%0 Journal Article %A Koile, Ezequiel %A Cristia, Alejandrina %+ Linguistic and Cultural Evolution, Max Planck Institute for the Science of Human History, Max Planck Society %T Toward cumulative cognitive science: a comparison of meta-analysis, mega-analysis, and hybrid approaches : %G eng %U https://hdl.handle.net/21.11116/0000-000A-370E-A %R 10.1162/opmi_a_00048 %7 2021-10-01 %D 2021 %8 01.10.2021 %* Review method: peer-reviewed %X There is increasing interest in cumulative approaches to science, in which instead of analyzing the results of individual papers separately, we integrate information qualitatively or quantitatively. One such approach is meta-analysis, which has over 50 years of literature supporting its usefulness, and is becoming more common in cognitive science. However, changes in technical possibilities by the widespread use of Python and R make it easier to fit more complex models, and even simulate missing data. Here we recommend the use of mega-analyses (based on the aggregation of data sets collected by independent researchers) and hybrid meta- mega-analytic approaches, for cases where raw data is available for some studies. We illustrate the three approaches using a rich test-retest data set of infants’ speech processing as well as synthetic data. We discuss advantages and disadvantages of the three approaches from the viewpoint of a cognitive scientists contemplating their use, and limitations of this article, to be addressed in future work. %K cumulative science, open science, meta-analyses, mega-analyses, data simulation, fixed effects, random effects %Z Introduction - Study Case: Reliability of Infant Speech Perception Measures - Alternatives to Meta-analyses: Mega-analyses, IPD Meta-analyses, and Hybrid Approaches The present study - A Brief Primer on Test-Retest Infant Speech Perception - Modeling Experiment 1: Natural data Experiment 2: Synthetic data General discussion - Potential Limitations Conclusion %J Open mind : discoveries in cognitive science %O Open mind %V 5 %& 154 %P 154 - 173 %] 00048 %I The MIT Press %C Cambridge, Mass. %@ 2470-2986