Dear all,

It is my pleasure to announce the next Hebb lecture! Please share this notice with your colleagues and anyone who might be interested. My apologies for any cross-posting.

Integrative Data Analysis: A Promising Methodological Framework for Data Harmonization

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Dr. Patrick J. Curran (Psychology, University of North Carolina at Chapel Hill) September 15 (F), 3:30 PM Location: TBA

Abstract: Integrative data analysis (IDA) is a powerful methodological framework that allows for the fitting of statistical models to raw data that have been pooled across two or more independent samples. Advantages of IDA include increased statistical power, higher observed frequencies of rare behavior, enhanced external validity due to greater between-subject heterogeneity, extended developmental coverage through overlapping cohorts, testing novel hypotheses that cannot be evaluated within a single contributing study, and building a more cumulative and reproducible science. There are also methodological challenges associated with IDA including the need to account for sampling heterogeneity across studies, to develop commensurate measures based on items that are unique to a single study or shared across multiple studies, and to account for multiple sources of study differences as they impact hypothesis testing. In this talk I will present a general overview of IDA, explore the potential advantages and disadvantages of IDA as a novel and timely research tool, present an example of IDA as applied to internalizing symptomatology in children with and without an alcoholic parent, and describe future avenues for developing IDA as a general methodological framework within the psychological sciences.

In addition to the afternoon lecture, there will be an informal morning seminar, entitled “The Separation of Within-Person and Between-Person Change in Latent Curve Model,” with the following abstract: Many substantive applications of latent curve models are focused primarily on the estimation of between-person effects; that is, on estimating person-to-person differences in the parameters that define the underlying growth model. However, there are a variety of situations in which it may be equally (if not more) important to also consider within-person influences over time. The disaggregation of between-person and within-person effects is well understood within the multilevel model, although this topic is almost universally ignored in growth models estimated within the structural equation modeling (SEM) tradition. Indeed, significant complications are encountered within the SEM when applying the strategies that were originally developed to disaggregate these effects within the multilevel model. As a result, there are important points of divergence in precisely how these effects are estimated within the multilevel and SEM frameworks even when based on the very same data. In this talk I will explore existing methods for disaggregating within- and between person effects in the multilevel and structural equation growth model; highlight similarities and differences in how these effects are tested in both modeling frameworks; identify existing challenges that must be addressed to provide better tests of these effects in practice; and to suggest potentially profitable directions for future research and analytical developments.

This morning seminar will take place from 10:30 AM - 12:00 PM, Room 735, 2001 McGill College.

Graduate students interested in attending lunch with Dr. Curran are invited to contact the student host Sunmee Kim at sunmee.kim@mail.mcgill.camailto:sunmee.kim@mail.mcgill.ca.

Best regards, Carl Falk

---- Carl F. Falk, Assistant Professor Department of Psychology, McGill University 2001 McGill College, Room 753 Montreal, QC H3A 1G1, Canada