Measurement. Palgrave Macmillan, Houndmills, London (2008b)CrossRefGoogle Scholar
  21. Kuklys W.: Amartya Sen’s Capability Approach: Theoretical Insights and Empirical Applications. Springer Verlag, Berlin (2005)Google Scholar
  22. Lancaster G., Green M.: Deprivation, ill-health and the ecological fallacy. J. R. Stat. Soc. A 165(Part 2), 263–278 (2002a)CrossRefGoogle Scholar
  23. Lancaster G., Green M.: Latent variable techniques for categorical data. Stat. Comput. 12, 153–161 (2002b)CrossRefGoogle Scholar
  24. Lawley, D.N.: On problems connected with item selection and test construction. In: Proc. Roy. Soc., Edinburgh, 61A, pp. 273–287 (1943)Google Scholar
  25. Layte R., Nolan B., Maître B., Whelan C.T: Explaining deprivation in the European Union. Acta Sociol. 44, 105–122 (2001)CrossRefGoogle Scholar
  26. Lord F.M.: A Theory of Test Scores. Psychometric Monogr. 7, New York (1952)Google Scholar
  27. Lord F.M.: Applications of Item Response Theory to Practical Testing Problems. Lawrence Erlbaum Hillsdale, NJ (1980)Google Scholar
  28. Minton H., Lewis L., Terman M.: Pioneer in Psychological Testing. New York University Press, New York (1988)Google Scholar
  29. Moisio P.: Latent class application to the multidimensional measurement of poverty. Qual. Quan. 38(6), 703–717 (2004)CrossRefGoogle Scholar
  30. Molenaar I.W.: Some Background for Item response Theory and the Rasch Model. In: Fischer, G.H., Molenaar, I.W. (eds) Rasch Models, Recent Developments and Applications, pp. 7–15. Springer-Verlag, New York (1995)Google Scholar
  31. Mokken R.J.: A Theory and Procedure of Scale Analysis. Mouton, The Hague (1971)CrossRefGoogle Scholar
  32. Nolan B., Whelan C.T.: Resources, Deprivation and Poverty. Clarendon Press, Oxford (1996)Google Scholar
  33. Ostini, R., Nering, M.: Polytomous item response theory models. Quantitative Applications in the Social Sciences 07-144. SAGE Publications Series (2006)Google Scholar
  34. Pérez-Mayo J.: Identifying deprivation profiles in Spain: a new approach Applied Economics. Taylor Francis J. 37(8), 943–955 (2005)Google Scholar
  35. Raileanu Szeles M., Tache I.: The forms and determinants of social exclusion in the European Union: the case of Luxemburg. Int. Adv. Econ. Res. 14, 369–380 (2008)CrossRefGoogle Scholar
  36. Rasch G.: An individualistic approach to item analysis. In: Lazarsfeld, P., Henry, N. (eds) Reading in Mathematical Social Science, MIT Press, Cambridge (1966)Google Scholar
  37. Ringen S.: Direct and indirect measures of poverty. J. Soc. Pol. 17, 351–366 (1988)CrossRefGoogle Scholar
  38. Sen A.K.: Issues in the measurement of poverty. Scand. J. Econ. 81, 285–307 (1979)CrossRefGoogle Scholar
  39. Skrondal, A., Rabe-Hesketh, S.: Generalized Latent Variable and Modeling: Multilevel, Longitudinal and Structural Equation Models. Chapman & Hall/CRC, Boca Raton (2004)Google Scholar
  40. Stewart F., Ruggeri C., Laderchi C., Saith R.: Introduction: Four Approaches to Defining and Measuring Poverty. In: Stewart, F., Saith, R., Harriss-White, B. (eds) Defining Poverty in the Developing World, Palgrave MacMillan, Houndmills (2007)Google Scholar
  41. Townsend P.: Deprivation. J. Soc. Pol. 16, 125–146 (1987)CrossRefGoogle Scholar
  42. Van der Linden W.J., Hambleton R.K.: Item response theory: Brief history, common models, and extensions. In: Vander Linden, W.J., Hambleton, R.K. (eds) Handbook of Modern Item Response Theory, pp. 1–28. Springer, New York (1997)Google Scholar
  43. Whelan, B.: Non-monetary indicators of poverty. In: Berghman, J., Cantillon, B. (eds.) The European Face of Social Security: Essays in Honour of Herman Deleeck, pp. 24–42. Avebury (1993)Google Scholar
  44. Wilson M.: On choosing a model for measuring. Meth. Psych. Res. 8, 1–22 (2003)Google Scholar
  45. Zheng X., Rabe-Hesketh S.: Estimating parameters of dichotomous and ordinal item response models with gllamm. Stata J. 7, 313–333 (2007)Google Scholar

Copyright information

© Springer Science+Business Media B.V. 2011

Authors and Affiliations

  1. 1.Faculty of Economic SciencesTransylvania University of Brasov, Romania and CEPS/INSTEADEsch sur AlzetteLuxembourg
  2. 2.CEPS/INSTEAD, LuxembourgEsch sur AlzetteLuxembourg

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