God of the hinge: treating LGBTQIA patients

This paper looks at systems of gender within the context of analysis. It explores the unique challenges of individuation faced by transsexual, transgender, gender queer, gender non‐conforming, cross‐dressing and intersex patients. To receive patients generously we need to learn how a binary culture produces profound and chronic trauma. These patients wrestle with being who they are whilst simultaneously receiving negative projections and feeling invisible. While often presenting with the struggles of gender conforming individuals, understanding the specifically gendered aspect of their identity is imperative. An analyst's unconscious bias may lead to iatrogenic shaming. The author argues that rigorous, humble inquiry into the analyst's transphobia can be transformative for patient, analyst, and the work itself. Analysis may, then, provide gender‐variant patients with their first remembered and numinous experience of authentic connection to self. Conjuring the image of a hinge, securely placed in the neutral region of a third space, creates a transpositive analytic temenos. Invoking the spirit of the Trickster in the construction of this matrix supports the full inclusion of gender‐variant patients. Nuanced attunement scaffolds mirroring and the possibility of play. Being mindful that gender is sturdy and delicate as well as mercurial and defined enriches the analyst's listening.     

A Monte Carlo evaluation of masked visual analysis in response‐guided versus fixed‐criteria multiple‐baseline designs

We developed masked visual analysis (MVA) as a structured complement to traditional visual analysis. The purpose of the present investigation was to compare the effects of computer‐simulated MVA of a four‐case multiple‐baseline (MB) design in which the phase lengths are determined by an ongoing visual analysis (i.e., response‐guided) versus those in which the phase lengths are established a priori (i.e., fixed criteria). We observed an acceptably low probability (less than .05) of false detection of treatment effects. The probability of correctly detecting a true effect frequently exceeded .80 and was higher when: (a) the masked visual analyst extended phases based on an ongoing visual analysis, (b) the effects were larger, (c) the effects were more immediate and abrupt, and (d) the effects of random and extraneous error factors were simpler. Our findings indicate that MVA is a valuable combined methodological and data‐analysis tool for single‐case intervention researchers.     

Estimating Incremental Validity Under Missing Data

A common form of missing data is caused by selection on an observed variable (e.g., Z). If the selection variable was measured and is available, the data are regarded as missing at random (MAR). Selection biases correlation, reliability, and effect size estimates when these estimates are computed on listwise deleted (LD) data sets. On the other hand, maximum likelihood (ML) estimates are generally unbiased and outperform LD in most situations, at least when the data are MAR. The exception is when we estimate the partial correlation. In this situation, LD estimates are unbiased when the cause of missingness is partialled out. In other words, there is no advantage of ML estimates over LD estimates in this situation. We demonstrate that under a MAR condition, even ML estimates may become biased, depending on how partial correlations are computed. Finally, we conclude with recommendations about how future researchers might estimate partial correlations even when the cause of missingness is unknown and, perhaps, unknowable.     

Sensitive Topics,Missing Data,and Refusal in Social Network Studies: An Ethical Examination

We describe our ethics‐driven process of addressing missing data within a social network study about accountability for racism, classism, sexism, heterosexism, cis‐sexism, ableism, and other forms of oppression among social justice union organizers. During data collection, some would‐be participants did not return emails and others explicitly refused to engage in the research. All refusals came from women of color. We faced an ethical dilemma: Should we continue to seek participation from those who had not yet responded, with the hopes of recruiting more women of color from within the network so their perspectives would not be tokenized? Or, should we stop asking those who had been contacted multiple times, which would compromise the social network data and analysis? We delineate ways in which current discussions of the ethics of social network studies fell short, given our framework and our community psychology (CP ) values. We outline literature that was helpful in thinking through this challenge; we looked outside of CP to the decolonization literature on refusal. Lessons learned include listening for the possible meanings of refusals and considering the level of engagement and the labor required of participants when designing research studies.     

Explaining White Opposition to Black Political Leadership: The Role of Fear of Racial Favoritism

Despite the election of America's first Black president, most non‐Hispanic Whites continue to oppose Black political leadership. The conventional explanation for White opposition is sheer racial prejudice, yet the available empirical evidence for this theory is inconsistent. I test an alternative theory that Whites perceive Black political leaders as a threat to their group's interests. Using a new survey measure and nationally representative panel data covering the 2008, 2010, and 2012 U.S. elections, I find that a majority of Whites perceive Black elected officials as likely to favor Blacks over Whites. Moreover, fear of racial favoritism predicts support for Barack Obama in both cross‐sectional models and fixed‐effects models of within‐person change, controlling for negative racial stereotypes. I replicate these findings using a separate cross‐sectional survey fielded after the 2014 election that controls for racial resentment. Collectively, these results suggest that perceptions of conflicting group interests—and not just prejudice—drive White opposition to Black political leadership.     

The Interdisciplinarity of Collaborations in Cognitive Science

We introduce a new metric for interdisciplinarity, based on co‐author publication history. A published article that has co‐authors with quite different publication histories can be deemed relatively “interdisciplinary,” in that the article reflects a convergence of previous research in distinct sets of publication outlets. In recent work, we have shown that this interdisciplinarity metric can predict citations. Here, we show that the journal Cognitive Science tends to contain collaborations that are relatively high on this interdisciplinarity metric, at about the 80th percentile of all journals across both social and natural sciences. Following on Goldstone and Leydesdorff (2006), we describe how scientometric tools provide a valuable means of assessing the role of cognitive science in broader scientific work, and also as a tool to investigate teamwork and distributed cognition. We describe how data‐driven metrics of this kind may facilitate this exploration without relying upon rapidly changing discipline and topic keywords associated with publications.