Replication Data for: Topics, Concepts, and Measurement: A Crowdsourced Procedure for Validating Topics as Measures

Topic models, as developed in computer science, are effective tools for exploring and summarizing large document collections. When applied in social science research, however, they are commonly used for measurement, a task that requires careful validation to ensure that the model outputs actually capture the desired concept of interest. In this paper, we review current practices for topic validation in the field and show that extensive model validation is increasingly rare, or at least not systematically reported. To supplement current practices, we refine an existing crowd-sourcing method for validating topic quality (Chang et al., 2009) and go on to create new procedures for validating conceptual labels provided by the researcher. We illustrate our method with an analysis of Facebook posts by U.S. Senators and provide software and guidance for researchers wishing to validate their own topic models. While tailored, case-specific validation exercises will always be best, we aim to improve standard practices by providing general-purpose tools to validate topics as measures.