Combining Data Sets through Integrative Data Analysis: A Practical Discussion of the What, Why, and How

• In Event: 1-005 - Data Integration

Abstract

Integrative data analysis (IDA) is a technique that allows researchers to pool raw data from existing studies in order to fit one model to the aggregated data (e.g., Curran et al., 2008; Curran & Hussong, 2009). IDA can help researchers maximize the use of previously collected data and address research questions that may not be sufficiently answered by a single sample. Despite the usefulness of this technique, IDA is underutilized, possibly because there are limited examples of its application in developmental science. In this workshop, we will (1) introduce attendees to the concept and general procedures of IDA; (2) illustrate specifically how IDA can be applied through examples of our own published manuscripts utilizing IDAs, and (3) guide participants through the process of developing a preliminary plan for their own IDA. We will emphasize the pragmatic issues we encountered during the IDA process and the ways that we addressed those issues. We will also provide time for participants to discuss their interests with other attendees and identify potential collaborators. The workshop leader, Sara K. Johnson, is the Principal Investigator of an IDA-based from the John Templeton Foundation (Co-Principal Investigator: Kristina S. Callina), and the co-leader Michelle B. Weiner is the Postdoctoral Fellow on the project.

Authors

• Sara K Johnson, Tufts University
  Presenting Author

• Michelle B Weiner, Tufts University
  Presenting Author