Characterizing Sources of Uncertainty in Item Response Theory Scale Scores
Characterizing Sources of Uncertainty in Item Response Theory Scale Scores
复制标题
描述项目反应理论量表分数中不确定性的来源
DOI：
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发表时间：
2012
影响因子：
2.7
通讯作者：
Li Cai
中科院分区：
文献类型：
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作者：
Ji Seung Yang;Mark Hansen;Li Cai
传统的项目反应理论量表得分的估计忽略了项目校准过程中遗留的不确定性，这可能导致测量标准误（SEM）的不正确估计。在这里，作者回顾了各种方法，已被应用于这个问题，并比较他们的统计方法和目标的基础上。然后，他们详细阐述了基于多重估算的方法的特殊灵活性和实用性，这种方法可以很容易地应用于混合项目类型和多个基本维度的测试。该方法获得了个体量表评分的校正估计值及其SEM。此外，这种方法使一个更完整的表征参数不确定性的影响，通过生成置信包络线（区间）的项目跟踪线，测试信息功能，条件SEM曲线，和边际可靠性系数。多重插补为基础的方法是通过人工数据集的分析说明，然后应用到一个大型教育评估的数据。还进行了模拟研究，以检查项目参数的不确定性的相对贡献，在各种条件下的分数估计的变异性。作者发现，项目参数不确定性的影响通常很小，尽管在某些情况下，项目校准带来的不确定性对分数的变化有很大的影响。当校准样本相对于要估计的项目参数的数量较小时，或者当与数据拟合的项目反应理论模型是多维的时，可能会出现这种情况。
Traditional estimators of item response theory scale scores ignore uncertainty carried over from the item calibration process, which can lead to incorrect estimates of the standard errors of measurement (SEMs). Here, the authors review a variety of approaches that have been applied to this problem and compare them on the basis of their statistical methods and goals. They then elaborate on the particular flexibility and usefulness of a multiple imputation–based approach, which can be easily applied to tests with mixed item types and multiple underlying dimensions. This proposed method obtains corrected estimates of individual scale scores, as well as their SEMs. Furthermore, this approach enables a more complete characterization of the impact of parameter uncertainty by generating confidence envelopes (intervals) for item trace lines, test information functions, conditional SEM curves, and the marginal reliability coefficient. The multiple imputation–based approach is illustrated through the analysis of an artificial data set, then applied to data from a large educational assessment. A simulation study was also conducted to examine the relative contribution of item parameter uncertainty to the variability in score estimates under various conditions. The authors found that the impact of item parameter uncertainty is generally quite small, though there are some conditions under which the uncertainty carried over from item calibration contributes substantially to variability in the scores. This may be the case when the calibration sample is small relative to the number of item parameters to be estimated or when the item response theory model fit to the data is multidimensional.
影响因子：
7
作者：
Cai, Li;Yang, Ji Seung;Hansen, Mark
通讯作者：
Hansen, Mark