Diagnostics of multiple group influential observations for logistic regression models

In this paper, two new multiple influential observation detection methods, GCD.GSPR and mCD*, are introduced for logistic regression. The proposed diagnostic measures are compared with the generalized difference in fits (GDFFITS) and the generalized squared difference in beta (GSDFBETA), which are multiple influential diagnostics. The simulation study is conducted with one, two and five independent variable logistic regression models. The performance of the diagnostic measures is examined for a single contaminated independent variable for each model and in the case where all the independent variables are contaminated with certain contamination rates and intensity. In addition, the performance of the diagnostic measures is compared in terms of the correct identification rate and swamping rate via a frequently referred to data set in the literature.     

“Who generates this city”? Socialist strategy in contemporary London

This essay, based on a “militant ethnography” of the attempts of the small radical grassroots activist group, Our London (a pseudonym), to mobilize a collective oppositional politics through activities around an election campaign, engages critically with E. Laclau and C. Mouffe's arguments on discourse and collectivity in Hegemony and Socialist Strategy (London: Verso, 1985). I argue, on the basis of my findings, that while their model does provide insights that help describe the process of building collectivity from among disparate perspectives and identities, we need to go beyond a focus on discourse alone and consider the ways politics is shaped by material contexts. This is necessary if we are to understand the continued appeal of class politics as well as the difficulties in mobilizing collectivity in highly unequal and fragmented cities. From an activist perspective, the essay also highlights how developing a conception of collective interests and a critique of overarching systems of exploitation can be important in building political unity.     

体育强国视域下体育治理的问题与出路

体育治理是国家治理体系和治理能力现代化的重要内容,直接关乎国家治理的时代进程。近年来,国家层面相继提出“体育强国”“健康中国”战略,如何通过有效的体育治理来实现体育强国战略,是推动我国体育事业改革的重要课题。目前我国体育治理目标定位与现实社会环境之间还存在偏差,体育治理组织形态在一定程度上也影响体育政策的执行,同时体育治理利益多元博弈对体育治理方式提出挑战。体育治理必须以满足人民群众的体育文化生活需求为根本导向,从治理理念、治理主体、治理结构、治理方式等层面进行深度改革,助力体育强国的实现。     

Identity Dilemmas: Toward a More Situated Understanding

When are identity dilemmas—when people possess identities that conflict with one another and both are potentially stigmatizing—most likely to occur? Are they the result of generic social processes? A review of some of the extant research on “identity work” suggests that historical “misalignments” of culture and stratification, which we refer to as “lag,” create the greatest potential for stigma and the reproduction of inequalities. Lag is exacerbated by complex, intersecting axes of hierarchy, and amplified as symbolic environments globalize and subcultures multiply. Articulating culture and structure reveals how power plays out in interaction, and highlights the omnipresence of struggles for treatment as “fully human.” We consider whether “alignment” is even possible when multiple dimensions of social location intertwine, compete, and collide. Following Schwalbe and Mason‐Schrock (1996), we argue that “subcultural” or collective identity work that brings new meanings into dominant cultural narratives may offer the greatest hope, but in the interim all coping strategies are costly.     

Ambidexterity penetration across multiple organizational levels in an aerospace and defense organization

The ambidexterity framework, which comprises two contradictory, yet interrelated processes of exploration and exploitation, has been researched using a variety of perspectives. Few studies, however, provide insight into the question: how is ambidexterity managed across multiple organizational levels? To address this question, we introduce the term ambidexterity penetration that refers to the enactment of ambidexterity across multiple organizational levels and develop a conceptual framework about how it is practiced (horizontally, vertically and organizationally). We empirically showcase this framework using findings from six business units of an aerospace and defense organization and analyzing data from 30 interviews. Overall, our study contributes to ambidexterity research and offers an empirical investigation of ambidexterity penetration across multiple organizational levels in the context of the aerospace and defense sector.     

