How to teach methods 

 Over on the  polmeth   mailing list there is a small discussion brewing about how to teach undergraduate methods classes. Much of the discussion is on how to manage the balance between computation and statistics. A few posters are using R as their main data analysis tool, which provoked others to comment that this might push a class too far away from its original intent: to learn research methods (although one teacher of R indicated that a bigger problem was the relative inability to handle .zip files). This got me thinking about how research methods, computing and statistics fit into the current education framework. 

 As a gross and unfair generalization, much of college is about learning how take a set of skills and use them to make effective and persuasive arguments. In a literature class, for instance, one might use the skills of reading and writing to critical engage a text. In mathematics, one might take the "skill" of logic and use it to derive a proof.  

 The issue with introductory methods classes is that many undergraduates come into school without a key skill: computing. It is becoming increasingly important to have proficient computing skills in order to make cogent arguments with data. I wonder if it is time to rethink how we teach computing at lower levels of education to adequately prepare students for the modern workplace. There is often emphasis on  using  computers to teach students, but I think it will become increasingly important to teach computers  to  students. This way courses on research methods can focus on how to combine computing and statistics in order to answer interesting questions. We could spend more time matching tools to questions and less time simply explaining the tool.  

 Of course, my argument reeks of passing buck. A broader question is this: where do data analysis and computing fit in the education model? Is this a more fundamental skill that we should build up in children earlier? Is it perfectly fine where it is, being taught in college?