They provide different kinds of answer. Depending on what kind of answers you are looking for, you might use one or the other. If you have a very good metric to answer your problem, like ratio between genders or race, then you quant. If no such metric exist, like impact of discrimination, then you use qual. Most often, people use a combination approach, like "from the scale of 1 to 10, how welcomed do you feel." You can get an average (quant) and then pick a representative sample to illustrate the experience of an average (qual) More on reddit.com

I'm kind of confused. Wouldn't any paper that discusses policy fit this bill? Or are you looking for like Senate hearings, where they bring in experts who explain what would happen? editing while we wait, to give this some solid r/A .S.S. effort. In general, we can understand that a significantly large portion of policy makers are economics-trained (I have no source for this statement, sadly). The rest would most likely fill out law (clearly) and political science (who, if they have to take math stuff, would go econ). I'm also assuming policy makers in this statement does not just mean elected representatives, but all staff associated with the representatives. That means that a large portion of policy makers will either know (a) nothing (b) understand percentages and pretty graphs (c) recognize that regression-based models work fairly well for a large majority of cases (which is all of econometrics). If we then proceed with (c), we need to ask - how are they analyzing the data? Recognize that a beauty in statistics is that while it is objective in some ways, how we approach, operationalize, define, and what we control for all are all subjective choices. That's how you can get two people showing results about the same policy proposal with drastically different results. Still - how? Well, we can start with academic discussions. For example, the 1033 Program - militarizing police. Good? Bad? Bove & Gravilova, 2017 Harris et al, 2017 These two use different regression analyses - Harris used instrumented panels at the county-year level, while Bove & Gravilova use linear regression at the county-year level. I have a paper forthcoming using negative binomial regressions at the state-month level. In all of these cases, a regression basically says, "Let's throw all of our data into a scatter plot and draw a line through it." Right, so we have purchases on the x axis, and for all 3 the y axis is "assaults against police". How we draw the lines differ - I draw a line for each individual state, while they draw lines for each individual county. However, we look at the slopes of the lines and say, "well, as X increases, Y is [decreasing, increasing, no effect - arguments!]." I can go further into why the choices for the three different methods of regressions were used. We can then look into Think Tanks. They get paid by the government to do research. You might know The Heritage Foundation for conservative think tanks, while perhaps I can point to Brookings for a more liberal think tank, and Mathematica for a relatively balanced one. They do similar research to academics, but they try and frame their results much more palatable for the every day person or senator. Finally, we have testimonials at Senate Hearings. This is usually less "here are statistical methods" but more so, "I am an expert, this is the state of research, this is why Policy A is good/bad." For example, Jane Waldfogel, a very important researcher in policy, has testified in front of Congress about the benefits of paid leave (I can't find the link, but source is Joint Hearing March 6, 2008, Serial No. 110-114, Investing in the future of the federal wordforce: Paid parental leave improves recruitment and retention.). Overall, (getting to your response to my original question) - if there is analysis being done, it comes in the form of a regression. This is twofold - currently, regressions are just the 'gold standard' of analysis. There is a new push from Bayesian statisticians and a push for Computer Modeling from some computer science geeks, but by and large, regression takes the cake. Now, there are a multitude of different types of regression, all which answer slightly different questions. Logistic regressions predict Yes/No answers (likelihood to vote; to get married). Panel regressions look at the same unit (individuals, states, countries) over a long period of time. We do this a lot in large governmental datasets where we're testing to see the effects of head start and other educational programs. Cross-sectional regressions look at different time points, but not necessarily the same individual (Developmental trends). There's negative binomials and poissons to look at count data, which has a definite 0 and typically has a strange distribution. There's difference of differences regressions (interactions) where we're looking to see the effects a certain policy had when we gave it to one group versus another (also called randomized control trials ). But all of them, in the end, are drawing a certain number of lines through the data and see what the slopes of those lines look like. More on reddit.com

Quantitative research is when you are measuring social issues you collect numbers (quantities) instead of abstract responses. So, if you were trying to survey people to find out how happy they are you would ask them to rate their happiness on a scale of 1-10 instead of simply giving them a box to write a freeform answer in.

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Quantitative- measured in numbers (quantity), qualitative- measured in characteristics (quality) More on reddit.com