Who Sees Corruption? The Bases of Mass Perceptions of Political Corruption in Latin America

The capacity of citizens to see political corruption where it exists and to link such perceptions to evaluations of public officials constitutes an important test of political accountability. Although past research has established that perceived corruption influences political judgments, much less is known regarding the critical prefatory matter of who sees corruption. This article develops a multifaceted theoretical framework regarding the possible bases of perceived corruption. Experiential factors - personal experience and vicarious experience with bribery - mark the starting point for our account. We then incorporate psychological dispositions that may colour judgments about corruption and that may strengthen or weaken the links between experiences and perceptions. Expectations derived from this framework are tested in a series of multi-level models, with data from over 30,000 survey respondents from 17 nations and 84 regions in the Americas

Replication data for: Reconsidering the Measurement of Political Knowledge

Political knowledge has emerged as one of the central variables in political behavior research, with numerous scholars devoting considerable effort to explaining variance in citizens' levels of knowledge and to understanding the consequences of this variance for representation. Although such substantive matters continue to receive exhaustive study, questions of measurement also warrant attention. I demonstrate that conventional measures of political knowledge—constructed by summing a respondent's correct answers on a battery of factual items—are of uncertain validity. Rather than collapsing incorrect and "don't know" responses into a single absence-of-knowledge category, I introduce estimation procedures that allow these effects to vary. Grouped-data multinomial logistic regression results demonstrate that incorrect answers and don't knows perform dissimilarly, a finding that suggests deficiencies in the construct validity of conventional knowledge measures. The likely cause of the problem is traced to two sources: knowledge may not be discrete, meaning that a simple count of correct answers provides an imprecise measure; and, as demonstrated by the wealth of research conducted in the field of educational testing and psychology since the 1930s, measurement procedures used in political science potentially result in "knowledge" scales contaminated by systematic personality effects

appendix – Supplemental material for The Trump Draw: Voter Personality and Support for Donald Trump in the 2016 Republican Nomination Campaign

<p>Supplemental material, appendix for The Trump Draw: Voter Personality and Support for Donald Trump in the 2016 Republican Nomination Campaign by David Fortunato, Matthew V. Hibbing, and Jeffery Mondak in American Politics Research</p