Data Integration Approaches to Longitudinal Growth Modeling

Katerina M. Marcoulides, Kevin Grimm

Research output: Contribution to journalArticlepeer-review

22 Scopus citations


Synthesizing results from multiple studies is a daunting task during which researchers must tackle a variety of challenges. The task is even more demanding when studying developmental processes longitudinally and when different instruments are used to measure constructs. Data integration methodology is an emerging field that enables researchers to pool data drawn from multiple existing studies. To date, these methods are not commonly utilized in the social and behavioral sciences, even though they can be very useful for studying various complex developmental processes. This article illustrates the use of two data integration methods, the data fusion and the parallel analysis approaches. The illustration makes use of six longitudinal studies of mathematics ability in children with a goal of examining individual changes in mathematics ability and determining differences in the trajectories based on sex and socioeconomic status. The studies vary in their assessment of mathematics ability and in the timing and number of measurement occasions. The advantages of using a data fusion approach, which can allow for the fitting of more complex growth models that might not otherwise have been possible to fit in a single data set, are emphasized. The article concludes with a discussion of the limitations and benefits of these approaches for research synthesis.

Original languageEnglish (US)
Pages (from-to)971-989
Number of pages19
JournalEducational and Psychological Measurement
Issue number6
StatePublished - Dec 1 2017


  • data fusion
  • data integration
  • longitudinal growth modeling

ASJC Scopus subject areas

  • Education
  • Developmental and Educational Psychology
  • Applied Psychology
  • Applied Mathematics


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