The hegemonic use of linear models in quantitative social sciences

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%).