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Doctoral Dissertation Research: Multidimensional Nominal Response Models in Adaptive Testing

博士论文研究：自适应测试中的多维名义响应模型

基本信息

批准号：
2119912
负责人：
Jonathan Templin
金额：
$ 1.2万
依托单位：
University of Iowa
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2021
资助国家：
美国
起止时间：
2021-09-01 至 2023-08-31
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=2119912&HistoricalAwards=false
关键词：
Doctoral Dissertation Research Multidimensional Nominal

项目摘要

This award is funded in whole or in part under the American Rescue Plan Act of 2021 (Public Law 117-2). This doctoral dissertation research project will investigate the use of multidimensional tests in computerized adaptive testing. Multidimensional tests are used across many disciplines to make critical decisions on issues such as psychological health, educational ability, and vocational suitability. Some of these tests are administered as Computerized Adaptive Tests (CATs), a mode of testing that adapts the test in real time based on the examinee's item responses. Generally, CATs are designed to work with item responses that are scored as 0 or 1, based on whether the examinee has chosen the correct response option or an incorrect response option. However, this dichotomous scoring scheme disregards information contained in incorrect response options, which can be modeled as nominal response data. Research on single-subject CATs has demonstrated that when its components are designed to work with nominal response data, the tests were shorter and more accurate than those designed for dichotomous responses. This project will examine the value of multidimensional adaptive tests that utilize item response options as nominal response data. Psychometric software will be developed and made publicly available. As a Doctoral Dissertation Research Improvement award, support is provided to enable a promising student to establish a strong, independent research career.This research project will advance the use of multidimensional tests in computerized adaptive testing by incorporating item response options into the modeling, item selection, and estimation processes that constitute the adaptive test. The project will compare the accuracy, efficiency, and security of this approach to existing procedures. Adaptive test components and results will be simulated for Multidimensional Adaptive Tests (MATs) and Cognitive Diagnostic-Computerized Adaptive Tests (CD-CATs). Both MATs and CD-CATs can be used for either summative or formative assessment purposes. However, they differ in their theoretical assumptions and thus must be investigated separately. To compare the incorporation of item response options in multidimensional adaptive testing to current dichotomous scoring procedures, item banks and item selection methods will be manipulated. Common item selection methods for dichotomous response data will be generalized for use with nominal response data to ensure comparability.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

该奖项全部或部分根据2021年美国救援计划法案（公法117-2）资助。本博士论文研究计画将探讨多维测验在电脑化适应性测验中的应用。多维测试用于许多学科，以就心理健康，教育能力和职业适应性等问题做出关键决定。其中一些测试是作为计算机化自适应测试（CAT）管理的，这是一种基于考生的项目反应在真实的时间内调整测试的测试模式。一般来说，CAT的设计是根据考生选择了正确的答案选项还是错误的答案选项，来处理得分为0或1的项目答案。然而，这种二分法评分方案忽略了包含在不正确的响应选项中的信息，这些信息可以被建模为标称响应数据。对单受试者CAT的研究表明，当其组件被设计为与标称响应数据一起工作时，测试比为二分响应设计的测试更短，更准确。本专题将探讨多维适应性测验的价值，它利用项目反应选项作为名义反应数据。将开发心理测量软件并公开提供。作为博士学位论文研究改进奖，提供支持，使有前途的学生建立一个强大的，独立的研究生涯。本研究项目将通过将项目响应选项纳入到建模，项目选择和估计过程，构成自适应测试，在计算机自适应测试中推进多维测试的使用。该项目将比较这种方法与现有程序的准确性、效率和安全性。将模拟多维自适应测试（MAT）和认知诊断计算机化自适应测试（CD-CAT）的自适应测试组件和结果。MAT和CD-CAT都可以用于总结性或形成性评估。然而，它们的理论假设不同，因此必须单独研究。为了将多维自适应测试中的项目反应选项与当前的二分法评分程序进行比较，将操纵项目库和项目选择方法。二分响应数据的通用项目选择方法将被推广用于标称响应数据，以确保可比性。该奖项反映了NSF的法定使命，并被认为值得通过使用基金会的知识价值和更广泛的影响审查标准进行评估。