Disputing Irrational Beliefs Among Convicted Terrorists and Extremist Beliefs

This study investigated the most common irrational beliefs among samples of: (1) convicted terrorists and (2) extremists, all of whom were prisoners in the country of Jordan. It also investigated the effectiveness of a program of disputing irrational beliefs (DIBP) with the same populations. The subjects were assigned randomly to two groups: an experimental group (EG) that consisted of 43 prisoners who received an training program, and a control group that consisted of 48 prisoners who received no training. The irrational beliefs scale (IBS) scores were assessed for both groups before and after exposure to the DIBP. To test the hypotheses of the study, means and standard deviations of the IBS measures were submitted to a two-way analysis of covariance. The results revealed that the most common irrational belief loaded on the factors of musts, exaggerations, and perfectionism. Moreover, the results revealed significant differences between the means of the two groups on the total IBS scores at post-test, with the EG scoring lower, which indicated the effectiveness of the training program in reducing the level of IBS. The results yielded significant differences in the effect of the reason for imprisonment in favor of extremists and the interaction between the reason for prison and treatment in favor of the EG and extremists.     

Comparing What to What,on What Scale? The Impact of Item Comparisons and Reference Points in Communicating Risk and Uncertainty

The mandated public availability of individual hospital's audit data for children's heart surgery in the UK creates a challenging scenario for communicating these complex and sensitive data to diverse audiences. On the basis of this scenario, we conducted three experiments with the aim of understanding how best to help lay people understand these data and the practical goal of improving the public presentation of these data. The experiments compared different outcome measures for displaying the survival rate (percentage scale versus the ratio of the predicted/observed rates) and presentation formats (individual hospital versus all hospitals shown) for outcomes data presented relative to prediction intervals generated by a risk model that adjusts for case mix. Our data highlight how easily inappropriate comparisons can influence evaluations of complex data: for instance, both a survival ratio of 1 and the presence of other hospitals seemingly provided reference points that resulted in inappropriately harsh evaluations of some hospitals. By drawing on evaluability theory, we demonstrate how to enhance people's understanding of these complex data while also discouraging inappropriate comparisons, which has implications for communicating risk and uncertainty and for choice architecture design in a range of contexts. Copyright © 2018 John Wiley & Sons, Ltd.     

The Relationship Between Automatic Thoughts and Irrational Beliefs Predicting Anxiety and Depression

Cognitive behavioral approaches differ in their views on core cognitions and their hypothesized role in the etiology of depression and anxiety. The present study provides empirical evidence regarding the relationship between irrational beliefs and components of automatic thoughts and their role in the etiology of depression and anxiety. The present study utilized newer and improved questionnaires to assess components of irrational belief. Based on prior research by Safren et al. (Cogn Ther Res 24(3):327–344, 2000), a three-factor structure of the combined automatic thought questionnaires were utilized to measure components of automatic thoughts as they relate to depression and anxiety. Factor analytical methods were utilized to confirm the factor structure of the irrational beliefs and automatic thoughts components. Advanced path modeling was utilized to model the relationship between irrational beliefs and automatic thoughts in predicting anxiety and depression. The study used a sample of N = 542 undergraduate psychology students during stressful exam times. Results indicated that the irrational belief Demandingness represents a primary factor, followed by the secondary irrational beliefs as proposed by Rational Emotive Behavioral Theory. Selfdowning beliefs were fully mediated by depressive automatic thoughts in the case of depressive affect. Low frustration tolerance contributed unique variance to anxious and depressive affect that was not fully mediated by automatic thoughts. Results from the present study add empirical evidence that irrational beliefs indeed represent core and intermediary beliefs that lead to specific automatic thoughts, which is congruent with cognitive behavioral theory as proposed by Rational Emotive Behavioral Therapy.     

The Madonna-Whore Dichotomy: Men Who Perceive Women's Nurturance and Sexuality as Mutually Exclusive Endorse Patriarchy and Show Lower Relationship Satisfaction

The Madonna-Whore Dichotomy (MWD) denotes polarized perceptions of women in general as either “good,” chaste, and pure Madonnas or as “bad,” promiscuous, and seductive whores. Whereas prior theories focused on unresolved sexual complexes or evolved psychological tendencies, feminist theory suggests the MWD stems from a desire to reinforce patriarchy. Surveying 108 heterosexual Israeli men revealed a positive association between MWD endorsement and patriarchy-enhancing ideology as assessed by Social Dominance Orientation (preference for hierarchical social structures), Gender-Specific System Justification (desire to maintain the existing gender system), and sexist attitudes (Benevolent and Hostile Sexism, Sexual Objectification of Women, and Sexual Double Standards). In addition, MWD endorsement negatively predicted men’s romantic relationship satisfaction. These findings support the feminist notion that patriarchal arrangements have negative implications for the well-being of men as well as women. Specifically, the MWD not only links to attitudes that restrict women’s autonomy, but also impairs men’s most intimate relationships with women. Increased awareness of motives underlying the MWD and its psychological costs can help practice professionals (e.g., couple therapists), as well as the general public, to foster more satisfying heterosexual relationships.     

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.     

Stereotype Threat Effects on Learning From a Cognitively Demanding Mathematics Lesson

Stereotype threat—a situational context in which individuals are concerned about confirming a negative stereotype—is often shown to impact test performance, with one hypothesized mechanism being that cognitive resources are temporarily co‐opted by intrusive thoughts and worries, leading individuals to underperform despite high content knowledge and ability (see Schmader & Beilock, 2012 ). We test here whether stereotype threat may also impact initial student learning and knowledge formation when experienced prior to instruction. Predominantly African American fifth‐grade students provided either their race or the date before a videotaped, conceptually demanding mathematics lesson. Students who gave their race retained less learning over time, enjoyed the lesson less, reported a diminished desire to learn more, and were less likely to choose to engage in an optional math activity. The detrimental impact was greatest among students with high baseline cognitive resources. While stereotype threat has been well documented to harm test performance, the finding that effects extend to initial learning suggests that stereotype threat's contribution to achievement gaps may be greatly underestimated.     

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.