Exploring Diversity through Machine Learning: A Case for the Use of Decision Trees in Social Science Research

Autor/inn/en	Srour, F. Jordan; Karkoulian, Silva
Titel	Exploring Diversity through Machine Learning: A Case for the Use of Decision Trees in Social Science Research
Quelle	In: International Journal of Social Research Methodology, 25 (2022) 6, S.725-740 (16 Seiten) PDF als Volltext Verfügbarkeit
Zusatzinformation	ORCID (Srour, F. Jordan)
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	1364-5579
DOI	10.1080/13645579.2021.1933064
Schlagwörter	Diversity; Decision Making; Artificial Intelligence; Correlation; Prediction; Age Differences; Gender Differences; Religion; Geographic Regions; Measurement Techniques; Models; Social Science Research; Programming Languages; Foreign Countries; Reliability; Validity; Teamwork; College Faculty; Computer Software; Teacher Attitudes; Self Disclosure (Individuals); Online Surveys; Teacher Characteristics; Group Dynamics; Lebanon + Suchen Sie Ihr Suchwort? Decision-making; Entscheidungsfindung; Künstliche Intelligenz; Korrelation; Vorhersage; Age; Difference; Age difference; Altersunterschied; Geschlechterkonflikt; Messtechnik; Analogiemodell; Social scientific research; Sozialwissenschaftliche Forschung; Ausland; Reliabilität; Gültigkeit; Fakultät; Lehrerverhalten; Gruppendynamik; Libanon
Abstract	The literature provides multiple measures of diversity along a single demographic dimension, but when it comes to studying the interaction of multiple diversity types (e.g. age, gender, and race), the field of useable measures diminishes. We present the use of decision trees as a machine learning technique to automatically identify the interactions across diversity types to predict different levels of a dependent variable. In order to demonstrate the power of decision trees, we use five types of surface-level diversity (age, gender, education level, religion, and region of origin) measured via the standardized Blau index as independent variables and knowledge sharing as the dependent variable. The results of our decision tree approach relative to linear regression show that decision trees serve as a powerful tool to identify key demographic faultlines without "a priori" specification of a model structure. (As Provided).
Anmerkungen	Routledge. Available from: Taylor & Francis, Ltd. 530 Walnut Street Suite 850, Philadelphia, PA 19106. Tel: 800-354-1420; Tel: 215-625-8900; Fax: 215-207-0050; Web site: http://www.tandf.co.uk/journals
Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2024/1/01