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http://dx.doi.org/10.7468/jksmeb.2013.20.1.59

THE DEVELOPMENT OF A ZERO-INFLATED RASCH MODEL  

Kim, Sungyeun (Peabody College, Vanderbilt University)
Lee, Guemin (Department of Education, Yonsei University)
Publication Information
The Pure and Applied Mathematics / v.20, no.1, 2013 , pp. 59-70 More about this Journal
Abstract
The purpose of this study was to develop a zero-inflated Rasch (ZI-Rasch) model, a combination of the Rasch model and the ZIP model. The ZI-Rasch model was considered in this study as an appropriate alternative to the Rasch model for zero-inflated data. To investigate the relative appropriateness of the ZI-Rasch model, several analyses were conducted using PROC NLMIXED procedures in SAS under various simulation conditions. Sets of criteria for model evaluations (-2LL, AIC, AICC, and BIC) and parameter estimations (RMSE, and $r$) from the ZI-Rasch model were compared with those from the Rasch model. In the data-model fit indices, regardless of the simulation conditions, the ZI-Rasch model produced better fit statistics than did the Rasch model, even when the response data were generated from the Rasch model. In terms of item parameter ${\lambda}$ estimations, the ZI-Rasch model produced estimates similar to those of the Rasch model.
Keywords
Rasch model; zero-inflated data; zero-inflated Poisson model; zero-inflated Rasch model;
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  • Reference
1 D. Andrich: Rasch models for measuremen. Newbury Park, CA: SAGE, 1988.
2 D. Fletcher, D.I. Mackenzie & E. Villouta: Modelling skewed data with many zeros: A simple approach combining ordinary and logistic regression. Environ. Ecol. Stat. 12 (2005), 45-54.   DOI
3 K.T. Han & R.K. Hambleton: User's manual for WinGen: Windows software that generates IRT model parameters and item responses. Center for Educational Assessment Research Report 642 (2007), 516-524.
4 American Invitational Mathematics Examination: In Wikipedia, The Free Encyclopedia. Retrieved from http://en.wikipedia.org/w/index.php?title=AmericanInvitationalMathematicsExamination&oldid=418555225, 2011.
5 T. Andreesscu & R. Gelca: Mathematical Olympiad Challenges. Birkhauser Boston. Retrieved from http://www.amc.maa.org, 2010.
6 M.S. Johnson: Marginal maximum likelihood estimation of item response models in R. J. stat. soft. 20 (2007), 1-25.
7 D. Lambert: Zero Inflated Poisson regression with an application to defects in manufacturing. Technometrics. 34 (1992), 1-14.   DOI   ScienceOn
8 J.D. Lewsey & W.M. Thomson: The utility of the zero-inflated Poisson and zero-inflated negative binomial models: a case study of cross-sectional and longitudinal DMF data examining the effect of socioeconomic status. Community. Dent. Oral. 32 (2004), 183-189.   DOI   ScienceOn
9 W. Mansell & P. Curtis: Top private school dumps too easy GCSEs. The GuardianE, 2009.
10 T.G. Martin & B.A. Wintle, J.R. Rhodes, P.M. Kuhnert, S.A. Field, J. Low-Choy, A.J. Tyre & H.P. Possingham: Zero tolerance ecology improving ecological inference by modelling the source of zero observations. Ecol. Lett. 8 (2005), 1235-1246.   DOI   ScienceOn
11 H. Ogasawara: Rasch's multiplicative Poisson model with covariates. Psychometrika. 61 (1996), 73-92.   DOI
12 G. Rasch: Probabilistic models for some intelligence and attainment tests. Copenhagen: Denmark Paedogogische Institut, 1960.
13 M. Ridout, C.G.B. Demetrio & J. Hind: Models for count data with many zeros. International Biometric Conference, Cape Town, 1998.
14 SAS Institute: SAS/STAT user'guide(Version 8), 2000.
15 A.H. Welsh, R.B. Cunningham, C.F. Donnelly & D.B. Lindenmayer: Modeling the abundance of rare species: Statistical models for counts with extra zeros. J. Ecol. Mode. (1996), 297-308.
16 S. Yang: A comparison of unidimensional and multidimensional Rasch models using parameter estimates and fit indices when assumption of unidimensionality is violated. Unpublished doctoral dissertation, Ohio State University, 2007.