Replication Data for: Measurement Error When Surveying Issue Positions: A MultiTrait MultiError Approach

Kuvaus

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.
Näytä enemmän

Julkaisuvuosi

2025

Aineiston tyyppi

Tekijät

Harvard Dataverse - Julkaisija

Helsinki University

Peter Söderlund - Tekijä

The University of Manchester

Alexandru Cernat - Tekijä

Kim Backström Orcid -palvelun logo - Muu tekijä, Tekijä

Rasmus Siren - Tekijä

Projekti

Muut tiedot

Tieteenalat

Valtio-oppi, hallintotiede

Kieli

Saatavuus

Avoin

Lisenssi

Creative Commons Yleismaailmallinen (CC0 1.0) Public Domain lausuma

Avainsanat

Asiasanat

Ajallinen kattavuus

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