Meta-Scraping: Two Technological Approaches to Support Meta-analyses

Abstract

Meta-analysis is a principled statistical approach for summarizing quantitative information reported across studies within a research domain of interest. Although the results of meta-analyses can be highly informative for taking a broad conceptual and empirical approach to an existing body of research literature, the process of collecting and coding the data for a meta-analysis is often a labor-intensive effort fraught with the potential for human error and idiosyncrasy, as researchers typically spend weeks poring over journal articles, technical reports, book chapters and other materials provided by researchers in order to retrieve key data elements that are then manually coded into some form of a spreadsheet for subsequent analyses (e.g., descriptive statistics, effect sizes, reliability estimates, demographics, study conditions). In this poster, we identify two technological solutions to support the process of collecting data for a meta-analysis.