How faith‐based social service organizations manage secular pressures associated with government funding

This article reports selected findings from a qualitative case study of two faith‐based social service organizations to address two questions: (1) How does government funding influence the religious characteristics of faith‐based social service organizations? (2) How do government‐funded, faith‐based social service organizations manage the tensions arising from both secular and religious contexts? The findings suggest that the adaptation of secular institutional practices is not as inevitable as some have feared. Rather, the two organizations studied showed convincingly that their faith traditions and values were alive and widely evident throughout their organizations. Three key strategies emerged as means for maintaining religiousness in the face of secular pressures: (1) Religious identities were perceived as given rather than chosen, and therefore were not negotiable; (2) religious values provided strong justification for seeking relationships with others who do not share their faith; (3) the religious worldview blurred religious and secular distinctions so that secular technologies and practices could comfortably be utilized.     

The Gendered Face of Terrorism

What does sociology have to contribute to our understanding of terrorism? As scholars of gender and the Far Right, we believe that we have much to offer the current debates. In this article, we focus both on contemporary cases of terrorism, the attacks of September 11 and the Oklahoma City bombing, and terrorist movements as gendered: as enactments of masculinity. In particular, we focus on the contemporary white supremacist movement, the most dangerous domestic terrorist threat faced by the USA. We suggest that the ideology and organization of many terrorist groups are saturated with gendered meanings, both as the analytic prism through which they view their situation, and also as a means of political mobilization.     

Empirical Non-Parametric Control Charts: Estimation Effects and Corrections

Owing to the extreme quantiles involved, standard control charts are very sensitive to the effects of parameter estimation and non-normality. More general parametric charts have been devised to deal with the latter complication and corrections have been derived to compensate for the estimation step, both under normal and parametric models. The resulting procedures offer a satisfactory solution over a broad range of underlying distributions. However, situations do occur where even such a large model is inadequate and nothing remains but to consider non- parametric charts. In principle, these form ideal solutions, but the problem is that huge sample sizes are required for the estimation step. Otherwise the resulting stochastic error is so large that the chart is very unstable, a disadvantage that seems to outweigh the advantage of avoiding the model error from the parametric case. Here we analyse under what conditions non-parametric charts actually become feasible alternatives for their parametric counterparts. In particular, corrected versions are suggested for which a possible change point is reached at sample sizes that are markedly less huge (but still larger than the customary range). These corrections serve to control the behaviour during in-control (markedly wrong outcomes of the estimates only occur sufficiently rarely). The price for this protection will clearly be some loss of detection power during out-of-control. A change point comes in view as soon as this loss can be made sufficiently small.     

Markov chain Monte Carlo methods for high dimensional inversion in remote sensing

Summary.  We discuss the inversion of the gas profiles (ozone, NO₃, NO₂, aerosols and neutral density) in the upper atmosphere from the spectral occultation measurements. The data are produced by the 'Global ozone monitoring of occultation of stars' instrument on board the Envisat satellite that was launched in March 2002. The instrument measures the attenuation of light spectra at various horizontal paths from about 100 km down to 10–20 km. The new feature is that these data allow the inversion of the gas concentration height profiles. A short introduction is given to the present operational data management procedure with examples of the first real data inversion. Several solution options for a more comprehensive statistical inversion are presented. A direct inversion leads to a non-linear model with hundreds of parameters to be estimated. The problem is solved with an adaptive single-step Markov chain Monte Carlo algorithm. Another approach is to divide the problem into several non-linear smaller dimensional problems, to run parallel adaptive Markov chain Monte Carlo chains for them and to solve the gas profiles in repetitive linear steps. The effect of grid size is discussed, and we present how the prior regularization takes the grid size into account in a way that effectively leads to a grid-independent inversion.     

Interactionism,Social Organization,and Social Processes: Looking Back and Moving Ahead

With its roots in American pragmatism, symbolic interactionism has created a distinctive perspective and produced numerous important contributions and now offers significant prospects for the future. In this article, I review my intellectual journey with this perspective over forty years. This journey was initiated within the American society, sociology, and symbolic interaction of circa 1960. I note many of the contributions made by interactionists since that time, with particular focus on those who have contributed to the study of social organization and social process. I offer an agenda for the future based on currently underdeveloped areas that have potential. These are inequality orders, institutional analysis, collective action across space and time, and the integration of temporal and spatial orders. The article concludes with calls for further efforts at cross‐perspective dialogues, more attention to feminist scholars, and an elaborated critical pragmatism.     

Quasi-maximum likelihood estimation and inference in dynamic models with time-varying covariances

We study the properties of the quasi-maximum likelihood estimator (QMLE) and related test statistics in dynamic models that jointly parameterize conditional means and conditional covariances, when a normal log-likelihood os maximized but the assumption of normality is violated. Because the score of the normal log-likelihood has the martingale difference property when the forst two conditional moments are correctly specified, the QMLE is generally Consistent and has a limiting normal destribution. We provide easily computable formulas for asymptotic standard errors that are valid under nonnormality. Further, we show how robust LM tests for the adequacy of the jointly parameterized mean and variance can be computed from simple auxiliary regressions. An appealing feature of these robyst inference procedures is that only first derivatives of the conditional mean and variance functions are needed. A monte Carlo study indicates that the asymptotic results carry over to finite samples. Estimation of several AR and AR-GARCH time series models reveals that in most sotuations the robust test statistics compare favorably to the two standard (nonrobust) formulations of the Wald and IM tests. Also, for the GARCH models and the sample sizes analyzed here, the bias in the QMLE appears to be relatively small. An empirical application to stock return volatility illustrates the potential imprtance of computing robust statistics in practice.     

Earnings Inequality in the Nonmetropolitan United States: 1967–19901

Abstract Nonmetropolitan (nonmetro) inequality patterns are contrasted with those of metropolitan (metro) areas to assess the utility of neoclassical and restructuring theoretical frameworks. Inequality measures are constructed from March Current Population Surveys for the years 1968–1991. Results indicate that inequality is greater in nonmetro areas than in metro areas. Results of decomposition procedures suggest that the observed inequality is due to a mix of neoclassical and restructuring factors that account for more inequality in metro than nonmetro areas. National policies must take account of metro/nonmetro differences in patterns and sources of inequality.     

UNDERSTANDING THE DECISION TO PARTICIPATE IN A SURVEY

The lack of full participation in sample surveys threatens theinferential value of the survey method. We review a set of conceptualdevelopments and experimental findings that appear to be informativeabout causes of survey participation; offer an integration ofthat work with findings from the more traditional statisticaland survey methodological literature on nonresponse; and, giventhe theoretical structure, deduce potentially promising pathsof research toward the understanding of survey participation.