首页> 外文期刊>Applied Measurement in Education >A Comparison of Adjacent Categories and Cumulative Differential Step Functioning Effect Estimators
【24h】

A Comparison of Adjacent Categories and Cumulative Differential Step Functioning Effect Estimators

机译:相邻类别和累积微分阶跃功能估计量的比较

获取原文
获取原文并翻译 | 示例
       

摘要

The study of measurement invariance in polytomous items that targets individual score levels is known as differential step functioning (DSF). The analysis of DSF requires the creation of a set of dichotomizations of the item response variable. There are two primary approaches for creating the set of dichotomizations to conduct a DSF analysis: the adjacent categories approach, and the cumulative approach. To date, there is limited research on how these two approaches compare within the context of DSF, particularly as applied to a real data set. This study evaluated the results of a DSF analysis using both dichotomization schemes in order to determine if the two approaches yield similar results. The results revealed that the two approaches generally led to consistent results, particularly in the case where DSF effects were negligible. However, when significant DSF effects were present, the two approaches occasionally led to differing conclusions.View full textDownload full textRelated var addthis_config = { ui_cobrand: "Taylor & Francis Online", services_compact: "citeulike,netvibes,twitter,technorati,delicious,linkedin,facebook,stumbleupon,digg,google,more", pubid: "ra-4dff56cd6bb1830b" }; Add to shortlist Link Permalink http://dx.doi.org/10.1080/08957347.2012.660387
机译:针对个体得分水平的多项目项目的测量不变性的研究被称为微分步函数(DSF)。 DSF的分析需要创建一组项目响应变量的二分法。有两种主要方法可以创建二分法来进行DSF分析:相邻类别方法和累积方法。迄今为止,关于这两种方法如何在DSF中进行比较的研究非常有限,尤其是应用于实际数据集时。这项研究评估了使用两种二分法的DSF分析结果,以确定两种方法是否产生相似的结果。结果表明,这两种方法通常会产生一致的结果,尤其是在DSF效应可忽略的情况下。但是,当存在显着的DSF效果时,这两种方法有时会得出不同的结论。查看全文下载全文,facebook,stumbleupon,digg,google,更多”,发布号:“ ra-4dff56cd6bb1830b”};添加到候选列表链接永久链接http://dx.doi.org/10.1080/08957347.2012.660387

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