Ingmar Böschen | Searcharxiv

arXiv2026

Extraction of tabulated statistical results with tableParser

Tabulated content is omnipresent in scientific literature. This work presents the R package *tableParser*, designed to extract and postprocess tables from NISO-JATS-encoded XML, HTML, DOCX, and, with limitations, PDF documents. *tableParser* focuses on extracting and analyzing statistical test results reported in scientific publications. It can be used for large-scale analysis of effect sizes, reporting practices, or summarization of results, as well as for checking completeness and consistency of standard test results in unpublished documents. Documents can be processed in three decoding levels. *table2matrix()* compiles all tables into a list of character matrices with captions and footnotes. *table2text()* collapses the matrix contents into human-readable text, mimicking a screen reader. Optionally, many common codings that are reported within the table's caption and footnote can be used to decode and expand the table's content. The collapsed and decoded table content can be further processed match an ideal input for the extraction of statistical standard results with the *standardStats()* function from the *JATSdecoder* package. The output of *table2stats()* is a data frame with all detected standard results as columns and, if calculation is possible, a recalculated p-value. If desired, an automated consistency check of the reported and the coded p-values with the recalculated p-value can be initiated. *tableParser* works best on barrier-free HTML tables encoded in NISO-JATS, where captions and footnotes are clearly identifiable. By guessing the tables captions and footnotes conservatively, the processing of tables within HTML and DOCX documents is comparably robust. Technically, tables in PDFs often fail to be correctly extracted, with captions and footnotes not detectable. Therefore, a decoding of codes is not possible, which lowers *tableParser*'s decoding accuracy on PDFs.

stat.AP↗

arXiv2024

$statcheck$ is flawed by design and no valid spell checker for statistical results

The R package $statcheck$ is designed to extract statistical test results from text and check the consistency of the reported test statistics and corresponding p-values. Recently, it has also been featured as a spell checker for statistical results, aimed at improving reporting accuracy in scientific publications. In this study, I perform a check on $statcheck$ using a non-exhaustive list of 187 simple text strings with arbitrary statistical test results. These strings represent a wide range of textual representations of results including correctly manageable results, non-targeted test statistics, variable reporting styles, and common typos. Since $statcheck$'s detection heuristic is tied to a specific set of statistical test results that strictly adhere to the American Psychological Association (APA) reporting guidelines, it is unable to detect and check any reported result that even slightly deviates from this narrow style. In practice, $statcheck$ is unlikely to detect many statistical test results reported in the literature. I conclude that the capabilities and usefulness of the $statcheck$ software are very limited and that it should not be used to detect irregularities in results nor as a spell checker for statistical results. Future developments should aim to incorporate more flexible algorithms capable of handling a broader variety of reporting styles, such as those provided by $JATSdecoder$ and Large Language Models, which show promise in overcoming these limitations but they cannot replace the critical eye of a knowledgeable reader.

stat.CO↗