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The Accuracy of Accuracy Estimates for Single Form Dichotomous Classification Exams.

机译:单一形式二分分类考试的准确性估计值的准确性。

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摘要

The use of exams for classification purposes has become prevalent across many fields including professional assessment for employment screening and standards based testing in educational settings. Classification exams assign individuals to performance groups based on the comparison of their observed test scores to a pre-selected criterion (e.g. masters vs. nonmasters in dichotomous classification scenarios). The successful use of exams for classification purposes assumes at least minimal levels of accuracy of these classifications. Classification accuracy is an index that reflects the rate of correct classification of individuals into the same category which contains their true ability score. Traditional methods estimate classification accuracy via methods which assume that true scores follow a four-parameter beta-binomial distribution. Recent research suggests that Item Response Theory may be a preferable alternative framework for estimating examinees' true scores and may return more accurate classifications based on these scores. Researchers hypothesized that test length, the location of the cut score, the distribution of items, and the distribution of examinee ability would impact the recovery of accurate estimates of classification accuracy. The current simulation study manipulated these factors to assess their potential influence on classification accuracy. Observed classification as masters vs. nonmasters, true classification accuracy, estimated classification accuracy, BIAS, and RMSE were analyzed. In addition, Analysis of Variance tests were conducted to determine whether an interrelationship existed between levels of the four manipulated factors. Results showed small values of estimated classification accuracy and increased BIAS in accuracy estimates with few items, mismatched distributions of item difficulty and examinee ability, and extreme cut scores. A significant four-way interaction between manipulated variables was observed. In additional to interpretations of these findings and explanation of potential causes for the recovered values, recommendations that inform practice and avenues of future research are provided.
机译:将考试用于分类目的已在许多领域中变得普遍,包括职业筛选的专业评估和教育环境中基于标准的测试。分类考试是根据观察到的测试分数与预先选择的标准(例如,在二分类分类方案中的硕士与非硕士)进行比较,将个人分配到绩效组。成功地将考试用于分类目的的前提是,至少要使这些分类的准确性达到最低水平。分类准确度是反映个人正确分类到同一类别的比率的指数,其中包含他们的真实能力得分。传统方法通过假设真实分数遵循四参数β-二项分布的方法来估计分类准确性。最近的研究表明,项目反应理论可能是估计考生真实分数的更可取的替代框架,并且可能会基于这些分数返回更准确的分类。研究人员认为,考试时间,分数的位置,项目的分布以及考生能力的分布会影响分类准确性的准确估计的恢复。当前的模拟研究操纵了这些因素,以评估它们对分类准确性的潜在影响。观察分类为母版还是非母版,分析了真实的分类精度,估计的分类精度,BIAS和RMSE。此外,还进行了方差分析测试来确定四个可操作因素的水平之间是否存在相互关系。结果显示,估计的分类准确度值较小,且几乎没有项目的准确性估计中的BIAS增加,项目难度和考生能力的分配不匹配以及极端得分。观察到操纵变量之间存在显着的四向相互作用。除了对这些发现的解释和对回收价值的潜在原因的解释之外,还提供了一些建议,为实践和未来研究的途径提供了信息。

著录项

  • 作者

    Kunze, Katie.;

  • 作者单位

    Arizona State University.;

  • 授予单位 Arizona State University.;
  • 学科 Education Tests and Measurements.;Psychology Psychometrics.;Education Educational Psychology.
  • 学位 M.A.
  • 年度 2013
  • 页码 104 p.
  • 总页数 104
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

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