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Existing approaches to estimating ideal points offer no method for consistent estimation or inference without relying on strong parametric assumptions. In this paper, I introduce a nonparametric approach to ideal-point estimation and inference that goes beyond these limitations. I show that some inferences about the relative positions of two pairs of legislators can be made with minimal assumptions. This information can be combined across different possible choices of the pairs to provide estimates and perform hypothesis tests for all legislators without additional assumptions. I demonstrate the usefulness of these methods in two applications to Supreme Court data, one testing for ideological movement by a single justice and the other testing for multidimensional voting behavior in different decades.

Many types of time series data in political science, including polling data and events data, exhibit important features'such as irregular spacing, noninstantaneous observation, overlapping observation times, and sampling or other measurement error'that are ignored in most statistical analyses because of model limitations. Ignoring these properties can lead not only to biased coefficients but also to incorrect inference about the direction of causality. This article develops a continuous-time model to overcome these limitations. This new model treats observations as noisy samples collected over an interval of time and can be viewed as a generalization of the vector autoregressive model. Monte Carlo simulations and two empirical examples demonstrate the importance of modeling these features of the data.

The empirical study of spatial issue voting has experienced a resurgence in recent years due to advances in data collection and research design. Grynaviski and Corrigan make several important contributions to this literature. In this note, we comment on one of Grynaviski and Corrigan's recommendations—to not include a main effect for issue importance when estimating models assessing the interactive relationship between importance and policy proximity. According to the authors, including the main importance term is incorrect because it is not necessary in representing a scale-invariant functional form under some assumptions and is insufficient under others. In deriving their reduced-form expression, the authors produce a model that is unintuitive and inappropriate for most data. Moreover, the restrictions Grynaviski and Corrigan impose on their model can produce perverse empirical predictions. We show that a model including main effect terms for importance is indeed scale invariant and that inclusion of the main importance term is necessary for scale invariance with respect to partial utility functions.

AbstractIn this article, we present a principled method for updating
estimates of the ideology of Supreme Court justices based on each
new vote they cast. We apply this method to the ideological
positions of the newly appointed members of the Court: John Roberts,
Samuel Alito, and Sonia Sotomayor. This approach allows us to gain
not only an estimate of justices' ideologies but also a greater
understanding of the level of uncertainty we should have about these
values, including how much we can learn about a new justice's views
after he or she has cast a given number of votes on the Court.

Over the last decade the scholarship on judicial politics has increasingly emphasized the strategic aspects of decision making in the United States Supreme Court. This scholarship, however, has struggled with two significant limitations—the restriction to unidimensional policy spaces and the assumption of binary comparisons of alternatives. These two assumptions have the advantage of implying stable, predictable outcomes, but lack a sound theoretical foundation and assume away potentially important aspects of strategic behavior on the Court. In this article, we identify institutional features of the Court that, under certain conditions, allow us to relax these two assumptions without sacrificing stable, predictable policy outcomes. In particular, we formalize the "part-by-part" opinion voting used by the justices, a feature that, together with separable preferences over policy issues, implies stable policy outcomes around the issue-by-issue median of the justices.

AbstractHigh-profile advocates are pushing states to move away from judicial elections and toward a "merit" method because it purportedly produces the best quality judges. Quality, however, is difficult to measure empirically. Rather than attempt to measure quality, we examine whether certain types of state supreme courts are more forward-looking than others. States are likely to desire forward-looking behavior among judges because it can protect judicial legitimacy, help states to control policy, and could be more efficient than myopic behavior. Using a recent innovation in matching called covariate-balancing propensity scores, we find that the U.S. Supreme Court is equally likely to review and reverse decisions by judges regardless of their selection or retention methods. These results suggest that state supreme court justices, no matter their paths of getting to (and staying on) their courts, are roughly equal in terms of forward-looking behavior.

The social mediation role of mobile technology is typified by mHealth apps designed to connect individuals to others and support substance use disorder (SUD) recovery. In this study, we examined the use and utility of one such app designed to support people living with HIV (PLWH) and SUD. Drawing on Ling's emphasis on reciprocity and micro-coordination in mobile telephony as a social mediation technology, we gathered digital trace data from app logs to construct two metrics, initiation (i.e. whether a particular feature is engaged on a given day) and intensity (i.e. degree of involvement in the activity when engaged on that day), at three levels of communication—networked (one-to-many), dyadic (one-to-one), and intraindividual (self-to-self). We consider these system features alongside use of information resources, games and relaxation links, a meeting and events calendar, and support tools to address use urges. We found few differences in patterns of use by race, sex, and age, though African Americans were less likely to engage in intraindividual expression, whereas women and older users were more likely to make use of this feature. The initiation and intensity of network and dyadic reception, as well as the intensity of network expression, predicts recovery outcomes as measured on a weekly "check-in" survey, suggesting the utility of mobile log data for digital phenotyping in mHealth. By implementing this app during the COVID-19 pandemic, the study also found the disruption caused by national lockdown was negatively related to the app use.

BACKGROUND: By 2030, the number of US adults age ≥65 will exceed 70 million. Their quality of life has been declared a national priority by the US government. OBJECTIVE: Assess effects of an eHealth intervention for older adults on quality of life, independence, and related outcomes. DESIGN: Multi-site, 2-arm (1:1), non-blinded randomized clinical trial. Recruitment November 2013 to May 2015; data collection through November 2016. SETTING: Three Wisconsin communities (urban, suburban, and rural). PARTICIPANTS: Purposive community-based sample, 390 adults age ≥65 with health challenges. Exclusions: long-term care, inability to get out of bed/chair unassisted. INTERVENTION: Access (vs. no access) to interactive website (ElderTree) designed to improve quality of life, social connection, and independence. MEASURES: Primary outcome: quality of life (PROMIS Global Health). Secondary: independence (Instrumental Activities of Daily Living); social support (MOS Social Support); depression (Patient Health Questionnaire-8); falls prevention (Falls Behavioral Scale). Moderation: healthcare use (Medical Services Utilization). Both groups completed all measures at baseline, 6, and 12 months. RESULTS: Three hundred ten participants (79%) completed the 12-month survey. There were no main effects of ElderTree over time. Moderation analyses indicated that among participants with high primary care use, ElderTree (vs. control) led to better trajectories for mental quality of life (OR=0.32, 95% CI 0.10–0.54, P=0.005), social support received (OR=0.17, 95% CI 0.05–0.29, P=0.007), social support provided (OR=0.29, 95% CI 0.13–0.45, P<0.001), and depression (OR= −0.20, 95% CI −0.39 to −0.01, P=0.034). Supplemental analyses suggested ElderTree may be more effective among people with multiple (vs. 0 or 1) chronic conditions. LIMITATIONS: Once randomized, participants were not blind to the condition; self-reports may be subject to memory bias. CONCLUSION: Interventions like ET may help improve quality of life and socio-emotional ...