Suchergebnisse

Since the creation of Volunteers in Service to America (VISTA) in 1964 and AmeriCorps in 1993, a stated goal of national service programs has been to strengthen the overall health of communities across the United States. But whether national service programs have such community effects remains an open question. Using longitudinal cross-lagged panel and change-score models from 2005 to 2013, this study explores whether communities with national service programs exhibit greater subjective well-being. We use novel measures of subjective well-being derived from tweeted expressions of emotions, engagement, and relationships in 1,347 U.S. counties. Results show that national service programs improve subjective well-being primarily by mitigating threats to well-being and communities that exhibit more engagement are better able to attract national service programs. Although limited in size, these persistent effects are robust to multiple threats to inference and provide important new evidence on how national service improves communities in the United States.

This article is an empirical evaluation of the choice of fixed cutoff points in assessing the root mean square error of approximation (RMSEA) test statistic as a measure of goodness-of-fit in Structural Equation Models. Using simulation data, the authors first examine whether there is any empirical evidence for the use of a universal cutoff, and then compare the practice of using the point estimate of the RMSEA alone versus that of using it jointly with its related confidence interval. The results of the study demonstrate that there is little empirical support for the use of .05 or any other value as universal cutoff values to determine adequate model fit, regardless of whether the point estimate is used alone or jointly with the confidence interval. The authors' analyses suggest that to achieve a certain level of power or Type I error rate, the choice of cutoff values depends on model specifications, degrees of freedom, and sample size.

A key advantage of the root mean square error of approximation (RMSEA) is that under certain assumptions, the sample estimate has a known sampling distribution that allows for the computation of confidence intervals. However, little is known about the finite sampling behaviors of this measure under violations of these ideal asymptotic conditions. This information is critical for developing optimal criteria for using the RMSEA to evaluate model fit in practice. Using data generated from a computer simulation study, the authors empirically tested a set of theoretically generated research hypotheses about the sampling characteristics of the RMSEA under conditions commonly encountered in applied social science research. The results suggest that both the sample estimates and confidence intervals are accurate for sample sizes of n = 200 and higher, but caution is warranted in the use of these measures at smaller sample sizes, at least for the types of models considered here.

In this article, the authors examine the most common type of improper solutions: zero or negative error variances. They address the causes of, consequences of, and strategies to handle these issues. Several hypotheses are evaluated using Monte Carlo simulation models, including two structural equation models with several misspecifications of each model. Results suggested several unique findings. First, increasing numbers of omitted paths in the measurement model were associated with decreasing numbers of improper solutions. Second, bias in the parameter estimates was higher in samples with improper solutions than in samples including only proper solutions. Third, investigations of the consequences of using constrained estimates in the presence of improper solutions indicated that inequality constraints helped some samples achieve convergence. Finally, the use of confidence intervals as well as four other proposed tests yielded similar results when testing whether the error variance was greater than or equal to zero.

This article compares maximum likelihood (ML) estimation to three variants of two-stage least squares (2SLS) estimation in structural equation models. The authors use models that are both correctly and incorrectly specified. Simulated data are used to assess bias, efficiency, and accuracy of hypothesis tests. Generally, 2SLS with reduced sets of instrumental variables performs similarly to ML when models are correctly specified. Under correct specification, both estimators have little bias except at the smallest sample sizes and are approximately equally efficient. As predicted, when models are incorrectly specified, 2SLS generally performs better, with less bias and more accurate hypothesis tests. Unless a researcher has tremendous confidence in the correctness of his or her model, these results suggest that a 2SLS estimator should be considered.

AbstractLooking to supplement common economic indicators, politicians and policymakers are increasingly interested in how to measure and improve the subjective well-being of communities. Theories about nonprofit organizations suggest that they represent a potential policy-amenable lever to increase community subjective well-being. Using longitudinal cross-lagged panel models with IRS and Twitter data, this study explores whether communities with higher numbers of nonprofits per capita exhibit greater subjective well-being in the form of more expressions of positive emotion, engagement, and relationships. We find associations, robust to sample bias concerns, between most types of nonprofit organizations and decreases in negative emotions, negative sentiments about relationships, and disengagement. We also find an association between nonprofit presence and the proportion of words tweeted in a county that indicate engagement. These findings contribute to our theoretical understanding of why nonprofit organizations matter for community-level outcomes and how they should be considered an important public policy lever.

AbstractWhat determines countries' successful transition to democracy? This article explores the impact of granting civil rights in authoritarian regimes and especially the gendered aspect of this process. It argues that both men's and women's liberal rights are essential conditions for democratisation to take place: providing both women and men rights reduces an inequality that affects half of the population, thus increasing the costs of repression and enabling the formation of women's organising – historically important to spark protests in initial phases of democratisation. This argument is tested empirically using data that cover 173 countries over the years 1900–2012 and contain more nuanced measures than commonly used. Through novel sequence analysis methods, the results suggest that in order to gain electoral democracy a country first needs to furnish civil liberties to both women and men.

In the wake of the Cold War, democracy has gained the status of a mantra. Yet there is no consensus about how to conceptualize and measure regimes such that meaningful comparisons can be made through time and across countries. In this prescriptive article, we argue for a new approach to conceptualization and measurement. We first review some of the weaknesses among traditional approaches. We then lay out our approach, which may be characterized ashistorical,multidimensional,disaggregated,andtransparent.We end by reviewing some of the payoffs such an approach might bring to the study of democracy.