Simple Surveys: Response Retrieval Inspired by Recommendation Systems
Nandana Sengupta, Nati Srebro, James Evans

TL;DR
This paper evaluates simple survey methods like ratings, comparisons, and scales across different contexts, analyzing their predictive accuracy for individual and collective preferences, and visualizing respondent interpretations.
Contribution
It introduces simple survey techniques tailored for social science, comparing their effectiveness and revealing insights into respondent perceptions and preferences.
Findings
Continuous ratings best predict individual preferences.
Binary choices efficiently predict group assessments.
Pairwise comparisons excel at capturing personal preferences.
Abstract
In the last decade, the use of simple rating and comparison surveys has proliferated on social and digital media platforms to fuel recommendations. These simple surveys and their extrapolation with machine learning algorithms shed light on user preferences over large and growing pools of items, such as movies, songs and ads. Social scientists have a long history of measuring perceptions, preferences and opinions, often over smaller, discrete item sets with exhaustive rating or ranking surveys. This paper introduces simple surveys for social science application. We ran experiments to compare the predictive accuracy of both individual and aggregate comparative assessments using four types of simple surveys: pairwise comparisons and ratings on 2, 5 and continuous point scales in three distinct contexts: perceived Safety of Google Streetview Images, Likeability of Artwork, and Hilarity of…
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