바로가기메뉴

본문 바로가기 주메뉴 바로가기

logo

  • P-ISSN1226-0657
  • E-ISSN2287-6081
  • KCI

THE DEVELOPMENT OF A ZERO-INFLATED RASCH MODEL

Journal of the Korean Society of Mathematical Education Series B: The Pure and Applied Mathematics / Journal of the Korean Society of Mathematical Education Series B: The Pure and Applied Mathematics, (P)1226-0657; (E)2287-6081
2013, v.20 no.1, pp.59-70
https://doi.org/10.7468/jksmeb.2013.20.1.59
Kim, Sungyeun
Lee, Guemin

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 <TEX>$r$</TEX>) 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 <TEX>${\lambda}$</TEX> 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

Reference

1.

Rasch models for measuremen.

2.

(2005). Modelling skewed data with many zeros: A simple approach combining ordinary and logistic regression. Environ. Ecol. Stat., 12, 45-54. 10.1007/s10651-005-6817-1.

3.

User's manual for WinGen: Windows software that generates IRT model parameters and item responses, Center for Educational Assessment Research Report 642.

4.

American Invitational Mathematics Examination.

5.

Mathematical Olympiad Challenges.

6.

(2007). Marginal maximum likelihood estimation of item response models in R. J. stat. soft., 20, 1-25.

7.

(1992). Zero Inflated Poisson regression with an application to defects in manufacturing. Technometrics, 34, 1-14. 10.2307/1269547.

8.

(2004). 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, 183-189. 10.1111/j.1600-0528.2004.00155.x.

9.

Top private school dumps too easy GCSEs.

10.

(2005). Zero tolerance ecology improving ecological inference by modelling the source of zero observations. Ecol. Lett., 8, 1235-1246. 10.1111/j.1461-0248.2005.00826.x.

11.

(1996). Rasch's multiplicative Poisson model with covariates. Psychometrika, 61, 73-92. 10.1007/BF02296959.

12.

Probabilistic models for some intelligence and attainment tests.

13.

(1998). Models for count data with many zeros . International Biometric Conference.

14.

SAS/STAT user'guide(Version 8).

15.

(1996). Modeling the abundance of rare species: Statistical models for counts with extra zeros. J. Ecol. Mode., , 297-308.

16.

A comparison of unidimensional and multidimensional Rasch models using parameter estimates and fit indices when assumption of unidimensionality is violated.

Journal of the Korean Society of Mathematical Education Series B: The Pure and Applied Mathematics