The study of social science phenomena using empirical data such as register data* are very often approached with an regression model.(adsbygoogle = window.adsbygoogle || []).push({});

One of the most used approaches is a linear model where the parameters is estimated with ordinary least squares. The models of

this type is often called OLS-models within this context. A common example could be how income is impacted by marital status. In this example

the dependent variable is income and the independent experimental variable is marital status. Often a set of control variables are included, such as

gender, age, race, and etc. Often the researcher try including different type of control variables and observe how well the model performs by the "regression diagnostics" such as

overall R value, significance values and so on. This type of basic model and modeling work is very common within social science research.

This methodology is introduced for social science students and very little information of the math behind the models is taught. The linear "OLS"

model is the workhorse and alternative approaches are almost not even mentioned. In several books that are used in statistics courses at Ph.D student level

in social sciences for instance calculus and differential equations are not even mentioned.

In contrast, students that study a master of science in engineering are in the first semester introduced to calculus and differential equations and the approach

to model a phenomon is fundamently different (of course the phenomena are different so different approaches are likely needed) but the whole idea of

modeling is different. When I studied engineering I did not heard a single word about regression models. Instead, the approach we were taught was to

observe the data and try if the data could be fitted by a power function, exponential function, linear function, etc. or a combination of functions.

I guess my forum post boils down to this question: Why are social science students taught regression modeling without even mentioning for instance differential equations

which could be regarded as the workhorse of many natural and technical sciences? (Some social science Ph.Ds havent heard about Fourier Series, not to mention Laplace transforms...)

*note: register data is in this context is official data recorded and prepared by national statistics offices like "Statistics Sweden" or other similar office or agency.

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# Quantitative modeling in social science vs natural science

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