16:50 - 18:30
PS10
Room:
Room: South Room 225
Panel Session 10
Sebastian Ramirez-Ruiz - Cheating in Online Assessments of Political Knowledge: Evidence from Digital Trace Data
Jakob Kemper - Piloting Experimental Tests of Macro-Micro-Level Effects in an Artificial Online State: How Income Inequalities Affect Political Solidarities
Jakob Kemper - The behavioural consequences of political solidarities: Validating the solidarity game on a representative online sample with real-time interaction in oTree
Hannah Bucher - Analyzing voting behavior with different survey samples: Results from a large-scale comparison of a nonprobability and a probability survey.
Cheating in Online Assessments of Political Knowledge: Evidence from Digital Trace Data
PS10-1
Presented by: Sebastian Ramirez-Ruiz
Simon Munzert 1Sebastian Ramirez-Ruiz 1, Pablo Barberá 2, Andrew M. Guess 3, JungHwan Yang 4
1 Hertie School
2 Department of Political Science and International Relations, University of Southern California
3 Department of Politics, Princeton University
4 Department of Communication, University of Illinois at Urbana-Champaign
In this note, we provide direct evidence of cheating in online assessments of political knowledge. We combine survey responses with web tracking data of a German and a U.S. online panel to assess whether people turn to external sources for answers. We observe item-level prevalence rates of cheating that range from 0 to 12% depending on question type and difficulty, and find that 23% of respondents engage in cheating at least once across waves. In the U.S. panel, which employed a commitment pledge, we observe cheating behavior among less than 1% of respondents. We find robust respondent- and item-level characteristics associated with cheating. However, item-level instances of cheating are rare events; as such, they are difficult to predict and correct for without tracking data. Even so, our analyses comparing naive and cheating-corrected measures of political knowledge provide evidence that cheating does not substantially distort inferences.