Pooling Data From Multiple Longitudinal Studies: The Role of Item Response Theory in Integrative Data Analysis

Patrick J. Curran, Andrea M. Hussong, Li Cai, Wenjing Huang, Laurie Chassin, Kenneth J. Sher, Robert A. Zucker

Research output: Contribution to journalArticlepeer-review

115 Scopus citations


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.

Original languageEnglish (US)
Pages (from-to)365-380
Number of pages16
JournalDevelopmental psychology
Issue number2
StatePublished - Mar 2008


  • growth modeling
  • integrative data analysis
  • item response theory
  • pooling data

ASJC Scopus subject areas

  • Demography
  • Developmental and Educational Psychology
  • Life-span and Life-course Studies


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