Collaborative Research on Latent Class Models of Measurement Error

测量误差潜在类别模型的协作研究

• 批准号: 0550002
• 负责人: Frauke Kreuter
• 金额: $ 5.93万
• 依托单位: University of Maryland, College Park
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2006
• 资助国家: 美国
• 起止时间: 2006-03-01 至 2009-02-28
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0550002&HistoricalAwards=false
• 关键词: Collaborative; Research; Latent; Class; Models

One of the most crucial activities in mounting a survey is the development and testing of the survey questions. Unfortunately, this process largely remains a qualitative endeavor, one that features reviews of the questions by experts, focus group discussions with a handful of participants, and small numbers of intensive "cognitive" interviews. Many researchers have questioned the effectiveness of these methods for identifying problem items. In addition, there is a disconnect between the qualitative data produced by these conventional questionnaire pretest techniques and the quantitative standards (such as reliability and validity) that the data are meant to address. This project will systematically assess the potential of a quantitative method -- latent class analysis (LCA) -- for use in developing and testing survey questions. The project seeks to answer several specific questions about the application of LCA models as a tool for evaluating survey questions by conducting a series of new experiments and analyses of existing data. The experimental studies will compare results from the LCA models against "gold standards," where true values for the variables being assessed are known. These studies will compare the conclusions from the LCA method against those from more conventional analyses. The analytic studies will apply LCA models to existing data sets and also use simulations to assess the robustness of the LCA method to violations of its underlying assumptions.This project will advance basic knowledge about various strategies, including the use of latent class models, for questionnaire development. It will show whether these models can assess the measurement characteristics of survey items even in the absence of external validation data (such as administrative records). The project will compare the latent class models to conventional questionnaire development techniques and determine whether they can yield better questionnaires, reduced questionnaire development costs, or both compared to the traditional methods. The results of this research will be of value to the survey community, including the federal statistical agencies.

开展调查最关键的活动之一是调查问题的开发和测试。 不幸的是，这个过程在很大程度上仍然是一种定性的努力，其中包括专家对问题的审查、与少数参与者的焦点小组讨论以及少量的密集“认知”访谈。 许多研究人员质疑这些方法识别问题项目的有效性。 此外，这些传统的问卷预测试技术产生的定性数据与数据旨在解决的定量标准（例如可靠性和有效性）之间存在脱节。 该项目将系统地评估定量方法——潜在类别分析（LCA）——用于开发和测试调查问题的潜力。 该项目旨在通过进行一系列新实验和对现有数据的分析来回答有关应用 LCA 模型作为评估调查问题的工具的几个具体问题。 实验研究将 LCA 模型的结果与“黄金标准”进行比较，其中所评估变量的真实值是已知的。 这些研究将比较 LCA 方法的结论与更传统的分析的结论。 分析研究将 LCA 模型应用于现有数据集，并使用模拟来评估 LCA 方法在违反其基本假设的情况下的稳健性。该项目将推进有关各种策略的基础知识，包括使用潜在类别模型进行问卷开发。 它将显示即使在没有外部验证数据（例如行政记录）的情况下，这些模型是否可以评估调查项目的测量特征。 该项目将把潜在类别模型与传统的问卷开发技术进行比较，并确定与传统方法相比，它们是否可以产生更好的问卷，降低问卷开发成本，或两者兼而有之。 这项研究的结果将对包括联邦统计机构在内的调查界有价值。