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Activity Number: 518
Type: Contributed
Date/Time: Wednesday, August 6, 2014 : 10:30 AM to 12:20 PM
Sponsor: Section on Statistical Education
Abstract #312532
Title: Teaching About Generalizability in Meta-Analyses
Author(s): Betsy Becker*+ and Ariel Aloe and Christopher Thompson
Companies: Florida State University and University of Northern Iowa and Florida State University
Keywords: meta-analysis ; heterogeneity ; graphics ; effect sizes ; generalizability

Testing whether effect sizes are homogeneous and estimating the degree to which they vary are common practices in meta-analysis. When effects are inconsistent across studies statements about overall effects must be very broad and made with added uncertainty. We have found that the concepts of random effects and distributions of effect sizes are challenging to less-statistical meta-analysts. We present an approach to examining and quantifying sources of heterogeneity in meta-analysis based on frameworks for classifying participant characteristics and study features across which generalizations may be made. In the medical arena the PICOS framework includes patients (P), intervention (I), comparators (C), outcomes (O) and study design (S). In social sciences the elements have been labeled methods (M), units (U), treatments (T), observing operations (O), and setting (S) or MUTOS. Indices of the explanatory power of specific aspects of these elements, along with graphical displays, can show practitioners how to assess the degree to which findings can be generalized to a target population, or across a variety of instances of important study features. Relevant software will be shown.

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