...
  • 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>Educational and Psychological Measurement >Checking dimensionality in item response models with principal component analysis on standardized residuals
【24h】

Checking dimensionality in item response models with principal component analysis on standardized residuals

机译：使用标准残差的主成分分析检查项目响应模型中的维度

获取原文
获取原文并翻译 | 示例
           
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

Dimensionality is an important assumption in item response theory (IRT). Principal component analysis on standardized residuals has been used to check dimensionality, especially under the family of Rasch models. It has been suggested that an eigenvalue greater than 1.5 for the first eigenvalue signifies a violation of unidimensionality when there are 500 persons and 30 items. The cut-point of 1.5 is often used beyond this specific condition of sample size and test length. This study argues that a fixed cut-point is not applicable because the distribution of eigenvalues or their ratios depends on sample size and test length, just like other statistics. The authors conducted a series of simulations to verify this argument. They then proposed three chi-square statistics for multivariate independence to test the correlation matrix obtained from the standardized residuals. Through simulations, it was found that Steiger's statistic behaved fairly like a chi-square distribution, when its degrees of freedom were adjusted.
机译：维数是项目响应理论（IRT）中的重要假设。标准化残差的主成分分析已用于检查维数，尤其是在Rasch模型族下。已经提出，当有500个人和30个项目时，第一个特征值的特征值大于1.5表示违反一维性。超出该样本大小和测试长度的特定条件时，通常使用1.5的临界点。这项研究认为，固定的切入点不适用，因为特征值或其比率的分布取决于样本大小和测试时间，就像其他统计数据一样。作者进行了一系列模拟，以验证这一论点。然后，他们针对多元独立性提出了三个卡方统计量，以测试从标准化残差获得的相关矩阵。通过仿真发现，调整自由度后，Steiger的统计量表现得很像卡方分布。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号