Mountain View
biomedical and healthCAre Data Discovery Index Ecosystem
help Advanced Search
Title: Structural Topic Models for Open-Ended Survey Responses      
dateReleased:
07-05-2015
downloadURL: http://dx.doi.org/10.7910/DVN/29405
ID:
doi:10.7910/DVN/29405
description:
Collection and especially analysis of open-ended survey responses are relatively rare in the discipline and when conducted are almost exclusively done through human coding. We present an alternative, semiautomated approach, the structura ltopic model (STM) (Roberts, Stewart, and Airoldi 2013; Roberts et al. 2013), that draws on recent developments in machine learning based analysis of textual data. A crucial contribution of the method is that it incorporates information about the document, such as the author'™s gender, political affiliation, and treatment assignment (if an experimental study). This article focuses on how the STM is helpful for survey researchers and experimentalists. The STM makes analyzing open-ended responses easier, more revealing, and capable of being used to estimate treatment effects. We illustrate these innovations with analysis of text from surveys and experiments.
description:
Roberts, Margaret E; Stewart, Brandon M; Tingley, Dustin; Lucas, Christopher; Leder-Luis, Jetson; Gadarian, Shana Kushner; Albertson, Bethany; Rand, David G., 2015, "Structural Topic Models for Open-Ended Survey Responses", http://dx.doi.org/10.7910/DVN/29405, Harvard Dataverse, V1
name:
Roberts, Margaret E
Stewart, Brandon M
Tingley, Dustin
Lucas, Christopher
Leder-Luis, Jetson
Gadarian, Shana Kushner
Albertson, Bethany
Rand, David G.
homePage: http://www.harvard.edu/
name:
Harvard University
ID:
SCR:011273
abbreviation:
DataVerse
homePage: http://thedata.org/
name:
Dataverse Network Project
ID:
SCR:001997