Who Needs Math? - The Monkey Cage - 1 views
themonkeycage.org/...who-needs-math
math social science statistics economics political science quantification bias research
shared by Javier E on 20 Mar 15
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by Larry Bartels on April 9, 2013
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“When something new is encountered, the follow-up steps usually require mathematical and statistical methods to move the analysis forward.” At that point, he suggests finding a collaborator
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But technical expertise in itself is of little avail: ”The annals of theoretical biology are clogged with mathematical models that either can be safely ignored or, when tested, fail. Possibly no more than 10% have any lasting value. Only those linked solidly to knowledge of real living systems have much chance of being used.”
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. If you’re going to talk about economics at all, you need some sense of how magnitudes play off against each other, which is the only way to have a chance of seeing how the pieces fit together.
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My own work has become rather less mathematical over the course of my career. When people ask why, I usually say that as I have come to learn more about politics, the “sophisticated” wrinkles have seemed to distract more than they adde
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“Seeing how the pieces fit together” requires “some sense of how magnitudes play off against each other.” But, paradoxically, ”higher math” can get in the way of “mathematical intuition” about magnitudes. Formal theory is often couched in purely qualitative terms: under such and such conditions, more X should produce more Y. And quantitative analysis—which ought to focus squarely on magnitudes—is less likely to do so the more it is justified and valued on technical rather than substantive grounds.
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I recently spent some time doing an informal meta-analysis of studies of the impact of campaign advertising. At the heart of that literature is a pretty simple question: how much does one more ad contribute to the sponsoring candidate’s vote share? Alas, most of the studies I reviewed provided no intelligible answer to that question; and the correlation between methodological “sophistication” (logarithmic transformations, multinomial logits, fixed effects, distributed lag models) and intelligibility was decidedly negative. The authors of these studies rarely seemed to know or care what their results implied about the magnitude of the effect, as long as those results could be billed as “statistically significant.