在有限的样本中应用贝叶斯式检查取消公理的间隔缩放
Sanford R Student1, Wyatt S Read2
1University of Delaware School of Education, 113 Willard Hall Education Building, 16 West Main Street, Newark, DE, 19716, USA. srstu@udel.edu.
Behavior research methods
|October 7, 2025
概括
本研究介绍了一种贝叶斯方法,用于测试教育研究的拉什模型中的间隔尺度假设. 该方法有效地检测到1000个样本大小的违规行为,验证经验间隔缩放测试.
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:
- 间隔尺度假设在教育和心理学研究中很常见,但往往未经验证.
- 拉什模型经常用于隐性变量测量.
研究的目的:
- 概述和评估在拉什模型中验证间隔尺度假设的方法.
- 评估贝叶斯方法在检测间隔缩放违规性的能力.
主要方法:
- 用贝叶斯的方法来评估在拉什模型下遵守取消公理.
- 开发了引导程序,以创建违规率的零分布.
- 模拟进行了不同的样本大小 (250和1000),测试长度,难度差异和拉什模型坚持.
主要成果:
- 贝叶斯方法显示,在250个样本大小的样本中,检测间隔缩放违规的功率不足.
- 该程序在1000个样本大小中证明了一致的性能和足够的功率.
- 对间隔缩放的实证测试在适度的样本大小下是可行的.
结论:
- 建议的贝叶斯方法,加上引导,提供了一个可行的方法来测试Rash建模中的间隔尺度假设.
- 充分的样本大小 (N=1000) 对于可靠检测间隔缩放违规行为至关重要.
- 这项研究支持潜变量研究中测量尺度属性的经验验证.
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