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相关概念视频

Test for Homogeneity01:23

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The goodness–of–fit test can be used to decide whether a population fits a given distribution, but it will not suffice to decide whether two populations follow the same unknown distribution. A different test, called the test for homogeneity, can be used to conclude whether two populations have the same distribution. To calculate the test statistic for a test for homogeneity, follow the same procedure as with the test of independence. The hypotheses for the test for homogeneity can...
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Friedman Two-way Analysis of Variance by Ranks01:21

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The goodness-of-fit test is a type of hypothesis test which determines whether the data "fits" a particular distribution. For example, one may suspect that some anonymous data may fit a binomial distribution. A chi-square test (meaning the distribution for the hypothesis test is chi-square) can be used to determine if there is a fit. The null and alternative hypotheses may be written in sentences or stated as equations or inequalities. The test statistic for a goodness-of-fit test is given as...
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Updated: Jul 27, 2025

Author Spotlight: Validation of SICOLE-R for Assessing Cognitive and Reading Skills in Spanish-Speaking Children and Its Role in Personalized Education
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使用通用后勤回归方法来检测在认知诊断测试中的多组功能差异物品.

Xiaojian Sun1,2, Shimeng Wang3, Lei Guo4

  • 1School of Mathematics and Statistics, Southwest University, Chongqing, China.

Applied psychological measurement
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PubMed
概括
此摘要是机器生成的。

不同的项目功能 (DIF) 损害了测试有效性. 本研究引入了通用后勤回归 (GLR) 方法来检测认知诊断评估中的多个组中的DIF,优于传统方法.

关键词:
认知诊断评估是一种认知诊断评估.差异性项目的功能.一般化后勤回归的一般化后勤回归.多个群组多个群组.

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科学领域:

  • 心理测量 心理测量 心理测量
  • 教育测量教育的测量
  • 认知诊断评估 (CDA) 是一种认知诊断评估.

背景情况:

  • 不同项目功能 (DIF) 可以破坏评估的有效性和公平性.
  • 现有的DIF检测方法主要集中在两组比较上,限制了它们在复杂的多组场景中的应用.
  • 在多个组的认知诊断评估 (CDA) 中检测DIF仍然是一个研究不足的领域.

研究的目的:

  • 研究通用后勤回归 (GLR) 方法在多组CDA设置中检测DIF的有效性.
  • 为了比较基于GLR的沃尔德测试 (GLR-Wald) 和基于GLR的概率比测试 (GLR-LRT) 与普通的沃尔德测试的性能.
  • 评估使用估计属性配置文件作为基于GLR的DIF检测中的匹配标准的影响.

主要方法:

  • 使用通用后勤回归 (GLR) 来检测DIF项目.
  • 在GLR框架内使用估计属性配置文件作为匹配标准.
  • 进行了模拟研究,比较GLR-Wald,GLR-LRT和普通的Wald测试,并补充了真实数据分析.

主要成果:

  • 在大多数模拟条件下,GLR-Wald和GLR-LRT都显示出与普通Wald测试相比,对I型错误率有更好的控制.
  • GLR方法产生了更高的经验拒绝率,表明在检测DIF项目的能力比普通的沃尔德测试更大.
  • 使用估计属性配置文件作为匹配标准,导致GLR-Wald和GLR-LRT的类型I错误率和经验拒绝率相似.

结论:

  • GLR方法,特别是估计的属性配置文件,提供了一个强大的方法来检测多组CDA中的DIF.
  • 基于GLR的测试 (Wald和LRT) 与多组环境中的普通Wald测试相比,为DIF检测提供了更好的准确性和功率.
  • 这些发现支持GLR方法的应用,以提高多种群体认知诊断评估的有效性和公平性.