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相关概念视频

Friedman Two-way Analysis of Variance by Ranks01:21

Friedman Two-way Analysis of Variance by Ranks

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Friedman's Two-Way Analysis of Variance by Ranks is a nonparametric test designed to identify differences across multiple test attempts when traditional assumptions of normality and equal variances do not apply. Unlike conventional ANOVA, which requires normally distributed data with equal variances, Friedman's test is ideal for ordinal or non-normally distributed data, making it particularly useful for analyzing dependent samples, such as matched subjects over time or repeated measures...
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In order to make good decisions, we use our knowledge and our reasoning. Often, this knowledge and reasoning is sound and solid. However, sometimes, we are swayed by biases or by others manipulating a situation. For example, let’s say you and three friends wanted to rent a house and had a combined target budget of $1,600. The realtor shows you only very run-down houses for $1,600 and then shows you a very nice house for $2,000. Might you ask each person to pay more in rent to get the...
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The most basic experimental design involves two groups: the experimental group and the control group. The two groups are designed to be the same except for one difference— experimental manipulation. The experimental group gets the experimental manipulation—that is, the treatment or variable being tested—and the control group does not. Since experimental manipulation is the only difference between the experimental and control groups, we can be sure that any differences between...
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One-way ANOVA can be performed on three or more samples of unequal sizes. However, calculations get complicated when sample sizes are not always the same. So, while performing ANOVA with unequal samples size, the following equation is used:
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One-Way ANOVA can be performed on three or more samples with equal or unequal sample sizes. When one-way ANOVA is performed on two datasets with samples of equal sizes, it can be easily observed that the computed F statistic is highly sensitive to the sample mean.
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The two-way ANOVA is an extension of the one-way ANOVA. It is a statistical test performed on three or more samples categorized by two factors - a row factor and a column factor. Ronald Fischer mentioned it in 1925 in his book 'Statistical Methods for Researchers.'
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使用未知组和项目进行DIF分析.

Gabriel Wallin1, Yunxiao Chen2, Irini Moustaki3

  • 1Department of Mathematics and Statistics, Lancaster University, Umeå, Sweden.

Psychometrika
|February 22, 2024
PubMed
概括
此摘要是机器生成的。

本研究引入了一种新的统计方法,用于在未知子组或项时进行差异性项目功能 (DIF) 分析. 该方法使用隐性类和L1规范化来识别测试和调查中的公平性问题.

关键词:
差异性项目的功能.拉索 (Lasso) 是一个拉索.隐藏的 DIF 是一个潜在的 DIF.隐性类分析 隐性类分析的测量不变性.

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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
  • 统计建模 统计建模

背景情况:

  • 确保调查问卷和教育测试的公平性至关重要.
  • 差异性项目功能 (DIF) 分析通过检测子组响应差异来评估项目级测量不变性.
  • 传统的DIF方法需要预定义的参考/焦点组和点,这些并不总是可用.

研究的目的:

  • 提出一个新的统计框架,用于DIF分析,当两个比较组和项目是未知的.
  • 开发一种方法,在没有事先信息的情况下,同时识别潜在子组和DIF项目.
  • 提供一种强有力的方法,以提高评估中的公平性.

主要方法:

  • 提出了一个一般的统计框架,通过隐藏类来建模未知组.
  • 引入了项目特定的DIF参数,以捕捉不同项目的功能.
  • 使用L1规范化的估计器同时识别隐性类和DIF项目,假设少量DIF项目.
  • 为非平滑优化问题开发了一个计算效率高的预期最大化 (EM) 算法.

主要成果:

  • 拟议的L1规范化方法有效地同时识别潜在类 (未知组) 和DIF项目.
  • 模拟研究证明了该方法在各种场景中的性能.
  • 该方法已成功应用于现实世界的教育测试数据,验证了其实际实用性.

结论:

  • 开发的框架为DIF分析提供了一个强大的解决方案,在具有挑战性的环境中,缺少集团和项信息.
  • 这种方法有助于评估教育和心理测试中的测量不变性和公平性.
  • 这些发现有助于更公平,更可靠的测量仪器.