Pooling data from multiple longitudinal studies: The role of item response theory in integrative data analysis
Pooling data from multiple longitudinal studies: The role of item response theory in integrative data analysis
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DOI：
10.1037/0012-1649.44.2.365
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发表时间：
2008-03-01
影响因子：
4
通讯作者：
Zucker, Robert A.
中科院分区：
文献类型：
--
作者：
Curran, Patrick J.;Hussong, Andrea M.;Zucker, Robert A.
研究人员在长期研究发展时会遇到许多重大挑战，包括受试者流失、不同群体和发展时期测量结构的变化以及需要投入大量时间和金钱。综合数据分析是一组新兴的方法，使研究人员能够通过汇集从多个现有发展研究中提取的数据来克服单样本设计的许多挑战。这种方法具有许多优点，但这也带来了一些新的复杂性，在被发展研究人员广泛采用之前必须解决这些复杂性。在本文中，作者重点讨论了使用从多个纵向研究中提取的数据来拟合测量模型和创建量表分数的方法。作者提出了对内化症状学重复测量进行分析的结果，这些测量是从三项现有的发育研究中汇总而来的。作者描述并演示了分析中的每个步骤，并以对未来研究的潜在局限性和方向的讨论作为总结。
There are a number of significant challenges researchers encounter when studying development over an extended period of time, including subject attrition, the changing of measurement structures across groups and developmental periods, and the need to invest substantial time and money. Integrative data analysis is an emerging set of methodologies that allows researchers to overcome many of the challenges of single-sample designs through the pooling of data drawn from multiple existing developmental studies. This approach is characterized by a host of advantages, but this also introduces several new complexities that must be addressed prior to broad adoption by developmental researchers. In this article, the authors focus on methods for fitting measurement models and creating scale scores using data drawn from multiple longitudinal studies. The authors present findings from the analysis of repeated measures of internalizing symptomatology that were pooled from three existing developmental studies. The authors describe and demonstrate each step in the analysis and conclude with a discussion of potential limitations and directions for future research.