Ten easy steps to conducting transparent, reproducible meta-analyses for infant researchers

Meta-analyses provide researchers with an overview of the body of evidence in a topic, with quantified estimates of effect sizes and the role of moderators, and weighting studies according to their precision. We provide a guide for conducting a transparent and reproducible meta-analysis in the field of developmental psychology within the framework of the MetaLab platform, in 10 steps: (1) Choose a topic for your meta-analysis, (2) Formulate your research question and specify inclusion criteria, (3) Preregister and document all stages of your meta-analysis, (4) Conduct the literature search, (5) Collect and screen records, (6) Extract data from eligible studies, (7) Read the data into analysis software and compute effect sizes, (8) Visualize your data, (9) Create meta-analytic models to assess the strength of the effect and investigate possible moderators, (10) Write up and promote your meta-analysis. Meta-analyses can inform future studies, through power calculations, by identifying robust methods and exposing research gaps. By adding a new meta-analysis to MetaLab, datasets across multiple topics of developmental psychology can be synthesized, and the dataset can be maintained as a living, community-augmented meta-analysis to which researchers add new data, allowing for a cumulative approach to evidence synthesis.