Replication Data for: Measurement Error When Surveying Issue Positions: A MultiTrait MultiError Approach
Description
Voters’ issue preferences have been shown to be key determinants of vote choice, making it essential to reduce measurement error in responses to issue questions in surveys. This study uses a MultiTrait MultiError approach to assess the data quality of issue questions by separating four sources of variation: trait, acquiescence, method, and random error. The questions generally achieved moderate data quality, with 76% on average representing valid variance. Random error made up the largest proportion of error (23%). Error due to method and acquiescence was small. We found that 5-point scales are generally better than 11-point scales, while answers by respondents with lower political sophistication achieved lower data quality. The findings indicate a need to focus on decreasing random error when studying issue positions.
Show moreYear of publication
2025
Authors
Harvard Dataverse - Publisher
Helsinki University
Peter Söderlund - Creator
The University of Manchester
Alexandru Cernat - Creator
Rasmus Siren - Creator
Other information
Fields of science
Political science
Open access
Open
License
Creative Commons CC0 1.0 Universal (CC0 1.0) Public Domain Dedication