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<p>below is an interesting Data Science talk hosted by GradQuant on
April 22.<br>
</p>
<p>V. Tsotras<br>
</p>
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GradQuant will host Dr. Felicitas Mittereder from Facebook this
Spring for a talk titled
<i>"Can We Predict Breakoff and Intervene Appropriately in Web
Surveys?"</i> Please register to receive the Zoom link.
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<div><b>Date: </b>Thursday, April 22, 2021</div>
<div><b>Time: </b>11am-noon</div>
<div><b>Location:</b> Zoom </div>
<div><br>
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<div><b>Register at:</b>
<a class="moz-txt-link-freetext" href="https://ucr.zoom.us/meeting/register/tJAkc--srDsjHtHbVziUTzyAWUdDCMuH1CoA">https://ucr.zoom.us/meeting/register/tJAkc--srDsjHtHbVziUTzyAWUdDCMuH1CoA</a></div>
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<div><b>Abstract: </b>With increasing use of the internet for
social research, Web surveys have become one of the most
important and efficient tools for collecting survey data.
One of the biggest threats to data quality in Web surveys is
breakoff, which we see in this mode of data collection much
more prominently than in any other mode. Given the (already)
lower response rates in web surveys compared to more
traditional data collection modes, it is crucial to keep as
many diverse respondents in a given web survey as possible
and prevent breakoff bias, maintaining high data quality and
producing accurate survey estimates.</div>
<div><br>
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<div>We fitted a dynamic survival model to data from a real
web survey to predict the likelihood of breaking off at both
the respondent and page levels. This model makes use of the
survey data, along with rich paradata and accessible
administrative information from the sampling frame.</div>
<div><br>
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<div>After we evaluated the quality of predictions based on
the model, we applied the model as part of a randomized
experiment designed to reduce breakoff in the same on-going
online survey on sustainability conducted by the Institute
for Social Research at the University of Michigan. We used
the model to predict page-level breakoff risks in a live
fashion while respondents were taking the Web survey.
Respondents in the treatment group saw an intervention
message once their risk of breaking off passed a certain
threshold, while respondents in the control group had the
standard collection procedure.</div>
<div><br>
</div>
<div>Our analyses show that female respondents and students
reacted positively on intervention messages and broke off at
lower rates when assigned to the treatment group.
Additionally, breakoff respondents within the treatment
group answered more survey questions than untreated breakoff
respondents.</div>
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