The nature of incoming graduate social work students' attitudes toward sexual minorities

Competent social work practice with sexual minorities requires educators to identify factors that can be addressed in the graduate social work curriculum to foster affirming attitudes among students. This study explored the attitudes incoming master of social work (MSW) students hold toward sexual minorities, utilizing a scale that addresses contemporary subtle biases toward lesbian, gay, and bisexual (LGB) people, rather than overt, fear- and morality-based objections measured in previous studies. We explored the role of race/ethnicity, age, sex, sexual orientation, religiosity, political ideology, perceived biological causation of sexual orientation, and LGB social contacts on students' attitudes toward sexual minorities. Multivariable linear regression results suggest that being African American/Black (versus White), older, and heterosexual (versus sexual minority), and greater religiosity (importance of religion and frequency of service attendance) and conservative political ideology, predicted less affirming attitudes, while greater endorsement for genetic causation of sexual orientation and exposure to LGB friends and immediate family members each predicted more affirming attitudes among our sample.     

Mobility and the Return to Education: Testing a Roy Model with Multiple Markets

Self–selected migration presents one potential explanation for why observed returns to a college education in local labor markets vary widely even though U.S. workers are highly mobile. To assess the impact of self–selection on estimated returns, this paper first develops a Roy model of mobility and earnings where workers choose in which of the 50 states (plus the District of Columbia) to live and work. Available estimation methods are either infeasible for a selection model with so many alternatives or place potentially severe restrictions on earnings and the selection process. This paper develops an alternative econometric methodology that combines Lee's (1983) parametric maximum order statistic approach to reduce the dimensionality of the error terms with more recent work on semiparametric estimation of selection models (e.g., Ahn and Powell (1993)). The resulting semiparametric correction is easy to implement and can be adapted to a variety of other polychotomous choice problems. The empirical work, which uses 1990 U.S. Census data, confirms the role of comparative advantage in mobility decisions. The results suggest that self–selection of higher educated individuals to states with higher returns to education generally leads to upward biases in OLS estimates of the returns to education in state–specific labor markets. While the estimated returns to a college education are significantly biased, correcting for the bias does not narrow the range of returns across states. Consistent with the finding that the corrected return to a college education differs across the U.S., the relative state–to–state migration flows of college– versus high school–educated individuals respond strongly to differences in the return to education and amenities across states.     

竞教比赛的多层面Rasch模型分析

运用多层面Rasch模型,从选手、评委、性别和评分量表等几个层面对英语专业青年教师竞教比赛评分质量进行了研究。该研究涉及5名评委和6名选手。研究结果表明,选手的教学能力有显著差别。评委在评分严厉度上存在差异,这种差异可能会影响比赛结果的信度。评分量表不能较好地区分选手的教学能力,评委在评分过程中有较为严重的趋中现象。偏差分析表明,部分评委对某些选手的评分有显著偏差。     

Black Interactionist Thought and the Lived-Experience Approach to Symbolic Interactionism

Du Bois is often regarded as an important scholar for his contributions to the development of sociology. However, less is known about his work in developing interactionist thought. This essay is an introduction to this special issue, and a small attempt to acknowledge the work of scholars of color within the interactionist tradition. The Du Bosian approach to sociology has for too long been dismissed out of hand. Scholars pursuing new areas of inquiry, topics outside the bounds of “mainstream sociology,” are often met with fierce resistance—even today. Instead of building these scholars up, through mentorship and aid, so-called “accomplished” scholars see fit to tear down the work of those not like them. The Du Bosian perspective celebrates the plurality of voices, advocates for mentorship, and sees sociological inquiry as rooted in the real needs and concerns of those so marginalized. As this collection of articles illustrates, even when conforming to scientific standards work in this tradition has a political dimension as it lays bare the inequities in society—even, at times, drawing government interference with their work. This issue also calls upon professional sociologists to reflect on the ways they reproduce these patterns within the discipline and higher education.     

Making available information from studies sponsored by the pharmaceutical industry: some current practices

Since the web-based registry ClinicalTrials.gov was launched on 29 February 2000, the pharmaceutical industry has made available an increasing amount of information about the clinical trials that it sponsors. The process has been spurred on by a number of factors including a wish by the industry to provide greater transparency regarding clinical trial data; and has been both aided and complicated by the number of institutions that have a legitimate interest in guiding and defining what should be made available. This article reviews the history of this process of making information about clinical trials publicly available. It provides a reader's guide to the study registries and the databases of results; and looks at some indicators of consistency in the posting of study information.     

