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A comparison of Kernel Equating to the Test Characteristic Curve method.

机译：核等效法与测试特征曲线法的比较。

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摘要

This study examines the accuracy of Kernel Equating (KE) and the Test Characteristic Curve (TCC) method under different conditions. Through a Monte Carlo simulation study simulees' equated scores on form Y are compared to parametric true scores based on item and person parameters. The study uses the non-equivalent anchor test (NEAT) equating design. For KE both the chain equating (CE) and post-stratification equating (PSE) techniques are examined. The 2 parameter logistic (2PL) model is used for TCC equating and for calculating parametric true scores. The effects of four independent variables (sample size, test length, the percent of anchor items, and average factor loading) on equating method accuracy are investigated.;Results suggest that both equating methods perform fairly well under the varying levels of independent variables. Test length, the percent of anchor items, average factor loading, and sample size do affect which method is more accurate. Root mean square difference (RMSD) values indicate that there is an interaction between these four variables. For example, on the long tests (75 items) KE is always more accurate when the average loading is .62 regardless of the percent of anchor items or sample size. When the average loading is .50 then TCC equating is more accurate when used with 30% anchor items and for the large sample size condition. For the other .50 loading conditions and 75-item test length KE is either more accurate or the two methods are indistinguishable.;A graphical examination of the average mean difference between parametric true scores and each equating method reveals that the accuracy of each equating method varies along the score range of form X. Specifically, KE produces more accurate expected scores on Y for individuals who score in the middle to upper-middle range of scores on X. This corresponds to the range in which the majority of individuals fell. In contrast, TCC equating is more consistently accurate across the entire range of scores. Overall, this study suggests that KE tends to perform well in comparison to TCC equating.;Key Words: Kernel Equating, Test Characteristic Curve Equating
机译：本研究考察了在不同条件下的核等效法（KE）和测试特征曲线（TCC）方法的准确性。通过蒙特卡洛模拟研究，将基于形式和人的参数将模型Y上的方程式等效得分与参数化真实得分进行比较。该研究使用非等效锚定试验（NEAT）等效设计。对于KE，链等同（CE）和分层后等同（PSE）技术都得到了检查。 2参数逻辑（2PL）模型用于TCC等效和计算参数真实分数。研究了四个独立变量（样本量，测试长度，锚项目的百分比和平均因子负荷）对等值法准确性的影响。结果表明，两种等值法在不同变量下均表现良好。测试长度，锚项目的百分比，平均因子负荷和样本大小都会影响哪种方法更准确。均方根差（RMSD）值表明这四个变量之间存在相互作用。例如，在长期测试（75个项目）中，当平均载荷为0.62时，无论锚定项目的百分比或样本量如何，KE总是更准确。当平均载荷为0.50时，当与30％的锚定项一起使用时以及在大样本量的情况下，TCC等效更为准确。对于其他.50加载条件和75个项目的测试长度KE更为准确，或者这两种方法无法区分。;对参数真实得分与每种等值方法之间的平均均值差异的图形检查表明，每种等值方法的准确性沿形式X的分数范围变化。具体来说，对于在X分数上中到上范围得分的个人，KE在Y上产生更准确的期望分数。这对应于大多数人跌倒的范围。相反，在整个分数范围内，TCC等同更准确。总体而言，这项研究表明，与TCC等效相比，KE的性能往往更好。关键词：核等式，测试特性曲线等

著录项

  • 作者

    Norman Dvorak, Rebecca L.;

  • 作者单位

    The University of Nebraska - Lincoln.;

  • 授予单位 The University of Nebraska - Lincoln.;
  • 学科 Education Tests and Measurements.
  • 学位 Ph.D.
  • 年度 2009
  • 页码 134 p.
  • 总页数 134
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类 教育;
  • 关键词

  • 入库时间 2022-08-17 11:38:26

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