Evaluation of the extraction of methodological study characteristics with JATSdecoder

This paper introduces and evaluates the study.character module from the JATSdecoder package which extracts several key methodological study characteristics from NISO-JATS coded scientific articles. study.character splits the text into sections and applies its heuristic-driven extraction procedures to the text of the method and result section/s. When used individually, study.character’s functions can also be applied to any textual input. An externally coded data set of 288 PDF articles serves as an indicator of study.character’s capabilities in extracting the number of sub-studies reported per article, the statistical methods applied and software solutions used. Its precision of extraction of the reported [Formula: see text] -level, power, correction procedures for multiple testing, use of interactions, definition of outlier, and mentions of statistical assumptions are evaluated by a comparison to a manually curated data set of the same collection of articles. Sensitivity, specificity, and accuracy measures are reported for each of the evaluated functions. study.character reliably extracts the methodological study characteristics targeted here from psychological research articles. Most extractions have very low false positive rates and high accuracy ([Formula: see text] ). Most non-detections are due to PDF-specific conversion errors and complex text structures, that are not yet manageable. study.character can be applied to large text resources in order to examine methodological trends over time, by journal and/or by topic. It also enables a new way of identifying study sets for meta-analyzes and systematic reviews.