CAREER: Accounting for Measurement Bias and Error in Integrative Data Analysis

这个CAREER项目将为综合数据分析（IDA）开发一个强大而有效的框架。数据共享活动标志着过去二十年来科学领域最重要的趋势之一，在社会和行为科学领域建立了许多丰富的公共数据集。利用不同的数据集，研究人员可以获得更强的统计能力，并提出更广泛的研究问题。使用IDA，研究人员可以汇集多个数据集，以获得更准确的统计结果。然而，在社会和行为科学中，通常使用不同的工具来测量同一个概念，这对IDA提出了重大挑战。该项目将开发统计方法和开放源码软件，以应对这一挑战。将开发的工具将使研究人员能够协调和调整数据来源之间的不兼容性和偏见。在教育活动方面，调查员将（a）为应用研究人员举办培训讲习班，以提高IDA的熟练程度，（B）开设一门关于心理测量的开放式课程，以增加公众知识，及（c）制定培训课程，以提高高层次人士的数据素养，该研究项目将通过提出一个强大而有效的潜在变量框架来加强IDA的基础设施这减少了用于组合多个数据集的劳动和计算时间。尽管潜力巨大，但现有的国际开发协会办法并没有提供一个统一的框架来解决主要的计量问题。这些问题包括在研究中使用不相容的测量方法，测量分数的不可靠性，以及违反不同亚组和时间点的测量不变性。新的数据协调算法将是第一个强大的优化程序，同时考虑到测量不兼容性和测量偏差。该项目还将开发和验证一个统计程序，两阶段随机效应路径分析，用于分析正确解释测量误差的协调分数。将为这些新的统计工具开发开放源码和可访问的软件，并将制定方法指南，告知这些方法何时运作良好，何时应谨慎行事。该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

This CAREER project will develop a robust and efficient framework for integrative data analysis (IDA). Data sharing activities mark one of the most significant trends in science for the past two decades, establishing many rich public data sets in the social and behavioral sciences. Leveraging diverse data sets allow researchers to obtain stronger statistical power and ask broader research questions. Using IDA, researchers can pool multiple data sets to obtain more accurate statistical results. However, in the social and behavioral sciences, different instruments usually are used to measure the same concept, which presents a significant challenge for IDA. This project will develop statistical methods and open-source software to address this challenge. The tools to be developed will allow researchers to harmonize and adjust incompatibility and biases across data sources. In terms of educational activities, the investigator will (a) develop training workshops for applied researchers to increase proficiency in IDA, (b) create an open-access course on psychological measurement to increase public knowledge, and (c) develop a training curriculum to increase the data literacy of high-school educators for underrepresented students.This research project will enhance IDA infrastructure by proposing a robust and efficient latent variable framework that reduces labor and computational time for combining multiple data sets. Despite immense potential, existing IDA approaches do not provide a unifying framework to address major measurement issues. These issues include the use of incompatible measures across studies, the unreliability of measured scores, and the violation of measurement invariance across different subgroups and time points. The new data harmonization algorithm to be developed will be the first robust optimization procedure that simultaneously accounts for measurement incompatibility and measurement bias. The project also will develop and validate a statistical procedure, two-stage random-effect path analysis, for analyzing harmonized scores that correctly accounts for measurement error. Open-source and accessible software will be developed for these new statistical tools, and methodological guidelines will be created that inform when these methods perform well and when cautions should be applied. The methodological tools and guidelines to be developed will allow researchers in the social and behavioral sciences to answer new research questions in many areas.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.