首页> 美国卫生研究院文献>other >A Two-Dimensional Multiple-Choice Model Accounting for Omissions
【2h】

A Two-Dimensional Multiple-Choice Model Accounting for Omissions

机译:用于遗漏的二维多重选择模型

代理获取
本网站仅为用户提供外文OA文献查询和代理获取服务,本网站没有原文。下单后我们将采用程序或人工为您竭诚获取高质量的原文,但由于OA文献来源多样且变更频繁,仍可能出现获取不到、文献不完整或与标题不符等情况,如果获取不到我们将提供退款服务。请知悉。

摘要

This paper presents a new two-dimensional Multiple-Choice Model accounting for Omissions (MCMO). Based on Thissen and Steinberg multiple-choice models, the MCMO defines omitted responses as the result of the respondent not knowing the correct answer and deciding to omit rather than to guess given a latent propensity to omit. Firstly, using a Monte Carlo simulation, the accuracy of the parameters estimated from data with different sample sizes (500, 1,000, and 2,000 subjects), test lengths (20, 40, and 80 items) and percentages of omissions (5, 10, and 15%) were investigated. Later, the appropriateness of the MCMO to the Trends in International Mathematics and Science Study (TIMSS) Advanced 2015 mathematics and physics multiple-choice items was analyzed and compared with the Holman and Glas' Between-item Multi-dimensional IRT model (B-MIRT) and with the three-parameter logistic (3PL) model with omissions treated as incorrect responses. The results of the simulation study showed a good recovery of scale and position parameters. Pseudo-guessing parameters (d) were less accurate, but this inaccuracy did not seem to have an important effect on the estimation of abilities. The precision of the propensity to omit strongly depended on the ability values (the higher the ability, the worse the estimate of the propensity to omit). In the empirical study, the empirical reliability for ability estimates was high in both physics and mathematics. As in the simulation study, the estimates of the propensity to omit were less reliable and their precision varied with ability. Regarding the absolute item fit, the MCMO fitted the data better than the other models. Also, the MCMO offered significant increments in convergent validity between scores from multiple-choice and constructed-response items, with an increase of around 0.02 to 0.04 in R2 in comparison with the two other methods. Finally, the high correlation between the country means of the propensity to omit in mathematics and physics suggests that (1) the propensity to omit is somehow affected by the country of residence of the examinees, and (2) the propensity to omit is independent of the test contents.
机译:本文提出了一个新的二维多重选择模型(MCMO)。基于Thissen和Steinberg多项选择模型,MCMO定义了省略的答复,因为被调查者不知道正确的答案,并决定忽略而不是猜测给定了潜在的忽略倾向。首先,使用蒙特卡洛模拟,从不同样本量(500、1,000和2,000个受试者),测试长度(20、40和80个项目)和遗漏百分比(5、10,和15%)进行了调查。之后,分析了MCMO是否适合2015年国际数学和科学研究(TIMSS)趋势的数学和物理多项选择题,并将其与Holman和Glas的项目间多维IRT模型(B-MIRT)进行了比较。 )和三参数对数(3PL)模型,将遗漏视为错误响应。仿真研究的结果表明比例尺和位置参数的恢复良好。伪猜测参数(d)的准确性较差,但是这种不准确性似乎并未对能力估计产生重要影响。忽略倾向的精度很大程度上取决于能力值(能力越高,忽略倾向的估计就越差)。在实证研究中,能力估计的实证可靠性在物理和数学上都很高。正如在模拟研究中一样,忽略倾向的估计不太可靠,其精度随能力而变化。关于绝对项目适合度,MCMO比其他模型更适合数据。而且,MCMO在多项选择题和构造反应项得分之间的收敛效度显着增加,与其他两种方法相比,R 2 增加了约0.02至0.04。最后,数学和物理学中国家遗漏倾向的均值之间的高度相关性表明:(1)遗漏倾向在某种程度上受考生居住国的影响,(2)遗漏倾向与测试内容。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
代理获取

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