TY - JOUR
T1 - Structural topic models for open-ended survey responses
AU - Roberts, Margaret E.
AU - Stewart, Brandon Michael
AU - Tingley, Dustin
AU - Lucas, Christopher
AU - Leder-Luis, Jetson
AU - Gadarian, Shana Kushner
AU - Albertson, Bethany
AU - Rand, David G.
N1 - Publisher Copyright:
©2014, Midwest Political Science Association.
PY - 2014/10/1
Y1 - 2014/10/1
N2 - 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 structural topic 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.
AB - 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 structural topic 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.
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U2 - 10.1111/ajps.12103
DO - 10.1111/ajps.12103
M3 - Article
AN - SCOPUS:84907983886
SN - 0092-5853
VL - 58
SP - 1064
EP - 1082
JO - American Journal of Political Science
JF - American Journal of Political Science
IS - 4
ER -