Stewart, Brandon M. 2014.
Latent Factor Regressions for the Social Sciences.
Abstract
In this paper I present a general framework for regression in the presence of complex dependence structures between units such as in time-series cross-sectional data, relational/network data, and spatial data. These types of data are challenging for standard multilevel models because they involve multiple types of structure (e.g. temporal effects and cross-sectional effects) which are interactive. I show that interactive latent factor models provide a powerful modeling alternative that can address a wide range of data types. Although related models have previously been proposed in several different fields, inference is typically cumbersome and slow. I introduce a class of fast variational inference algorithms that allow for models to be fit quickly and accurately.
tensorreg.pdf
tensorregappendix.pdf Roberts, Margaret E, et al. 2014. “
Structural topic models for open-ended survey responses”.
American Journal of Political Science 58:1064-1082.
AbstractCollection 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, semi-automated 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.
topicmodelsopenendedexperiments_0.pdf
ajpsappendix.pdfAwarded the Gosnell Prize for Excellence in Political Methodology for the best work in political methodology presented at any political science conference during the preceding year. Data at: http://dx.doi.org/10.7910/DVN/29405