Of Riots and Racism: Fifty Years Since the Best Laid Schemes of the Kerner Commission (1968–2018)

“What happened? Why did it happen? What can be done to prevent it from happening again and again?” Voiced less that one week after the July 1967 race riots in Detroit, Michigan, Lyndon B. Johnson spoke these words as he ordered the establishment of the National Advisory Commission on Civil Disorders. Seven months later, on March 1, 1968, the Commission's account—known as the Kerner Commission Report—was a scathing appraisal of riots and racism in the United States. While it included bold language about the linkage between rioting and racism, it is rife with paradoxical assumptions and findings. Moreover, the report's failure to define sociological concepts, coupled with a reliance on individualism and cognitive attitudes via psycho‐analytic and pop‐psychological conjecture, together beckon scholars to wrestle with how this state‐issued report reflected and reproduced dominant assumptions about the “race” concept, violence, and human nature. Employing a critical content analysis of the report, I ask: How does the Kerner Commission Report define and use the concept of “riots” and “racism,” and what are the logics employed in the production of that knowledge?     

Indirect Inference for Lévy‐driven continuous‐time GARCH models

We advocate the use of an Indirect Inference method to estimate the parameter of a COGARCH(1,1) process for equally spaced observations. This requires that the true model can be simulated and a reasonable estimation method for an approximate auxiliary model. We follow previous approaches and use linear projections leading to an auxiliary autoregressive model for the squared COGARCH returns. The asymptotic theory of the Indirect Inference estimator relies on a uniform strong law of large numbers and asymptotic normality of the parameter estimates of the auxiliary model, which require continuity and differentiability of the COGARCH process with respect to its parameter and which we prove via Kolmogorov's continuity criterion. This leads to consistent and asymptotically normal Indirect Inference estimates under moment conditions on the driving Lévy process. A simulation study shows that the method yields a substantial finite sample bias reduction compared with previous estimators.     

Subgroup analysis and interpretation for phase 3 confirmatory trials: White paper of the EFSPI/PSI working group on subgroup analysis

Subgroup by treatment interaction assessments are routinely performed when analysing clinical trials and are particularly important for phase 3 trials where the results may affect regulatory labelling. Interpretation of such interactions is particularly difficult, as on one hand the subgroup finding can be due to chance, but equally such analyses are known to have a low chance of detecting differential treatment effects across subgroup levels, so may overlook important differences in therapeutic efficacy. EMA have therefore issued draft guidance on the use of subgroup analyses in this setting. Although this guidance provided clear proposals on the importance of pre‐specification of likely subgroup effects and how to use this when interpreting trial results, it is less clear which analysis methods would be reasonable, and how to interpret apparent subgroup effects in terms of whether further evaluation or action is necessary. A PSI/EFSPI Working Group has therefore been investigating a focused set of analysis approaches to assess treatment effect heterogeneity across subgroups in confirmatory clinical trials that take account of the number of subgroups explored and also investigating the ability of each method to detect such subgroup heterogeneity. This evaluation has shown that the plotting of standardised effects, bias‐adjusted bootstrapping method and SIDES method all perform more favourably than traditional approaches such as investigating all subgroup‐by‐treatment interactions individually or applying a global test of interaction. Therefore, these approaches should be considered to aid interpretation and provide context for observed results from subgroup analyses conducted for phase 3 clinical trials.     

Bootstrap-based bias corrections for INAR count time series

Integer-valued autoregressive (INAR) processes form a very useful class of processes suitable to model time series of counts. Several practically relevant estimators based on INAR data are known to be systematically biased away from their population values, e.g. sample autocovariances, sample autocorrelations, or the dispersion index. We propose to do bias correction for such estimators by using a recently proposed INAR-type bootstrap scheme that is tailor-made for INAR processes, and which has been proven to be asymptotically consistent under general conditions. This INAR bootstrap allows an implementation with and without parametrically specifying the innovations' distribution. To judge the potential of corresponding bias correction, we compare these bootstraps in simulations to several competitors that include the AR bootstrap and block bootstrap. Finally, we conclude with an illustrative data application.     

Merging Incomplete Tertiary Datasets: The Case of 2-1-1 Information and Referral Missing Data

This study proposes a method to handle missing data when merging large tertiary datasets. Combining 25 datasets of 2-1-1 Texas Information & Referral Network’s call records to analyze unmet needs during Hurricanes Katrina and Rita highlighted a considerable bias problem due to missing data of a key variable in some of the 25 datasets. First, extensive literature about existing techniques for handling missing data was reviewed but determined not applicable for this type of missing data problem. Next, a systematic algorithm was developed to calculate missing data types and strategies in tertiary datasets. Last, this method was applied to the 2-1-1 datasets to test its effectiveness on bias due to previous missing data. Using this approach, the volume of cases available for analysis was increased approximately 30 percent, hence greatly improving validity of the findings. In terms of social service research, minimizing bias of missing data in existing tertiary data resources would help policymakers make more appropriate decisions and provide more effective and timely social support and disaster services to residents. This new method could be applied to using tertiary data with a similar dilemma and contribute to increasing potential use of available public datasets.