Dear Applied Statistics Workshop Community,
Our next meeting of the semester will be on January 25 (12:00 EST). Justin
Grimmer will present "A Statistical Framework to Engage the Problem of
Disengaged Survey Respondents."
<Where>
CGIS K354
Bagged lunches are available for pick-up at 11:40 (CGIS K354).
Zoom:
https://harvard.zoom.us/j/99181972207?pwd=Ykd3ZzVZRnZCSDZqNVpCSURCNnVvQT09
<Abstract>
Researchers in academia, government, and industry increasingly rely upon
cheaper online surveys to measure public opinion. However, with their lower
cost, online surveys increase the risk of bias from inattentive or
disengaged survey respondents entering the sample – a risk that remains
even after survey firms and researchers use well-developed filters and
attention checks to exclude these disengaged respondents. In this paper, we
introduce a statistical framework for surveys with disengaged respondents
and tools to address the bias. First, we develop a partial identification
approach that clarifies the extent to which relevant estimands can be
identified in the presence of disengaged respondents. These bounds apply
regardless of how well attention checks uncover disengaged respondents.
Second, we show that simply dropping respondents who are flagged as
disengaged or inattentive from the analysis can lead to selection bias if
the scientific question is about the attitudes or beliefs in a general
target population (e.g., adults in the US). To correct for this, we
introduce partial and point identification approaches that adjust for this
selection bias. We apply our estimators to study the prevalence of extreme
anti-democratic attitudes and find that – despite alarming topline results
— that the survey data is consistent with there being effectively no
respondents who support these views.
<2022-2023 Schedule>
GOV 3009 Website:
https://projects.iq.harvard.edu/applied.stats.workshop-gov3009
Calendar:
https://calendar.google.com/calendar/embed?src=c_3v93pav9fjkkldrbu9snbhned8…
Best,
Shusei