Gender Bias Produces Gender Gaps in STEM Engagement

We explored whether the existence of gender bias causes gender gaps in STEM engagement. In Experiment 1 (n?=?322), U.S. women projected less sense of belonging, positivity toward, and aspirations to participate in STEM than did men when exposed to the reality of STEM gender bias. These gender differences disappeared when participants were told that STEM exhibits gender equality, suggesting that gender bias produces STEM gender gaps. Experiment 2 (n?=?429) explored whether results generalized to a specific STEM department, and whether organizational efforts to mitigate gender bias might shrink gender gaps. U.S. women exposed to a biased chemistry department anticipated more discrimination and projected less sense of belonging, positive attitudes and trust and comfort than did men. These gender differences vanished when participants read about an unbiased department, again suggesting that gender bias promotes STEM gender gaps. Further, moderated mediation analyses suggested that in the presence of gender bias (but not gender equality), women projected less positive attitudes and trust and comfort than did men because they experienced less sense of belonging and anticipated more discrimination. Results were largely unaffected by whether departments completed a diversity training, suggesting that knowledge of diversity initiatives alone cannot close STEM gender gaps.     

Score-Based Tests of Differential Item Functioning via Pairwise Maximum Likelihood Estimation

Measurement invariance is a fundamental assumption in item response theory models, where the relationship between a latent construct (ability) and observed item responses is of interest. Violation of this assumption would render the scale misinterpreted or cause systematic bias against certain groups of persons. While a number of methods have been proposed to detect measurement invariance violations, they typically require advance definition of problematic item parameters and respondent grouping information. However, these pieces of information are typically unknown in practice. As an alternative, this paper focuses on a family of recently proposed tests based on stochastic processes of casewise derivatives of the likelihood function (i.e., scores). These score-based tests only require estimation of the null model (when measurement invariance is assumed to hold), and they have been previously applied in factor-analytic, continuous data contexts as well as in models of the Rasch family. In this paper, we aim to extend these tests to two-parameter item response models, with strong emphasis on pairwise maximum likelihood. The tests’ theoretical background and implementation are detailed, and the tests’ abilities to identify problematic item parameters are studied via simulation. An empirical example illustrating the tests’ use in practice is also provided.     

Automated Item Generation with Recurrent Neural Networks

Utilizing technology for automated item generation is not a new idea. However, test items used in commercial testing programs or in research are still predominantly written by humans, in most cases by content experts or professional item writers. Human experts are a limited resource and testing agencies incur high costs in the process of continuous renewal of item banks to sustain testing programs. Using algorithms instead holds the promise of providing unlimited resources for this crucial part of assessment development. The approach presented here deviates in several ways from previous attempts to solve this problem. In the past, automatic item generation relied either on generating clones of narrowly defined item types such as those found in language free intelligence tests (e.g., Raven’s progressive matrices) or on an extensive analysis of task components and derivation of schemata to produce items with pre-specified variability that are hoped to have predictable levels of difficulty. It is somewhat unlikely that researchers utilizing these previous approaches would look at the proposed approach with favor; however, recent applications of machine learning show success in solving tasks that seemed impossible for machines not too long ago. The proposed approach uses deep learning to implement probabilistic language models, not unlike what Google brain and Amazon Alexa use for language processing and generation.     

Nonlinear Predictive Models for Multiple Mediation Analysis: With an Application to Explore Ethnic Disparities in Anxiety and Depression Among Cancer Survivors

Mediation analysis allows the examination of effects of a third variable (mediator/confounder) in the causal pathway between an exposure and an outcome. The general multiple mediation analysis method (MMA), proposed by Yu et al., improves traditional methods (e.g., estimation of natural and controlled direct effects) to enable consideration of multiple mediators/confounders simultaneously and the use of linear and nonlinear predictive models for estimating mediation/confounding effects. Previous studies find that compared with non-Hispanic cancer survivors, Hispanic survivors are more likely to endure anxiety and depression after cancer diagnoses. In this paper, we applied MMA on MY-Health study to identify mediators/confounders and quantify the indirect effect of each identified mediator/confounder in explaining ethnic disparities in anxiety and depression among cancer survivors who enrolled in the study. We considered a number of socio-demographic variables, tumor characteristics, and treatment factors as potential mediators/confounders and found that most of the ethnic differences in anxiety or depression between Hispanic and non-Hispanic white cancer survivors were explained by younger diagnosis age, lower education level, lower proportions of employment, less likely of being born in the USA, less insurance, and less social support among Hispanic patients.     

Combining Indian and Western Spiritual Psychology: Applications to Health and Social Renewal

We introduce the topic of Indian/US collaboration in studying religion and spirituality (R/S), providing both historical and philosophical context. A fully in-depth and comprehensive study of R/S will require taking into account three potential types and sources of knowledge: (1) theoretically and intellectually oriented knowledge as exemplified by William James; (2) empirically oriented knowledge as exemplified by Gordon Allport; and (3) experience- and realization-derived knowledge based on reports from eminent spiritual figures. We sketch the distinct but mutually informative challenges of clinical and epistemic integration. We suggest several topics for future collaboration, arguing that the most comprehensive study of R/S will require combining the western attention to empirical knowledge with the Indian psychology movement’s attention to realization-derived knowledge. Suggested topics include processes of learning from spiritual exemplars and teachers; health effects from engagement in Hindu or other Indian religious practice; psychological and health effects of specific spiritual practices; the importance and influence of the focus used in meditation; the effects of japam, Ramnam, or similar repeated short prayers; how diverse spiritual practices affect mindfulness; and the nature and means of fostering skills for living in a religiously plural society.     

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).