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Monday, June 14, 2010

Quantitative methods in economics

I should clarify something I said last week about how this data and statistics course I'm prepping isn't really a standard course: I do know there are lots of econ departments that offer an economic statistics course that is a step below econometrics. I guess what I was thinking is that the intention behind this course was specifically to give students tangible skills working with data, so there is supposed to be a heavy emphasis on finding and accessing economic data and using Excel to manipulate that data, rather than on formulas and statistical theory (which students presumably should have already had anyway, in a lower-division required statistics course). But my impression is that a lot of econ statistics courses for majors at other schools are really more stats courses, just with econ-related examples*.

The interesting thing is that the Siegfried, et al, report ("The Status and Prospects of the Economics Major, JEE, Summer 1991) discusses this very situation (emphasis mine):
Originally conceived as a means of providing students with a sufficient empirical foundation to enrich their understanding and facilitate their active participation in applied courses, the [quantitative methods] requirement all too often fails to fulfill this purpose. Although this requirement presupposes the development of skills in working with real data, contrived numerical examples are more common in these courses... These courses are often overloaded and taught at too fast a pace to adequately prepare students for the empirical dimension of elective courses. Frequently, data appraisal (e.g., survey design, sampling procedures, data accuracy) is squeezed out of the course, and some quantitative methods courses fail to cover adequately the philosophy, appropriate use, and limitations of hypothesis testing and regression analysis. Sophistication in empirical work requires more than just training in statistics. It requires attention to observation skills, measurement problems, and empirical judgment. Students need guidance on how to judge the quality of data, and how to identify evidence that would help to resolve an empirical dispute.

I think that this is what my department was likely striving for when we originally adopted this course. Over the years, instructors have tried to retain the emphasis on data but the content seems to be more along the lines of 'contrived numerical examples'; students use lots of real data and have assignments to make graphs and charts in Excel but from what I can tell, there is still more focus on formulas and statistical concepts than the 'art' of using data to resolve economic questions.

As I plan this course, I am definitely striving for something more along the lines of what Siegfried, et al, suggest:
Rather than view this as a matter of learning statistics, we need to ask what it is that student must know to function as economists. The foundation of empirical methods depends on (1) knowing something about the measurement of economic variables (methods of data collection, reliability, etc.); (2) being able to organize, work with, and manipulate data for purposes of comparison; (3) the capacity to test hypotheses with empirical data; and (4) knowing how to interpret the results of various statistical procedures.

In the framework of backward design, that pretty much lays out my learning outcomes (what I want my students to be able to do). Now I just have to figure out how to achieve this...

* If I am wrong about this, I would really love to hear from people who teach more data- and application-driven courses. As an undergrad advisor, I've seen a lot of requests from transfer students who want to take an econ stats course somewhere else and have it count the same as our course but they rarely look comparable (or at least, they don't look like the course that we think we have, though now that I'm taking a closer look at what my colleagues have been teaching, I think the courses at other schools may be closer than we thought). 

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