Computerized Adaptive Testing for Survey Research

该项目将推进计算机自适应测试（CAT），作为减少静电电池的一种替代方法。社会科学家了解个人特征和信仰的一种常见方法是让人们回答关于同一主题的许多密切相关的问题。这些大型的多项目电池被用来测量人格特征、心理疾病、政治意识形态、个人价值观、知识水平等等。然而，由于考虑到受访者的负担或长期调查的成本，民意研究人员经常选择不使用这些广受尊重的多项目电池。标准的解决方案是选择可用项目的一个子集，然后将其管理给所有受访者。然而，众所周知，这种减少静电的电池会增加偏置和降低测量精度。CAT改善了与静态缩减尺度相关的测量精度和准确度的下降。该项目以教育测试和心理测量学领域的现有工作为基础，开发软件和在线网络服务，采用和调整CAT，以满足民意研究人员的具体需求。本研究项目将CAT算法应用于调查研究领域，并针对民意研究人员的需求提供实践资源和理论指导。CAT算法动态适应测量潜在结构，同时最小化每个应答者必须回答的问题数量。这种方法使用关于每个问题项的质量的信息，通过选择随后的问题来响应个人的先前答案，这些问题将以最大的精度和最少的问题数量将它们放置在潜在维度上。也就是说，它根据每个被调查者之前的回答来选择问题，这样研究人员就可以尽可能多地了解被调查者的观点。该项目将为开发CAT电池和在调查中管理它们提供必要的基本软件基础设施。这包括完成和托管一个基于云的网络服务，很容易集成到调查管理软件中。该项目还将为调查研究人员可能面临的自适应电池问题提供解决方案，包括适当处理高水平的测量误差和诊断有缺陷的问题项校准。最后，该项目将收集数据，建立自适应电池，测量社会科学中的重要概念，特别强调人格清单，使用大型方便样本，全国代表性样本和现有数据集的组合。这些校准的分发将使研究人员能够使用CAT技术，而无需增加大量预测试的成本。

This project will advance computerized adaptive testing (CAT) as an alternative approach to static-reduced batteries. A common way that social scientists learn about individuals' traits and beliefs is to have people answer many closely related questions about the same topic. These large multi-item batteries are used to measure constructs like personality traits, psychological illnesses, political ideology, personal values, levels of knowledge, and more. Public opinion researchers, however, often choose not to use these widely respected multi-item batteries because of concerns regarding respondent burden or the costs of long surveys. The standard solution is to select a subset of the available items, which then are administered to all respondents. Such static-reduced batteries are well known to increase bias and lower measurement precision, however. CAT ameliorates much of the decreased measurement precision and accuracy associated with static-reduced scales. The project builds on existing work in the fields of educational testing and psychometrics to develop software and an online webservice that adopts and adjusts CAT for the specific needs of public opinion researchers.This research project will apply CAT algorithms to the domain of survey research and provide practical resources and theoretical guidance specific to the needs of public opinion researchers. CAT algorithms adapt dynamically to measure latent constructs while minimizing the number of questions each respondent must answer. This approach uses information about the qualities of each question item to respond to individuals' prior answers by choosing subsequent questions that will place them on the latent dimension with maximum precision and a minimum number of questions. That is, it chooses questions for each respondent based on their previous responses so that researchers can learn as much as possible about the opinions of a respondent. The project will provide basic software infrastructure necessary for developing CAT batteries and administering them on surveys. This includes the completion and hosting of a cloud-based webservice easily integrated into survey-administration software. The project also will provide solutions to problems likely to confront survey researchers fielding adaptive batteries including appropriately handling high levels of measurement error and diagnosing flawed question-item calibrations. Finally, the project will collect data to build adaptive batteries measuring important concepts in the social sciences, with a special emphasis on personality inventories, using a combination of large convenience samples, a nationally representative sample, and existing datasets. The distribution of these calibrations will allow researchers to use CAT techniques without the added cost of extensive pre-testing.