Using Bayesian Aldrich-McKelvey Scaling to Study Citizens' Ideological Preferences and Perceptions

Aldrich‐McKelvey scaling is a powerful method that corrects for differential‐item functioning (DIF) in estimating the positions of political stimuli (e.g., parties and candidates) and survey respondents along a latent policy dimension from issue scale data. DIF arises when respondents interpret issue scales (e.g., the standard liberal‐conservative scale) differently and distort their placements of the stimuli and themselves. We develop a Bayesian implementation of the classical maximum likelihood Aldrich‐McKelvey scaling method that overcomes some important shortcomings in the classical procedure. We then apply this method to study citizens' ideological preferences and perceptions using data from the 2004–2012 American National Election Studies and the 2010 Cooperative Congressional Election Study. Our findings indicate that DIF biases self‐placements on the liberal‐conservative scale in a way that understates the extent of polarization in the contemporary American electorate and that citizens have remarkably accurate perceptions of the ideological positions of senators and Senate candidates.