Journal article
A Comparison of Parametric and Nonparametric Approaches to Item Analysis for Multiple-Choice Tests
Educational and psychological measurement, Vol.64(4), pp.565-587
08/01/2004
DOI: 10.1177/0013164403261760
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.
Details
- Title: Subtitle
- A Comparison of Parametric and Nonparametric Approaches to Item Analysis for Multiple-Choice Tests
- Creators
- Pui-Wa Lei - University of IowaStephen B. Dunbar - University of IowaMichael J. Kolen - University of Iowa
- Resource Type
- Journal article
- Publication Details
- Educational and psychological measurement, Vol.64(4), pp.565-587
- DOI
- 10.1177/0013164403261760
- ISSN
- 0013-1644
- eISSN
- 1552-3888
- Language
- English
- Date published
- 08/01/2004
- Academic Unit
- Psychological and Quantitative Foundations
- Record Identifier
- 9984371093802771
Metrics
1 Record Views