Periodic Reporting for period 4 - QUALITY (Qualitative and Quantitative Social Science: Unifying the Logic of Causal Inference?)
Berichtszeitraum: 2022-01-01 bis 2022-12-31
Social scientists spend a lot of time and money hunting causes. For example, a political scientist might want to explore the causal relationship between natural resource wealth and civil war: did the fact that a country has lots of natural resource wealth causally contributed to the start of a civil war in that country? There are two broad traditions within social science, the quantitative tradition and the qualitative tradition, and each has a different way of hunting causes. Qualitative social scientists tend to hunt causes by talking to people and trying to understand their motivations, their experiences and their reactions. In contrast, quantitative social scientists tend to hunt causes by collecting data on a very large number of cases (a large number of countries and civil wars, for example) and then running statistical analyses of this data. Qualitative researchers are very good at pointing out general problems that apply to most quantitative work. And quantitative researchers are very good at pointing out general problem that apply to most qualitative work. This raises the question: in light of this, what methods can social scientists use to hunt causes? This project first begins by answering the question “why do we care about causes in the first place?” and then uses the answer to this question to identify the best methods for hunting causes.
In discussing these issues, methodologists use some rather vague and abstract terminology: “quantitative” “qualitative” “causation” “necessary cause”. Our project has found a way to make this terminology more clear and precise. Our hope is that this will make it much easier to engage in discussions of these complex issues.
In drawing the conclusions that we did, we also did something new. The old approach is to rely on people’s “intuitions” about causation, and to use these intuitions to tell us about what causation is like, and thereby to tell us the best way to hunt causes. But people’s intuitions are unreliable. The new approach instead is to start with the question: “why do we care about causation in the first place?” “what would have to be true about causation for causal knowledge to play the central role that it does in our thinking?”. Answering these questions allows us to work out what causation is like, and thereby what the best methods are for hunting causes---all without having to rely on people’s intuitions.
We have examined the implications of our research for several areas of societal relevance, for example how to study and measure the degree to which women are treated equally to men.
In drawing the conclusions that we did, we also did something new. The old approach is to rely on people’s “intuitions” about causation, and to use these intuitions to tell us about what causation is like, and thereby to tell us the best way to hunt causes. But people’s intuitions are unreliable. The new approach instead is to start with the question: “why do we care about causation in the first place?” “what would have to be true about causation for causal knowledge to play the central role that it does in our thinking?”. Answering these questions allows us to work out what causation is like, and thereby what the best methods are for hunting causes---all without having to rely on people’s intuitions.
We have examined the implications of our research for several areas of societal relevance, for example how to study and measure the degree to which women are treated equally to men.
We resolve five major debates about what causation is like. This allows us to identify the circumstances under which qualitative methods are appropriate, and the circumstances under which quantitative methods are appropriate.
The clear definitions we offer will (we hope) allow the debate about quantitative vs qualitative evidence to be more productive, with each side understanding better where the other side is coming from.
The clear definitions we offer will (we hope) allow the debate about quantitative vs qualitative evidence to be more productive, with each side understanding better where the other side is coming from.