![]() ![]() Compared to manual coding, an automated identification of study characteristics is very time and cost-efficient. Research on scientific practice can benefit greatly from NLP techniques. Thus, unlike in the aforementioned large multipurpose NLP libraries, no further programming effort is required to perform specific extraction. This extraction is implemented using expert-driven heuristics. In contrast, the JATSdecoder package 11 focuses on metadata and study feature extraction (in the context of the NISO-JATS format). This often involves the use of statistical models and machine learning. Well-known NLP libraries such as NLTK 9 or spaCy 10 provide users with a variety of programs for linguistic evaluation of natural language. ![]() In addition to rudimentary computer commands on textual input (regular expressions), there are also many software programs and toolkits that provide model-based methods of natural language processing (NLP). It facilitates extraction and unification tasks that cannot be done by hand when the analyzed text corpus becomes large. It is an interdisciplinary field that draws on data mining, machine learning, natural language processing, statistics, and more 8. Text mining is the process of discovering and capturing knowledge or useful patterns from a large amount of unstructured textual data 8. 7 analyzed the use of statistical methods and analysis software solutions in 288 articles (36 articles each from 8 journals), all from a publication period of about one year.Ī technology that is suitable for analyzing large amounts of text and helps to overcome the problem of small samples in the analysis of scientific research practice is text mining. The selectivity of these samples therefore severely limits the generalizability of the findings to a wider scope. Most of these studies used manually coded data of a limited number of articles, journals, topics or time interval. Numerous studies have investigated the use and development of statistical techniques in scientific research practice 1, 2, 3, 4, 5, 6, 7. One aspect to consider is the ever-increasing number of scientific publications coming out each year. With new methods and standards, the way research is planned, conducted and presented changes over time and represents an interesting field of research. This applies not only to the study design, but also to the choice of statistical methods and their settings. There are also changing standards set by journal editors and the community. In scientific research practice, many individual decisions can be made that affect the scientific quality of a study. It also enables a new way of identifying study sets for meta-analyzes and systematic reviews. study.character can be applied to large text resources in order to examine methodological trends over time, by journal and/or by topic. Most non-detections are due to PDF-specific conversion errors and complex text structures, that are not yet manageable. Most extractions have very low false positive rates and high accuracy ( \(\ge 0.9\)). study.character reliably extracts the methodological study characteristics targeted here from psychological research articles. Sensitivity, specificity, and accuracy measures are reported for each of the evaluated functions. Its precision of extraction of the reported \(\alpha \)-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. 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. When used individually, study.character’s functions can also be applied to any textual input. study.character splits the text into sections and applies its heuristic-driven extraction procedures to the text of the method and result section/s. ![]() 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. ![]()
0 Comments
Leave a Reply. |
AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |