应用多维项目反应理论模型探索分数减法测验的维度识别

发布时间：2018-03-07 03:06

  本文选题：MPL模型　切入点：LASSO　出处：《数学的实践与认识》2017年21期 　论文类型：期刊论文

【摘要】：多维项目反应理论(MIRT)模型是国际教育统计与心理测量学研究的热点模型.在简要介绍一种常见的MIRT模型和数理统计学热门的变量筛选方法的基础上,针对教育统计研究者常用的分数减法测验数据进行测验题目的维度识别.通过分别使用传统的因子分析法、LASSO方法和弹性网方法分析测验数据,获得了测验题目的维度识别结果,并对它们的识别准确率进行比较.研究表明使用变量筛选方法尤其是LASSO方法能够较好地识别该测验的题目维度间隶属关系,为多维测验的维度识别提供可靠的信息.
[Abstract]:Multi-dimensional item response theory (RT) model is a hot research model in international educational statistics and psychometrics. Based on a brief introduction of a common MIRT model and a popular variable screening method in mathematical statistics, the paper presents a new method for selecting variables in international educational statistics and psychometrics. Based on the commonly used score subtraction test data of educational statistics researchers, the dimension recognition results of test questions are obtained by using the traditional factor analysis method and elastic net method, respectively, by using the traditional factor analysis method and the elastic network method to analyze the test data. The results show that the variable selection method, especially the LASSO method, can better identify the subordination relationship between the subject dimensions of the test, and provide reliable information for dimension recognition of multidimensional tests.
【作者单位】： 北京林业大学理学院;
【基金】：中央高校基本科研业务费专项资金(2015ZCQ-LY-01) 国家自然科学基金青年科学基金项目(11701029);国家自然科学基金数学天元基金(11626040)
【分类号】：G40-051
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本文编号：1577651