Collaborative Research on Latent Class Models of Measurement Error

开展调查的最关键活动之一是制定和测试调查问题。不幸的是，这一过程在很大程度上仍然是一种定性的努力，其特点是专家对问题进行审查，与少数参与者进行焦点小组讨论，以及少量密集的“认知”面谈。许多研究人员质疑这些方法在识别问题项目方面的有效性。此外，这些传统问卷预测技术产生的定性数据与数据要解决的量化标准(如信度和效度)之间存在脱节。这个项目将系统地评估一种定量方法--潜在类别分析(LCA)--用于开发和测试调查问题的潜力。该项目试图通过对现有数据进行一系列新的实验和分析，回答有关将生命周期评价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，在“黄金标准”中，被评估变量的真值是已知的。这些研究将把LCA方法得出的结论与更传统的分析结果进行比较。分析研究将LCA模型应用于现有的数据集，并使用模拟来评估LCA方法对违反其基本假设的稳健性。本项目将增进关于各种策略的基本知识，包括使用潜在班级模型来开发问卷。它将表明，即使在没有外部验证数据(如行政记录)的情况下，这些模型是否能够评估调查项目的衡量特征。该项目将比较潜在班级模型和传统问卷开发技术，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，或者两者兼而有之。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。

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.