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Activity Number: 389 - Improving Survey Data Quality with Machine Learning Techniques
Type: Invited
Date/Time: Tuesday, July 31, 2018 : 2:00 PM to 3:50 PM
Sponsor: Survey Research Methods Section
Abstract #326562 Presentation
Title: Dynamic, Personalized Instruments via Responsive Matrix Sampling with High-Dimensional Covariates
Author(s): Sean Taylor and Curtiss Cobb and Chelsea Zhang*
Companies: Facebook and Facebook and UC Berkeley
Keywords: surveys; matrix sampling; matrix completion; web surveys; active learning; side information
Abstract:

In a landscape of declining response rates, researchers seek more meaningful data from fewer respondents willing to answer long surveys. Increasingly, survey platforms are leveraging rich sets of covariates from large data sources to build sophisticated outcome models. We show how these models can be used to reduce respondent burden without decreasing information, via an adaptive matrix sampling procedure that downsamples a longer survey instrument into the most informative subset of questions for each respondent. The question selection method optimizes for variance reduction while incorporating side information. As a respondent answers questions, their predictions are updated in an online fashion. The efficiency gains of our approach enable the researcher to terminate sampling early when desired precision is reached or adapt the instrument over time.


Authors who are presenting talks have a * after their name.

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