Abstract
Researchers from most academic disciplines use statistical tools to analyze experimental and observational data. Unfortunately there is evidence that a disturbing number of peer-reviewed articles that use statistical methods use them incorrectly. Prior research has found this to be a problem in journals dedicated to a variety of fields including medicine, psychology, business and education. Currently, the identification of common statistical errors in journal articles involves a manual and thus very labor-intensive process. As a result, only a small number of articles in a few disciplines from a limited set of journals have been assessed. In this paper we propose developing text mining models to partially automate the identification of common statistical errors in journal articles. We use the term literature mining to refer to the application of text mining tools to academic journals. Once developed, such models can be used to audit the existing journal-based body of knowledge and rapidly compile lists of articles that likely contain a particular statistical error. Looking forward, the models can also be used by editors and reviewers to screen papers prior to publication. The identification of new research opportunities is also discussed.
Recommended Citation
Ryker, Randy and Viosca, Chuck
(2015)
"Literature Mining For Common Statistical Errors,"
Journal of Business, Industry, and Economics: Vol. 20, Article 4.
Available at:
https://roar.una.edu/jobie/vol20/iss1/4