Social science training typically involves statistics as equivalent to "quantitative methods", particularly statistical modelling but also some material about data quality and exploratory analysis. But there's a range of other mathematical topics that are useful for thinking about social science issues. I am in the early stages of developing a module that would be available to undergraduate students in any social science degree (sociology, criminology, business, anthropology, geography... but potentially more generally) that introduces them to these not-statistics topics.
When I look for textbooks or other sources of ideas, I keep on getting "Quantitative Reasoning" GRE preparation materials, but that's not what I'm after. Here's a preliminary list of topics:
- Spatial data
- Standardisation (comparing mortality rates across populations, price indices)
- Population dynamics (evolution, cultural transmission)
- Decision Making under uncertainty (utility, regret, multiple criteria?)
- Social Dilemmas and Game Theory
- Machine learning (likely just k-means clustering or similar)
- Network analysis
- Thinking about data (ethics, meaning of data)
- Logic and fuzzy sets
Obviously they are not going to really learn much of any of these topics and probably several won't make the cut but I have listed them to give you an idea of the intent of the module. It would be entirely case study driven, with some starting question for each topic.
If anyone has any experience developing a module along these lines, I would really appreciate suggestions for topics and/or textbooks.
