Sage Journals: Discover world-class research

Abstract

This study compares the parametric multiple-choice model and the nonparametric kernel smoothing approach to estimating option characteristic functions (OCCs) using an empirical criterion, the stability of curve estimates over occasions that represents random error. The potential utility of graphical OCCs in item analysis was illustrated with selected items. The effect of increasing the smoothing parameter on the nonparametric model and the effect of small sample on both approaches were investigated. Differences between estimated curve values for between-model within-occasion, within-model between-occasion, and between-model between-occasion were evaluated. The between-model differences were minor in relation to the within-model stabilities, and the incremental difference attributable to model was smaller than that attributable to occasion. Either model leads to the same choice in item analysis.

Keywords

item analysis multiple-choice tests polytomous item response theory parametric and nonparametric models

Get full access to this article

View all access options for this article.

References

Bock, R. D. (1972). Estimating item parameters and latent ability when the responses are scored in two or more nominal categories. Psychometrika, 37, 29-51.

Hoover, H. D. , Hieronymus, A. N. , Frisbie, D. A. , & Dunbar, S. B. (1993). Iowa Tests of Basic Skills. Chicago: Riverside.

Kelley, T. L. (1939). The selection of upper and lower groups for the validation of test items. Journal of Educational Psychology, 30, 17-24.

Mokken, K. J. (1997). Non-parametric models for dichotomous responses. In W. J. van der Linden & R. K. Hambleton (Eds.), Handbook of modern item response theory (pp. 351-367). New York: Springer-Verlag.

Ramsay, J. O. (1995). TestGraf: A program for the graphical analysis of multiple choice test and questionnaire data (Technical Report). Montreal, Quebec, Canada: McGill University.

Ramsay, J. O. (1997). A functional approach to modeling test data. In W. J. van der Linden & R. K. Hambleton (Eds.), Handbook of modern item response theory (pp. 381-394). New York: Springer-Verlag.

Samejima, F. (1979). A new family of models for the multiple choice item (Research Report No. 79-4). Knoxville: University of Tennessee, Department of Psychology.

Samejima, F. (1998). Efficient non-parametric approaches for estimating the operating characteristics of discrete item responses. Psychometrika, 63(1), 111-130.

Thissen, D. (1991). MULTILOG users guide—version 6. Chicago: Scientific Software.

10.

Thissen, D. , & Steinberg, L. (1984). A model for multiple choice items. Psychometrika, 49(4), 501-519.

11.

Thissen, D. , & Steinberg, L. (1997). A response model for multiple-choice items. In W. J. van der Linden & R. K. Hambleton (Eds.), Handbook of modern item response theory (pp. 51-65). New York: Springer-Verlag.

12.

Thissen, D. , Steinberg, L. , & Fitzpatrick, A. R. (1989). Multiple choice models: The distracters are also part of the item. Journal of Educational Measurement, 26(2), 161-176.

13.

van der Linden, W. J. , & Hambleton, R. K. (1997). Part IV: Non-parametric models introduction. In W. J. van der Linden & R. K. Hambleton (Eds.), Handbookof modern item response theory (pp. 347-349). New York: Springer-Verlag.

14.

Wainer, H. (1989). The future of item analysis. Journal of Educational Measurement, 26, 191-208.

A Comparison of Parametric and Nonparametric Approaches to Item Analysis for Multiple-Choice Tests

Abstract

Keywords

Get full access to this article

References