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Viechtbauer, Wolfgang; López-López, José Antonio – Research Synthesis Methods, 2022
Heterogeneity is commonplace in meta-analysis. When heterogeneity is found, researchers often aim to identify predictors that account for at least part of such heterogeneity by using mixed-effects meta-regression models. Another potentially relevant goal is to focus on the amount of heterogeneity as a function of one or more predictors, but this…
Descriptors: Meta Analysis, Models, Predictor Variables, Computation
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Mikkel Helding Vembye; James Eric Pustejovsky; Therese Deocampo Pigott – Research Synthesis Methods, 2024
Sample size and statistical power are important factors to consider when planning a research synthesis. Power analysis methods have been developed for fixed effect or random effects models, but until recently these methods were limited to simple data structures with a single, independent effect per study. Recent work has provided power…
Descriptors: Sample Size, Robustness (Statistics), Effect Size, Social Science Research
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Fanshawe, Thomas R.; Shaw, Luke F.; Spence, Graeme T. – Research Synthesis Methods, 2017
Introduction: Previous studies suggest that many systematic reviews contain meta-analyses that display temporal trends, such as the first study's result being more extreme than later studies' or a drift in the pooled estimate. We assessed the extent and characteristics of temporal trends using all Cochrane intervention reports published 2008-2012.…
Descriptors: Meta Analysis, Intervention, Databases, Medical Research
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Wu, Meng-Jia; Becker, Betsy Jane – Research Synthesis Methods, 2013
Regression methods are widely used by researchers in many fields, yet methods for synthesizing regression results are scarce. This study proposes using a factored likelihood method, originally developed to handle missing data, to appropriately synthesize regression models involving different predictors. This method uses the correlations reported…
Descriptors: Regression (Statistics), Correlation, Research Methodology, Accuracy