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Rasch analysis: Estimation and tests with raschtest

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  • Jean-Benoit Hardouin

    () (Department of Biomathematics and Biostatistics, University of Nantes)

Abstract

Analyzing latent variables is becoming more and more important in several fields, such as clinical research, psychology, educational sciences, ecology, and epidemiology. The item response theory allows analyzing latent variables measured by questionnaires of items with binary or ordinal responses. The Rasch model is the best known model of this theory for binary responses. Although one can estimate the parameters of the Rasch model with the clogit or xtlogit com- mand (or with the unofficial gllamm command), these commands require special data preparation. The proposed raschtest command easily allows estimating the parameters of the Rasch model and fitting the resulting model. Copyright 2007 by StataCorp LP.

Suggested Citation

  • Jean-Benoit Hardouin, 2007. "Rasch analysis: Estimation and tests with raschtest," Stata Journal, StataCorp LP, vol. 7(1), pages 22-44, February.
  • Handle: RePEc:tsj:stataj:v:7:y:2007:i:1:p:22-44
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    References listed on IDEAS

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    1. Henk Kelderman & Carl Rijkes, 1994. "Loglinear multidimensional IRT models for polytomously scored items," Psychometrika, Springer;The Psychometric Society, vol. 59(2), pages 149-176, June.
    2. Ghosh, Malay, 1995. "Inconsistent maximum likelihood estimators for the Rasch model," Statistics & Probability Letters, Elsevier, vol. 23(2), pages 165-170, May.
    3. Ivo Molenaar, 1983. "Some improved diagnostics for failure of the Rasch model," Psychometrika, Springer;The Psychometric Society, vol. 48(1), pages 49-72, March.
    4. Rizopoulos, Dimitris, 2006. "ltm: An R Package for Latent Variable Modeling and Item Response Analysis," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 17(i05).
    5. Sophia Rabe-Hesketh & Anders Skrondal & Andrew Pickles, 2004. "GLLAMM Manual," U.C. Berkeley Division of Biostatistics Working Paper Series 1160, Berkeley Electronic Press.
    6. Arnold Wollenberg, 1982. "Two new test statistics for the rasch model," Psychometrika, Springer;The Psychometric Society, vol. 47(2), pages 123-140, June.
    7. Herbert Matschinger, 2006. "Estimating IRT models with gllamm," German Stata Users' Group Meetings 2006 03, Stata Users Group.
    8. Cees Glas, 1988. "The derivation of some tests for the rasch model from the multinomial distribution," Psychometrika, Springer;The Psychometric Society, vol. 53(4), pages 525-546, December.
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    Citations

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    Cited by:

    1. Raileanu Szeles, Monica & Fusco, Alessio, 2009. "Item response theory and the measurement of deprivation: Evidence from PSELL-3," IRISS Working Paper Series 2009-05, IRISS at CEPS/INSTEAD.
    2. Irina Kunovskaya & Brenda Cude & Natalia Alexeev, 2014. "Evaluation of a Financial Literacy Test Using Classical Test Theory and Item Response Theory," Journal of Family and Economic Issues, Springer, vol. 35(4), pages 516-531, December.
    3. Xiaohui Zheng & Sophia Rabe-Hesketh, 2007. "Estimating parameters of dichotomous and ordinal item response models with gllamm," Stata Journal, StataCorp LP, vol. 7(3), pages 313-333, September.
    4. Monica Szeles & Alessio Fusco, 2013. "Item response theory and the measurement of deprivation: evidence from Luxembourg data," Quality & Quantity: International Journal of Methodology, Springer, vol. 47(3), pages 1545-1560, April.

    More about this item

    Keywords

    raschtest; Rasch model; generalized estimating equations; conditional maximum likelihood method; marginal maximum likelihood method; An- dersen Z test; van den Wollenberg Q1 test; R1c; R1m; fit tests; item response theory; U test; splitting test; item characteristics curves;

    JEL classification:

    • Q1 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Agriculture

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