Disappearing Dykes? Post-Lesbian Discourse and Shifting Identities and Communities

ABSTRACT

Researchers and activists have argued that the term “lesbian” has been disappearing, in decline for decades, and have focused on tensions around gender and sexual identities as the source of its decline. I identify “post-lesbian discourse” as key concept in the changing landscape of LGBTQ terminology, and I highlight four concerns that emerge in this discourse: shifting lesbian politics, a too-inclusive community, a loss of lesbian spaces, and the meaning of embodied lesbian identity. Rather than focusing solely on gender and sexuality, my aim is to explore the development of post-lesbian discourse and its effects on lesbian identities and communities.     

Navigating choices when applying multiple imputation in the presence of multi-level categorical interaction effects

Multiple imputation (MI) is an appealing option for handling missing data. When implementing MI, however, users need to make important decisions to obtain estimates with good statistical properties. One such decision involves the choice of imputation model–the joint modeling (JM) versus fully conditional specification (FCS) approach. Another involves the choice of method to handle interactions. These include imputing the interaction term as any other variable (active imputation), or imputing the main effects and then deriving the interaction (passive imputation). Our study investigates the best approach to perform MI in the presence of interaction effects involving two categorical variables. Such effects warrant special attention as they involve multiple correlated parameters that are handled differently under JM and FCS modeling. Through an extensive simulation study, we compared active, passive and an improved passive approach under FCS, as JM precludes passive imputation. We additionally compared JM and FCS techniques using active imputation. Performance between active and passive imputation was comparable. The improved passive approach proved superior to the other two particularly when the number of parameters corresponding to the interaction was large. JM without rounding and FCS using active imputation were also mostly comparable, with JM outperforming FCS when the number of parameters was large. In a direct comparison of JM active and FCS improved passive, the latter was the clear winner. We recommend improved passive imputation under FCS along with sensitivity analyses to handle multi-level interaction terms.     

A statistical procedure for representing state fragility and transition paths

State fragility is a concept that entered the political discourse in the last decades producing remarkable implications for aid allocation and international policies. The operationalization of this concept has generated a number of composite indices to produce rankings of fragile states. However, the temporal dimension of the driving forces leading to fragility has been rather neglected. This article discusses a statistical procedure that helps to represent the global fragility of a country and the path that a country has followed or will follow in the future when possibly entering into (or escaping from) a fragility condition. Specifically, multiple factor analysis is applied to depict vulnerable and weak countries, and to identify the fundamental forces that determine their overall fragility. Moreover, the trajectories of countries along the years are estimated using partial factor scores. Finally, the path of each country is predicted by means of parsimonious regression models, based on a reduced set of explanatory variables, and according to scenarios elaborated from available international outlooks.     

A Factor-Adjusted Multiple Testing Procedure With Application to Mutual Fund Selection

In this article, we propose a factor-adjusted multiple testing (FAT) procedure based on factor-adjusted p-values in a linear factor model involving some observable and unobservable factors, for the purpose of selecting skilled funds in empirical finance. The factor-adjusted p-values were obtained after extracting the latent common factors by the principal component method. Under some mild conditions, the false discovery proportion can be consistently estimated even if the idiosyncratic errors are allowed to be weakly correlated across units. Furthermore, by appropriately setting a sequence of threshold values approaching zero, the proposed FAT procedure enjoys model selection consistency. Extensive simulation studies and a real data analysis for selecting skilled funds in the U.S. financial market are presented to illustrate the practical utility of the proposed method. Supplementary materials for this article are available online.     

Testing the equality of two independent regression models

In some situations, for example in agriculture, biology, hydrology, and psychology, researchers wish to determine whether the relationship between response variable and predictor variables differs in two populations. In other words, we are interested in comparing two regression models for two independent datasets. In this work, we will use the parametric and nonparametric methods to establish hypothesis testing for the equality of two independent regression models. Then the simulation study is provided to investigate the performance of the proposed method.