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

马尔可夫潜在类别分析用于评估复杂面板调查中测量误差的有效性

• 批准号: 1229222
• 负责人: Paul Biemer
• 金额: $ 17.5万
• 依托单位: Research Triangle Institute
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2012
• 资助国家: 美国
• 起止时间: 2012-10-01 至 2015-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1229222&HistoricalAwards=false
• 关键词: Validity; Markov; Latent; Class; Analysis

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.

马尔可夫潜类分析（MLCA）包括一大类模型和技术，用于分析容易被错误分类的分类纵向数据。一个重要的应用领域是探讨小组调查中的数据质量问题。因为MLCA不依赖于黄金标准或重复测量，它可以应用于几乎任何小组调查。对于数据质量评估，MLCA被用于比较访谈模式和替代问卷设计，估计测量偏差，调查误分类的原因，以及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，但由于缺乏将MLC模型拟合到复杂调查数据的实际指导，MLCA尚未在调查方法学家中广泛使用。本项目将：(1)在广泛的条件下分析复杂的调查数据时，当一个或多个MLCA假设失败时，评估模型偏差的大小；(2)识别和评估当前诊断和修复MLC模型故障和错误规范的策略；(3)通过开发改进的策略来诊断和修复MLC模型故障和错误规范，特别是在复杂调查中的应用，解决当前方法的局限性；(4)将最有效的诊断和补救方法应用于真实的面板调查数据，以展示在实际应用中可能出现的建模问题的范围以及如何有效地处理它们。作为应用这些方法的一部分，将分析几个国家小组调查至少10年的数据，以确定主要国家统计数据测量误差的时间趋势。本研究对分类错误存在的所有科学分支（包括社会科学、流行病学、临床研究、教育测试和心理学）的MLCA具有重要意义。该项目的影响至少体现在四个方面。该研究特别适用于复杂的调查应用，因为该研究强调了以不等概率选择的集群相关数据的建模，并受到无响应和测量误差的影响。它还对弱势群体和少数群体具有重要意义，这些群体的数据可能受到测量误差的不同影响。此外，对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，对测量误差与调查参与之间关系的理论进行阐述和检验。该项目由方法、测量和统计项目和联邦统计机构联盟支持，作为支持调查和统计方法研究的联合活动的一部分。