The hegemonic use of linear models in quantitative social sciences
Saved in:
| Title: | The hegemonic use of linear models in quantitative social sciences |
|---|---|
| Authors: | Castro Torres, Andres F., Akbaritabar, Aliakbar |
| Source: | UPCommons. Portal del coneixement obert de la UPC Universitat Politècnica de Catalunya (UPC) |
| Publisher Information: | SAGE Publications, 2025. |
| Publication Year: | 2025 |
| Subject Terms: | Sociology of science, Science of science, Àrees temàtiques de la UPC::Recursos generals, Linear models, Quantitative methods |
| Description: | The literature shows that more than half of quantitative research in social sciences has relied on linear modeling techniques. Linear models focus on measuring the isolated effects of independent variables on outcomes, which often implies an additive conceptualization of the social phenomena. This particular analytically-oriented framework is different from others that conceptualize the social world in terms of configurations and processes where social factors correlate, change, vary, and evolve in tandem. Configurational and processual analysis frameworks often rely on statistical methods other than linear models (e.g., geometric data analysis techniques). We contribute an empirical analysis of the frequency of use of linear models in quantitative research across three fields of science (Social Sciences, Medicine, and Environmental Sciences) and nine Social Science and Humanities (SSHs) subdisciplines using the publicly available OpenAlex database from 1960 to 2024. We found that linear models are used prevalently from 70% to 80% across all SSHs subdisciplines, with most of them displaying the highest prevalence in the use of linear models in recent years. This prevalence is lower in Environmental Sciences (i.e., around 60%) and much higher in Medicine (i.e., above 80%). |
| Document Type: | Article |
| File Description: | application/pdf |
| Language: | English |
| ISSN: | 2070-2779 0759-1063 |
| DOI: | 10.1177/07591063251349381 |
| Rights: | CC BY NC ND URL: https://journals.sagepub.com/page/policies/text-and-data-mining-license |
| Accession Number: | edsair.doi.dedup.....4bd4d2a80e6f08bf5982ebbd9d6df5db |
| Database: | OpenAIRE |
Nájsť tento článok vo Web of Science | Abstract: | The literature shows that more than half of quantitative research in social sciences has relied on linear modeling techniques. Linear models focus on measuring the isolated effects of independent variables on outcomes, which often implies an additive conceptualization of the social phenomena. This particular analytically-oriented framework is different from others that conceptualize the social world in terms of configurations and processes where social factors correlate, change, vary, and evolve in tandem. Configurational and processual analysis frameworks often rely on statistical methods other than linear models (e.g., geometric data analysis techniques). We contribute an empirical analysis of the frequency of use of linear models in quantitative research across three fields of science (Social Sciences, Medicine, and Environmental Sciences) and nine Social Science and Humanities (SSHs) subdisciplines using the publicly available OpenAlex database from 1960 to 2024. We found that linear models are used prevalently from 70% to 80% across all SSHs subdisciplines, with most of them displaying the highest prevalence in the use of linear models in recent years. This prevalence is lower in Environmental Sciences (i.e., around 60%) and much higher in Medicine (i.e., above 80%). |
|---|---|
| ISSN: | 20702779 07591063 |
| DOI: | 10.1177/07591063251349381 |