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Population-Based Survey Experiments$
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Diana C. Mutz

Print publication date: 2011

Print ISBN-13: 9780691144511

Published to Princeton Scholarship Online: October 2017

DOI: 10.23943/princeton/9780691144511.001.0001

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Treatments to Improve Measurement

Treatments to Improve Measurement

Chapter:
(p.25) Chapter Two Treatments to Improve Measurement
Source:
Population-Based Survey Experiments
Author(s):

Diana C. Mutz

Publisher:
Princeton University Press
DOI:10.23943/princeton/9780691144511.003.0002

This chapter illustrates population-based experiments designed to improve measurement. These are descendants of the early split-ballot approach, also geared toward improving measurement of attitudes and behaviors, but the approaches are now far more sophisticated and complex. The experimental treatments discussed here are not designed to test a specific theoretical hypothesis so much as to improve measurement. Although the chapter makes a distinction between testing hypotheses and improving measurement, in many cases the fundamental hypothesis of a study is that a measure is flawed in some systematic way. Three general approaches to improving measurement are evident in population-based experiments: the “item count technique,” alteration of the inferential process, and techniques involving anchoring as a means of improving measurement.

Keywords:   measurement, split-ballot approach, hypotheses, item count technique, inferential process, anchoring, population-based experiments

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