当前位置: X-MOL 学术Educ. Psychol. Meas. › 论文详情
Our official English website, www.x-mol.net, welcomes your feedback! (Note: you will need to create a separate account there.)
On the Performance of Semi- and Nonparametric Item Response Functions in Computer Adaptive Tests
Educational and Psychological Measurement ( IF 2.7 ) Pub Date : 2021-05-18 , DOI: 10.1177/00131644211014261
Carl F Falk 1 , Leah M Feuerstahler 2
Affiliation  

Large-scale assessments often use a computer adaptive test (CAT) for selection of items and for scoring respondents. Such tests often assume a parametric form for the relationship between item responses and the underlying construct. Although semi- and nonparametric response functions could be used, there is scant research on their performance in a CAT. In this work, we compare parametric response functions versus those estimated using kernel smoothing and a logistic function of a monotonic polynomial. Monotonic polynomial items can be used with traditional CAT item selection algorithms that use analytical derivatives. We compared these approaches in CAT simulations with a variety of item selection algorithms. Our simulations also varied the features of the calibration and item pool: sample size, the presence of missing data, and the percentage of nonstandard items. In general, the results support the use of semi- and nonparametric item response functions in a CAT.



中文翻译:

关于计算机自适应测试中半参数和非参数项目响应函数的性能

大规模评估通常使用计算机自适应测试 (CAT) 来选择项目和对受访者进行评分。此类测试通常采用参数形式来表示项目响应与基础结构之间的关系。尽管可以使用半参数和非参数响应函数,但关于它们在 CAT 中的性能的研究很少。在这项工作中,我们将参数响应函数与使用核平滑和单调多项式的逻辑函数估计的响应函数进行比较。单调多项式项可以与使用解析导数的传统 CAT 项选择算法一起使用。我们将 CAT 模拟中的这些方法与各种项目选择算法进行了比较。我们的模拟还改变了校准和项目池的特征:样本量、缺失数据的存在、和非标准项目的百分比。一般来说,结果支持在 CAT 中使用半参数和非参数项目响应函数。

更新日期:2021-05-18
down
wechat
bug