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Direct linkJames Edward Hill; Catherine Harris; Andrew Clegg – Research Synthesis Methods, 2024
Data extraction is a time-consuming and resource-intensive task in the systematic review process. Natural language processing (NLP) artificial intelligence (AI) techniques have the potential to automate data extraction saving time and resources, accelerating the review process, and enhancing the quality and reliability of extracted data. In this…
Descriptors: Artificial Intelligence, Search Engines, Data Collection, Natural Language Processing
Jens H. Fünderich; Lukas J. Beinhauer; Frank Renkewitz – Research Synthesis Methods, 2024
Multi-lab projects are large scale collaborations between participating data collection sites that gather empirical evidence and (usually) analyze that evidence using meta-analyses. They are a valuable form of scientific collaboration, produce outstanding data sets and are a great resource for third-party researchers. Their data may be reanalyzed…
Descriptors: Data Collection, Cooperation, Data Analysis, Data Use
Rebecca Whittle; Joie Ensor; Miriam Hattle; Paula Dhiman; Gary S. Collins; Richard D. Riley – Research Synthesis Methods, 2024
Collecting data for an individual participant data meta-analysis (IPDMA) project can be time consuming and resource intensive and could still have insufficient power to answer the question of interest. Therefore, researchers should consider the power of their planned IPDMA before collecting IPD. Here we propose a method to estimate the power of a…
Descriptors: Data, Individual Characteristics, Participant Characteristics, Meta Analysis
