Has Society Grown More Hostile Towards Conservative Christians? Evidence from ANES Surveys

Several conservative Christian activists have complained of increasing hatred directed towards them and a majority of Christians today believe that persecution against them has increased in the United States. To date there has not been an empirical assessment of whether there are increasing levels of anti-Christian animosity with two groups as representative of conservative Christians: Christian fundamentalists and evangelicals. Levels of anti-Christian hostility has not significantly risen over the past few decades, however those with this hostility have become wealthier. Furthermore, there has not been a coalescing or lessening of support for conservative Christians by other Christians, as seen in the persistent level of mildly negative score by progressive Christians. One may envision hostility against conservative Christians in the twentieth century as something possessed by highly educated progressives with cultural influence but little ability to punish conservative Christians economically. The ability to threaten conservative Christians’ material well-being and the lack of support of conservative Christians by other Christians augments those with hostility towards conservative Christians in the twenty-first century. Conservative Christians are incorrect asserting that Christian hostility recently dramatically increased but may be correct in asserting that they face more problems due to that hostility.     

Seventh-day Adventist Clergy: Understanding Stressors and Coping Mechanisms

This article examines the extent and types of clergy stress, the strategies used in coping with stressors, and the relationship between stressors and coping mechanisms in a sample of 261 Seventh-day Adventist pastors in North America. The results indicate that the most commonly reported stressors in order are: (1) lack of social support, (2) financial stress, and (3) time and workload stress. In terms of coping strategies, pastors sought relief most often through: (1) reflective growth/internal change; (2) social/emotional coping; (3) passive coping; and lastly (4) action-oriented coping. Significant correlational relationships existed between passive coping and financial stress, relocating stress, and congregational stress. In addition, we found significant inverse correlations between coping through reflective growth or internal change and relocating stress and congregational stress. There were no significant relationships with action coping or social/emotional coping and any stressor. Multi-regression analysis reveals that passive coping strategies were significantly related to financial stress. Thus, the greater the financial stress, the more likely pastors were to engage in passive coping strategies. Other coping strategies showed no significant relationships when included in multi-regression analysis. We conclude with recommendations for Church administrators to address structures and practices in place for pastors including an expansion of coping mechanisms to help pastors address their stress.     

On Fair Person Classification Based on Efficient Factor Score Estimates in the Multidimensional Factor Analysis Model

Since Hooker, Finkelman and Schwartzman (Psychometrika 74(3): 419–442, 2009) it is known that person parameter estimates from multidimensional latent variable models can induce unfair classifications via paradoxical scoring effects. The open question as to whether there is a fair and at the same time multidimensional scoring scheme with adequate statistical properties is addressed in this paper. We develop a theorem on the existence of a fair, multidimensional classification scheme in the context of the classical linear factor analysis model and show how the computation of the scoring scheme can be embedded in the context of linear programming. The procedure is illustrated in the framework of scoring the Wechsler Adult Intelligence Scale (WAIS-IV).     

Generating Multivariate Ordinal Data via Entropy Principles

When conducting robustness research where the focus of attention is on the impact of non-normality, the marginal skewness and kurtosis are often used to set the degree of non-normality. Monte Carlo methods are commonly applied to conduct this type of research by simulating data from distributions with skewness and kurtosis constrained to pre-specified values. Although several procedures have been proposed to simulate data from distributions with these constraints, no corresponding procedures have been applied for discrete distributions. In this paper, we present two procedures based on the principles of maximum entropy and minimum cross-entropy to estimate the multivariate observed ordinal distributions with constraints on skewness and kurtosis. For these procedures, the correlation matrix of the observed variables is not specified but depends on the relationships between the latent response variables. With the estimated distributions, researchers can study robustness not only focusing on the levels of non-normality but also on the variations in the distribution shapes. A simulation study demonstrates that these procedures yield excellent agreement between specified parameters and those of estimated distributions. A robustness study concerning the effect of distribution shape in the context of confirmatory factor analysis shows that shape can affect the robust \(\chi ^2\) and robust fit indices, especially when the sample size is small, the data are severely non-normal, and the fitted model is complex.     

Psychometric Models of Small Group Collaborations

The social combination theory of group problem solving is used to extend existing psychometric models to collaborative settings. A model for pairwise group work is proposed, the implications of the model for assessment design are considered, and its estimation is addressed. The results are illustrated with an empirical example in which dyads work together on a twelfth-grade level mathematics assessment. In conclusion, attention is given to avenues of research that seem most fruitful for advancing current initiatives concerning the assessment of collaboration, teamwork, and related constructs.     

Outliers and Influential Observations in Exponential Random Graph Models

We discuss measuring and detecting influential observations and outliers in the context of exponential family random graph (ERG) models for social networks. We focus on the level of the nodes of the network and consider those nodes whose removal would result in changes to the model as extreme or “central” with respect to the structural features that “matter”. We construe removal in terms of two case-deletion strategies: the tie-variables of an actor are assumed to be unobserved, or the node is removed resulting in the induced subgraph. We define the difference in inferred model resulting from case deletion from the perspective of information theory and difference in estimates, in both the natural and mean-value parameterisation, representing varying degrees of approximation. We arrive at several measures of influence and propose the use of two that do not require refitting of the model and lend themselves to routine application in the ERGM fitting procedure. MCMC p values are obtained for testing how extreme each node is with respect to the network structure. The influence measures are applied to two well-known data sets to illustrate the information they provide. From a network perspective, the proposed statistics offer an indication of which actors are most distinctive in the network structure, in terms of not abiding by the structural norms present across other actors.