Exploring diversity through machine learning: a case for the use of decision trees in social science research
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 te...
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| المؤلف الرئيسي: | |
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| مؤلفون آخرون: | |
| التنسيق: | article |
| منشور في: |
2021
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| الوصول للمادة أونلاين: | http://hdl.handle.net/10725/17291 https://doi.org/10.1080/13645579.2021.1933064 http://libraries.lau.edu.lb/research/laur/terms-of-use/articles.php https://www.tandfonline.com/doi/full/10.1080/13645579.2021.1933064 |
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| _version_ | 1864513474336391168 |
|---|---|
| author | Srour, F. Jordan |
| author2 | Karkoulian, Silva |
| author2_role | author |
| author_facet | Srour, F. Jordan Karkoulian, Silva |
| author_role | author |
| dc.creator.none.fl_str_mv | Srour, F. Jordan Karkoulian, Silva |
| dc.date.none.fl_str_mv | 2021-06-05 2022 2025-09-25T13:23:37Z 2025-09-25T13:23:37Z |
| dc.identifier.none.fl_str_mv | 1364-5579 http://hdl.handle.net/10725/17291 https://doi.org/10.1080/13645579.2021.1933064 Srour, F. J., & Karkoulian, S. (2022). Exploring diversity through machine learning: a case for the use of decision trees in social science research. International Journal of Social Research Methodology, 25(6), 725-740. http://libraries.lau.edu.lb/research/laur/terms-of-use/articles.php https://www.tandfonline.com/doi/full/10.1080/13645579.2021.1933064 |
| dc.language.none.fl_str_mv | en |
| dc.relation.none.fl_str_mv | International Journal of Social Research Methodology |
| dc.rights.*.fl_str_mv | info:eu-repo/semantics/openAccess |
| dc.title.none.fl_str_mv | Exploring diversity through machine learning: a case for the use of decision trees in social science research |
| dc.type.none.fl_str_mv | Article info:eu-repo/semantics/publishedVersion info:eu-repo/semantics/article |
| description | 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. |
| eu_rights_str_mv | openAccess |
| format | article |
| id | LAURepo_09f9208f45a44e997d262acf961a9feb |
| identifier_str_mv | 1364-5579 Srour, F. J., & Karkoulian, S. (2022). Exploring diversity through machine learning: a case for the use of decision trees in social science research. International Journal of Social Research Methodology, 25(6), 725-740. |
| language_invalid_str_mv | en |
| network_acronym_str | LAURepo |
| network_name_str | Lebanese American University repository |
| oai_identifier_str | oai:laur.lau.edu.lb:10725/17291 |
| publishDate | 2021 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| spelling | Exploring diversity through machine learning: a case for the use of decision trees in social science researchSrour, F. JordanKarkoulian, SilvaThe 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.Published2025-09-25T13:23:37Z2025-09-25T13:23:37Z20222021-06-05Articleinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/article1364-5579http://hdl.handle.net/10725/17291https://doi.org/10.1080/13645579.2021.1933064Srour, F. J., & Karkoulian, S. (2022). Exploring diversity through machine learning: a case for the use of decision trees in social science research. International Journal of Social Research Methodology, 25(6), 725-740.http://libraries.lau.edu.lb/research/laur/terms-of-use/articles.phphttps://www.tandfonline.com/doi/full/10.1080/13645579.2021.1933064enInternational Journal of Social Research Methodologyinfo:eu-repo/semantics/openAccessoai:laur.lau.edu.lb:10725/172912025-09-25T13:23:37Z |
| spellingShingle | Exploring diversity through machine learning: a case for the use of decision trees in social science research Srour, F. Jordan |
| status_str | publishedVersion |
| title | Exploring diversity through machine learning: a case for the use of decision trees in social science research |
| title_full | Exploring diversity through machine learning: a case for the use of decision trees in social science research |
| title_fullStr | Exploring diversity through machine learning: a case for the use of decision trees in social science research |
| title_full_unstemmed | Exploring diversity through machine learning: a case for the use of decision trees in social science research |
| title_short | Exploring diversity through machine learning: a case for the use of decision trees in social science research |
| title_sort | Exploring diversity through machine learning: a case for the use of decision trees in social science research |
| url | http://hdl.handle.net/10725/17291 https://doi.org/10.1080/13645579.2021.1933064 http://libraries.lau.edu.lb/research/laur/terms-of-use/articles.php https://www.tandfonline.com/doi/full/10.1080/13645579.2021.1933064 |