Information about the ideological positions of political actors is central to questions of representation, polarization, and voting behavior. Surveys that ask respondents to place actors on a common ideological scale are common, but respondents often introduce systematic biases into those placements.
🔍 What the Paper Examines
The paper focuses on two common respondent-level distortions in scaling: rationalization bias and differential item functioning (DIF). Aldrich–McKelvey (AM) scaling provides a widely used correction for DIF but does not account for rationalization bias. As a result, AM-type approaches can produce misleading estimates when rationalization bias is present.
🧪 How the Claim Is Tested
- Monte Carlo simulations are used to evaluate performance of AM-type models versus an alternative approach.
- The alternative is a unified Bayesian scaling model that simultaneously estimates DIF and rationalization bias.
📈 Key Findings
- Respondents introduce two distinct problems when placing actors on a common ideological scale: rationalization bias (systematic distortion toward respondents' own views) and DIF (items functioning differently across respondents).
- AM scaling effectively addresses DIF but ignores rationalization bias.
- Monte Carlo evidence shows AM-type models can give inaccurate results when rationalization bias exists.
- The Bayesian scaling approach that jointly models DIF and rationalization bias performs better than AM-type models in the presence of rationalization bias.
💡 Why It Matters
Better measurement of actors' ideological positions matters for inferences about political representation, polarization, and voting behavior. A unified Bayesian scaling approach reduces bias from respondent behavior and yields more reliable ideological estimates when rationalization tendencies are present.
