The prevention of suicide in India and the developing world: the need for population-based strategies

Very high rates of suicide have been reported from India and the developing world. However, much of the debate on suicide prevention focuses on individuals, methods, site-specific solutions, or particular suicide prevention strategies. This article argues for population based approaches that focus on improving the general health of populations (e.g., macroeconomic policies that aim for social justice, schemes to meet basic human needs, organizing local support groups within vulnerable sections of society, developing and implementing an essential pesticide list, addressing gender issues, and increasing public awareness through the mass media) rather than medical, psychiatric, and other strategies that target individuals (e.g., treatment of mental illness, counseling, etc.) in order to reduce high suicide rates in India and developing countries. Individual approaches will help people in distress and prevent individuals from committing suicide, but will not reduce population suicide rates.     

Determinants of polling accuracy: the effect of opt-in Internet surveys

With the changing polling environment and the difficulties in correctly measuring public opinion, it is crucial to study the effects of polling practices. Elections provide an opportunity to examine how well polls measure support for political parties and how polling practices influence the accuracy of polls. Based on 110 pre-election polls conducted in Sweden, a multiparty system, we find that probability-based samples yield more accurate polls compared to opt-in Internet samples. Thus, our results point to a tradeoff between inexpensive online polls and accuracy.     

Tree‐Based Models for Political Science Data

Political scientists often find themselves analyzing data sets with a large number of observations, a large number of variables, or both. Yet, traditional statistical techniques fail to take full advantage of the opportunities inherent in “big data,” as they are too rigid to recover nonlinearities and do not facilitate the easy exploration of interactions in high‐dimensional data sets. In this article, we introduce a family of tree‐based nonparametric techniques that may, in some circumstances, be more appropriate than traditional methods for confronting these data challenges. In particular, tree models are very effective for detecting nonlinearities and interactions, even in data sets with many (potentially irrelevant) covariates. We introduce the basic logic of tree‐based models, provide an overview of the most prominent methods in the literature, and conduct three analyses that illustrate how the methods can be implemented while highlighting both their advantages and limitations.     

Reconsidering the Role of Politics in Leaving Religion: The Importance of Affiliation

Studies have pointed to politics as an important force driving people away from religion—the argument is that the dogmatic politics of the Christian Right have alienated liberals and moderates, effectively threatening organized religion in America. We argue that existing explanations are incomplete; a proper reconsideration necessitates distinguishing processes of affiliation (with specific congregations) from identification (with religious traditions). Using three data sets, we find evidence that qualifies and complements existing narratives of religious exit. Evaluations of congregational political fit drive retention decisions. At the same time, opposition to the Christian Right only bears on retention decisions when it is salient in a congregational context, affecting primarily evangelicals and Republicans. These results help us understand the dynamics of the oft‐observed relationship between the Christian Right and deidentification and urge us to adopt a broader, more pluralistic view of the politicization of American religion.     

Strength in Numbers or Quality over Quantity? Examining the Importance of Criterion Measure Selection to Define Validity Groups in Performance Validity Test (PVT) Research

Mirroring clinical guidelines, recent Performance Validity Test (PVT) research emphasizes using ≥ 2 criterion PVTs to optimally identify validity groups when validating/cross-validating PVTs; however, even with multiple measures, the effect of which specific PVTs are used as criterion measures remains incompletely explored. This study investigated the accuracy of varying two-PVT combinations for establishing validity status and how adding a third PVT or applying more liberal failure cut-scores affects overall false-positive (FP)/-negative (FN) rates. Clinically referred veterans (N = 114; 30% clinically identified as invalid) completing a six-PVT protocol as during their evaluation were included. Concordance rates were calculated across all possible two-and three-PVT combinations at conservative and liberal cutoffs. Two-PVT combinations classified 72–91% of valid (0–4% FPs) and 17–74% of invalid (0–40% FNs) cases, and three-PVT combinations classified 67–86% of valid (0–6% FPs) and 57–97% of invalid (0–24% FNs) at conservative cutoffs. Liberal cutoffs classified 53–86% of valid (0–15% FPs) and 39–82% of invalid (0–30% FNs) cases for two-PVT combinations and 46–75% of valid (3–27% FPs) and 60–97% of invalid (0–17% FNs) cases for three-PVT combinations. Irrespective of whether a two-or three-PVT combination or conservative/liberal cutoffs were used, many valid and invalid cases failed only one PVT (3–68%).Two-PVT combinations produced high FNs and were less accurate than three-PVTs for detecting invalid cases, though variable accuracy was found within both types of combinations based on the specific PVTs in the combination. Thus, both PVT quantity and quality are important for accurate validity classification in research studies to ensure reliability and replicability of findings. Applying more liberal cutoffs yielded increased sensitivity, but with generally higher FPs yielding problematic specificity, particularly for three-PVT combinations.     

Racial Dynamics of Subprime Mortgage Lending at the Peak

Subprime mortgage lending in the early 2000s was a leading cause of the Great Recession. From 2003 to 2006, subprime loans jumped from 7.6% of the mortgage market to 20.1%, with black and Latino borrowers receiving a disproportionate share. This article leveraged the Home Mortgage Disclosure Act data and multinomial regression to model home-purchase mortgage lending in 2006, the peak of the housing boom. The findings expose a complicated story of race and income. Consistent with previous research, blacks and Latinos were more likely and Asians less likely to receive subprime loans than whites were. Income was positively associated with receipt of subprime loans for minorities, whereas the opposite was true for whites. When expensive (jumbo) loans were excluded from the sample, regressions found an even stronger, positive association between income and subprime likelihood for minorities, supporting the theory that wealthier minorities were targeted for subprime loans when they could have qualified for prime loans. This finding also provides another example of an aspect of American life in which minorities are unable to leverage higher class position in the same way as whites are. Contrary to previous research, model estimates did not find that borrowers paid a penalty (in increased likelihood of subprime outcome) for buying homes in minority communities.