2-062 - Conducting Longitudinal Data Analysis: Knowing What to Do and Learning How to Do It

Session Type: Professional Development Session

Integrative Statement

One of the most exciting trends in child development research over the past decades has been the increased emphasis on collecting longitudinal data. At the same time, options for analyzing longitudinal data have expanded rapidly. Indeed, the array of techniques now available to developmental scientists can sometimes be dizzying. Increasing confusion, longitudinal methodology is rife with jingle-jangle fallacies. Sometimes the same label refers to quite different models. Even more commonly, different labels refer to the same model. This state of affairs makes it difficult for developmentalists to know how to choose the most appropriate analytic techniques for testing their research hypotheses. A further challenge is acquiring the expertise to apply these techniques in a thoughtful and rigorous way. Not all universities offer graduate coursework on advanced longitudinal data analysis techniques. Even when such courses are readily available, not all researchers can afford the luxury of a semester of training before embarking on their analyses. This workshop seeks to help address these problems. Our goals are twofold. First, we survey modern longitudinal data analysis techniques, cutting though the jingle-jangle jungle of terminology to provide a guide to which modeling approaches optimally address which kinds of hypotheses. Second, we discuss a variety of options for how to learn new data analytic techniques, including both formal and informal training opportunities. Participants are encouraged to come with questions.

Sub Unit

• Professional Development Track

Leaders

• Daniel Bauer, University of North Carolina at Chapel Hill

• Patrick Curran, University of North Carolina at Chapel Hill