A Comparison of Parametric and Nonparametric Approaches to Item Analysis for Multiple-Choice Tests
| Autor: | Stephen B. Dunbar, Michael J. Kolen, Pui Wa Lei |
|---|---|
| Rok vydání: | 2004 |
| Předmět: |
Item analysis
Applied Mathematics 05 social sciences Nonparametric statistics 050401 social sciences methods 050301 education Stability (probability) Education Nonparametric regression 0504 sociology Statistics Item response theory Developmental and Educational Psychology Kernel smoother Econometrics 0503 education Applied Psychology Smoothing Parametric statistics Mathematics |
| Zdroj: | Educational and Psychological Measurement. 64:565-587 |
| ISSN: | 1552-3888 0013-1644 |
| DOI: | 10.1177/0013164403261760 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|