| This collection of three papers develops two statistical techniques for addressing canonical problems in applied computational social science: unsupervised text analysis and regression with dependent data. In both cases I provide a flexible framework that allows the analyst to leverage known structure within the data to improve inference. The first paper introduces the Structural Topic Model (STM) which generalizes and extends a broad class of probabilistic topic models developed in computer science. Crucially for applied social science, STM provides a framework for estimating the factors which drive topical frequency and content within documents. The second paper explores the challenge that non-convex likelihoods pose for applied research with topic models. The paper presents a series of diagnostics and discusses the under-appreciated role of initialization methods. The third paper introduces Latent Factor Regressions (LFR), a new set of tools for regression modeling in the presence of unobserved heterogeneity or dependence between observations. The approach uses interactive latent effects to provide a unified framework for modeling different data structures, including network, time-series cross-sectional and spatial data.;Each of these methods is designed with a focus on applied work. Estimation algorithms are presented which are fast enough for applied work and software is either currently available (STM) or in development (LFR). The use of these techniques is illustrated with a range of applications from across political science. |
