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

机译：多选择题测试的参数分析和非参数方法比较

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摘要

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.
机译：这项研究比较了基于经验标准的参数多选模型和非参数核平滑方法来估计期权特征函数（OCC），曲线估计的稳定性代表了随机误差。所选项目说明了图形OCC在项目分析中的潜在效用。研究了增加平滑参数对非参数模型的影响以及小样本对两种方法的影响。评估了模型内事件之间，模型内事件之间以及模型间事件之间的估计曲线值之间的差异。模型间的差异相对于模型内的稳定性而言较小，并且模型所引起的增量差异要小于场合所引起的差异。两种模型都会在项目分析中产生相同的选择。

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