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Mapping Policy Preferences From TextsStatistical Solutions for Manifesto Analysts$
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Andrea Volkens, Judith Bara, Ian Budge, Michael D. McDonald, and Hans-Dieter Klingemann

Print publication date: 2013

Print ISBN-13: 9780199640041

Published to Oxford Scholarship Online: January 2014

DOI: 10.1093/acprof:oso/9780199640041.001.0001

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Measuring Uncertainty and Error Directly from End Estimates

Measuring Uncertainty and Error Directly from End Estimates

Chapter:
(p.107) 6 Measuring Uncertainty and Error Directly from End Estimates
Source:
Mapping Policy Preferences From Texts
Author(s):

Andrea Volkens

Judith Bara

Ian Budge

Michael D. McDonald

Robin Best

Simon Franzmann

Publisher:
Oxford University Press
DOI:10.1093/acprof:oso/9780199640041.003.0007

This chapter starts by re-emphasising the importance of context in assessing estimate uncertainty and error. This not only means bringing general validity and reliability to the assessment but also going beyond individual estimate error (SEM) to distributional comparisons, where relying exclusively on SEMs leads to Type I error (rejecting true differences). Individual estimate intervals are useful for the limited purpose of deciding whether the difference between two adjacent estimates is real or just noise. Drawing on general measurements models proposed by Hausman (1978) and Gulliksen (1950) SEMs are derived for RILE estimates at individual, country and party levels. These are reported on the MARPOR website. A computer programme to calculate new ones is also available. Over the dataset the average SEM for RILE estimates is ±7.00 out of a scale with 200 points. This buttresses earlier assertions that error in the Manifesto data is limited.

Keywords:   analysing reliability, estimating error, point estimates, confidence intervals, measuring uncertainty

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