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In the absence of an effective treatment or vaccine against SARS-CoV-2, preventing further transmission of the virus is a major challenge. In addition to mobility restrictions and testing efforts, governmental and corporate actors have started to deploy digital contact tracing apps in support of classical contact tracing. However, a key condition for these apps to work is the comprehensive uptake and usage by substantive parts of the population, in particular by those at risk of exposure and transmission. By design, privacy restrictions of many apps impede both evaluation of these tools and decision-making to support uptake. Combining a unique set of mobile tracking data to measure uptake of Germany’s official tracing app with an online-representative panel survey, we explore the predictability of app uptake with respondent characteristics. Using a randomized intervention, we then identify the effects of short video messages addressing privacy concerns and pro-social versus self-serving behavior on behavioral compliance and other relevant outcomes.

We extend a previous study of uptake of Germany’s official COVID-19 contact tracing app (see https://osf.io/6jstp/). Using a randomized intervention, we identify the effects of varying monetary incentives on people’s willingness to install and use the app on their mobile device and other relevant outcomes.

We implement a paired conjoint with forced choice experiment to elicit citizen preferences in the moral dilemma of triage decisions. The experiment is framed in the context of the looming consequences of the COVID-19 pandemic on resource allocation in hospitals. We aim to identify (a) key heuristics applied in the population to make these ethically difficult choices, (b) how they vary across subpopulations, and (c) how they vary across countries.

Scholars often use Federal Open Market Committee (FOMC) votes to estimate the preferences of central bankers. However, rarely do committee members on the FOMC cast dissenting votes. This article demonstrates the usefulness of using what central bankers say in FOMC meetings rather than how they vote to better measure central bank preferences. Using automated text analysis tools and scaling methods, we develop a new measure of central bank preferences on the FOMC leading up to the financial crisis (2005 - 2008).

In this article I’m going to argue two points. First, content analysis should not be seen as fundamentally different to any other type of quantitative method available to political scientists. And second, that political methodologists need to integrate content analysis into their current statistical machinery.

We review various dimensions along which words differ and which, sometimes as part of a word recognition model, have been claimed to predict performance in the visual lexical decision task. Models of word recognition have typically involved inadequate, or non-existent, semantic representations and have dealt with words existing in isolation from any context. We propose an alternative perspective in which it is the relationships between words - reflecting usage and meaning - rather than the discrete entities themselves, that are fundamental to lexical processing. We present Contextual Distinctiveness (CD), a corpus-derived measure of the plurality of the different content-word contexts in which a word occurs in speech, and demonstrate that it is a significant predictor of response times in a simple visual lexical decision task. We argue that LDT effects previously attributed to Age of Acquisition and word frequency should be reinterpreted in terms of CD. As well as subsuming a number of other lexical variables, we detail further advantages of CD, in terms of computational tractability, objectivity, relation to real language, and relation to formal linguistics.