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Making Social Sciences More ScientificThe Need for Predictive Models$
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Rein Taagepera

Print publication date: 2008

Print ISBN-13: 9780199534661

Published to Oxford Scholarship Online: September 2008

DOI: 10.1093/acprof:oso/9780199534661.001.0001

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Example of Model Building: Electoral Volatility

Example of Model Building: Electoral Volatility

Chapter:
(p.34) 4 Example of Model Building: Electoral Volatility
Source:
Making Social Sciences More Scientific
Author(s):

Rein Taagepera (Contributor Webpage)

Publisher:
Oxford University Press
DOI:10.1093/acprof:oso/9780199534661.003.0004

The foremost mental roadblocks in predictive model building are refusal to make outrageous simplifications and reluctance to play with means of extreme cases. “Ignorance-based” models focus on conceptual constraints: What do we already know about the situation, even before collecting any data? Eliminate the conceptually forbidden areas where data points could not possibly occur, and locate the conceptual anchor points where the value of x imposes a unique value of y. Once this is done, few options may remain for how y can depend on x–unless you tell yourself “It can't be that simple.” A low R 2 may still confirm a predictive model, and a high one may work to reject it.

Keywords:   anchor points, conceptual constraints, electoral volatility, forbidden areas, ignorance-based models, means of extreme cases, mental roadblocks, model building, predictive model, R2, simplifications

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