The Validity of Markov Latent Class Analysis for Evaluating Measurement Errors in Complex Panel Surveys

马尔可夫潜在类分析(MLCA)包括一大类模型和技术，用于分析容易发生错误分类的分类纵向数据。一个重要的应用领域是探索小组调查中的数据质量问题。由于MLCA不依赖黄金标准或重复测量，它几乎可以应用于任何面板调查。对于数据质量评估，MLCA已被用于比较访谈模式和备选问卷设计，估计测量偏差，调查错误分类的原因，并调查许多其他测量误差问题。尽管MLCA在调查工作中有许多潜在的应用，但由于缺乏使MLC模型适应复杂调查数据的实用指导，MLCA在调查方法学家中并未得到广泛使用。本项目将：(1)在分析各种条件下的复杂调查数据时，当一个或多个MLCA假设失败时，评估模型偏差的大小；(2)确定和评估当前诊断和修复MLC模型故障和错误说明的策略；(3)通过制定改进的MLC模型故障和错误说明的诊断和修复策略，解决当前方法的局限性，特别是在复杂调查的应用中；(4)对真实的小组调查数据应用最有效的诊断和补救方法，以演示实际应用中可能出现的一系列建模问题以及如何有效地处理这些问题。作为这些方法应用的一部分，将分析来自几个国家小组调查的至少10年的数据，以确定关键国家统计数据的测量误差的时间趋势。这项研究对包括社会科学、流行病学、临床研究、教育测试和心理学在内的所有科学分支的MLCA具有重要的影响。该项目的影响将至少从四个方面感受到。这项研究对于复杂的调查应用具有特别的相关性，因为在这项研究中，重点是对选择的概率不相等且受到无响应和测量误差影响的集群相关数据进行建模。它对那些数据可能受到测量误差不同影响的弱势群体和少数群体也有重要影响。此外，误差趋势的评估将为三个重要的联邦统计项目提供有关当前和历史测量误差水平的重要信息。最后，将阐述和检验有关测量误差和调查参与度之间关系的理论。作为支持调查和统计方法研究的联合活动的一部分，该项目得到了方法学、测量和统计方案和一个联邦统计机构联盟的支持。

Markov latent class analysis (MLCA) comprises a broad class of models and techniques for analyzing categorical longitudinal data subject to misclassification. An important application area is exploring data quality issues in panel surveys. Because MLCA does not rely on gold standard or replicate measurements, it can be applied to virtually any panel survey. For data quality evaluations, MLCA has been used to compare interview modes and alternative questionnaire designs, estimate measurement bias, investigate the causes of misclassification, and investigate many other measurement error issues. Despite its many potential applications in survey work, MLCA has not enjoyed widespread use among survey methodologists because practical guidance on fitting MLC models to complex survey data is lacking. This project will: (1) evaluate the magnitude of the model bias when one or more MLCA assumptions fail when analyzing complex survey data under a wide range of conditions; (2) identify and evaluate the current strategies for diagnosing and repairing MLC model failure and misspecification; (3) address the limitations of current methods by developing improved strategies for diagnosing and repairing MLC model failure and misspecification, particularly in applications to complex surveys; and (4) apply the most effective diagnostic and remedial approaches to real panel survey data to demonstrate the range of modeling issues that can arise in practical applications as well as how to deal with them effectively. As part of the application of these approaches, at least 10 years of data from several national panel surveys will be analyzed to identify temporal trends in measurement error for key national statistics.This research has important implications for MLCA in all branches of science where classification error is an issue, including social science, epidemiology, clinical research, educational testing, and psychology. The project's impact will be felt in at least four ways. The research has particular relevance for complex survey applications because of the emphasis in this research on modeling cluster-correlated data selected with unequal probabilities and subject to nonresponse and measurement error. It also has important implications for disadvantaged and minority populations whose data may be differentially affected by measurement error. In addition, the evaluation of error trends will provide important information on current and historical levels of measurement error for three important federal statistical programs. Finally, theories regarding the relationship between measurement error and survey participation will be formulated and tested. The project is supported by the Methodology, Measurement, and Statistics Program and a consortium of federal statistical agencies as part of a joint activity to support research on survey and statistical methodology.