Editorial

AStA Advances in Statistical Analysis -     

Broadening gender self-categorization development to include transgender identities

Research on gender in psychology has increasingly affirmed diversity in gender, with recognition of binary and nonbinary transgender experiences. However, gender has been presumed to be cisgender (congruent with sex) and binary (either male or female) in classical developmental theories. In response, this review engages in a critical analysis of classical gender development theory informed by findings about gender from research on transgender identities. Under analysis was the concept of gender self-categorization in classical theories of gender development including cognitive developmental theory, developmental gender schema theory, and social cognitive theory. Novel theoretical approaches are then outlined to situate recent advancements alongside classical theory. Conclusions are then drawn with brief recommendations for research methods that aim to include binary and nonbinary transgender participants. Drawing on the findings of this review it has been shown that gender self-categorization is often implicitly presumed to be cisgender identification. To overcome this bias, it is suggested that greater attention to specific gender self-categorization mechanisms are needed to open opportunities to include transgender experiences.     

Modeling immunogenecity data to establish screening bioassays cut point

The response of immunogenecity anti-drug antibody (ADA) generally includes biological and analytical variability. The nature of biological and analytical variations may lead to a variety of symmetric and asymmetric ADA data. As a result, current statistical methods may yield unreliable results because these methods assume special types of symmetric or asymmetric ADA data. In this paper, we survey and compare parametric models that are useful for analyzing a variety of asymmetric data that have rarely been used to calculate assay cut points. These models include symmetric distributions as limiting case; therefore, they are useful in the analysis of a variety of symmetric data. We also investigate two nonparametric approaches that have received little attention in screening cut point calculations. A simulation study was conducted to compare the performance of the methods. We evaluate the methods using four published different types of data, and make recommendations concerning the use of the methods.     

Retrospective accounts of first exposure to minoritized sexual and gender identities

Although there has been considerable research about attitudes towards LGBTQ+ people, there has been little research into how people first come to be aware of minoritized sexual and gender minority (SGM) identities. This study sought to address this gap. A sample of sexual minority (n = 150) and heterosexual (n = 802) young adults (N = 952; M_age = 18.88 years, SD = 1.75; 949 were cisgender, three were transgender), primarily recruited from a large southern university, were asked retrospectively to recount their first exposure to or awareness of SGM identities. Responses between SGM and heterosexual participants were compared through a variety of analytical approaches, including analyzing themes about the source from which participants first recalled encountering these identities, and whether understanding about these identities came through a personal connection to someone with these identities. SGM participants reported encountering minoritized sexual identities a year earlier than did heterosexual participants, with both groups encountering these concepts in middle childhood, on average. SGM participants were more likely than heterosexual participants to report learning about minoritized gender identities from someone with a minoritized gender identity, while heterosexual participants more often reported learning about these identities from media or celebrities. Heterosexual (vs. SGM) participants were also more likely to imply that minoritized gender identities were adopted to be popular, rather than being authentic identities in themselves. Framed by developmental intergroup theory (DIT), we discuss implications of these findings, especially potential interrelationships with the development of prejudiced attitudes about SGM identities.     

Beyond (But Including) the CEO: Diffusing Corporate Social Responsibility throughout the Organization through Social Networks

Chief Executive Officers and other organizational leaders can affect how corporate social responsibility initiatives are perceived in their organizations. However, in order to be successful with regard to promoting CSR, leaders need to have strong network competencies and to move beyond charismatic leadership. In this paper we offer a critique of charismatic leadership as it relates to CSR, posit that the intellectual stimulation brought about by transformational leadership is more important in this regard, propose that internal and networking is a leadership competence highly relevant to CSR, and emphasize the importance of working through highly credible opinion leaders in promoting CSR.     

Complexity as a Contract Design Factor: A Human‐to‐Human Experimental Study

Despite being theoretically suboptimal, simpler contracts (such as price‐only contracts and quantity discount contracts with limited number of price blocks) are commonly preferred in practice. Thus, exploring the tension between theory and practice regarding complexity and performance in contract design is especially relevant. Using human subject experiments, Kalkancı et al. (2011) showed that such simpler contracts perform effectively for a supplier interacting with a computerized buyer under asymmetric demand information. We use a similar set of experiments with the modification that a human supplier interacts with a human buyer. We show that human interactions strengthen the supplier's preference for simpler contracts. We find that suppliers have fairness concerns even when they interact with computerized buyers. These fairness concerns tend to be even stronger when suppliers interact with human buyers, particularly when the complexity of the contract is low. We also find that suppliers are more prone to random decision errors (i.e., bounded rationality) when interacting with human buyers. In the absence of social preferences, Kalkancı et al. identified reinforcement and bounded rationality as key biases that impact suppliers' decisions. In human‐to‐human experiments, we find evidence for social preference effects. However, these effects may be secondary to bounded rationality.