Can Heterosexism Harm Organizations? Predicting the Perceived Organizational Citizenship Behaviors of Gay and Lesbian Employees

An initial test and validation of a model predicting perceived organizational citizenship behaviors (OCBs) of lesbian and gay employees were conducted using structural equation modeling. The proposed structural model demonstrated acceptable goodness of fit and structural invariance across 2 samples (ns = 311 and 295), which suggested that altruistic OCB performance is precipitated by workplace outness. Furthermore, stigmatization salience and organizational climate for heterosexism predict levels of workplace outness. Organizational climate for heterosexism is negatively related to stigmatization salience. When the model was tested on 2 samples, all structural paths except 1 were significant.     

The servant identity: Influences on the cognition and behavior of servant leaders

This paper approaches the study of servant leadership by concentrating on the identity of servant leaders. An identity is important to one's sense of self and it influences the way a leader cognitively processes socially relevant information and exercises a particular leadership behavior in response to a situation. Unlike existing studies, which typically approach servant leadership as one of a number of possible leadership styles, and which merely describe its characteristics, this paper serves to explain the basis for individuals enacting this mode of leadership. This paper defines and elaborates on the servant identity by showing the psychological factors constituting it. Taking a socio-cognitive approach, the paper offers a theoretical framework for the servant identity. The framework encompasses the cognitive and behavioral disposition of leaders with servant identity and the organizational contexts that influence it. This new angle on servant leadership opens avenues for future research and practice.     

On the asymptotic distribution of cramer-von mises one-sample test statistics under an alternative

The asymptotic normality of the Cramer-von Mises one-sample test statistic and one of its variants under an alternative cdf is demonstrated. The derivation herein is unique in that it does not require knowledge of the theory of weak convergence of probability measures defined on metrized function spaces, and thus is accessible to a broader class of students and practitioners.     

Estimation of mean using double sampling for stratification and multivariate auxiliary information

Several estimators for estimating the mean of a principal variable are proposed based on double sampling for stratification (DSS) and multivariate auxiliary information. The general properties of the proposed estimators are studied, search for optimum estimators is made and the proposed estimators are compared with the corresponding estimators based on unstratified double sampling (USDS).     

Recurrence relations for moments of different orders of extremes from gamma distribution

Certain recurrence relations for the moments of different orders of the largest order statistic from a gamma distribution with shape parameter p are obtained. By using this it is shown that for obtaining the moment of any order of each order statistic of a sample of size n from the gamma distribution, one has to evaluate at most n-2 single integrals.     

A Theory for Coloring Bivariate Statistical Maps

Consideration of some practical uses of statistical bivariate maps—for example, display of association between variables—leads to principles for making effective use of color to represent data values. Effective color schemes for bivariate maps are viewed as continuous transformations from color models to the unit square with appropriate restrictions involving hue, saturation, and brightness. Several schemes, including those used by the U.S. Census Bureau, are criticized on the basis of this theory.     

Automatic locally adaptive smoothing for tree-based set estimation

Tree-based methods similar to CART have recently been utilized for problems in which the main goal is to estimate some set of interest. It is often the case that the boundary of the true set is smooth in some sense, however tree-based estimates will not be smooth, as they will be a union of ‘boxes’. We propose a general methodology for smoothing such sets that allows for varying levels of smoothness on the boundary automatically. The method is similar to the idea underlying support vector machines, which is applying a computationally simple technique to data after a non-linear mapping to produce smooth estimates in the original space. In particular, we consider the problem of level-set estimation for regression functions and the dyadic tree-based method of Willett and Nowak [Minimax optimal level-set estimation, IEEE Trans. Image Process. 16 (2007), pp. 2965–2979].     

k-POD: A Method for k-Means Clustering of Missing Data

The k-means algorithm is often used in clustering applications but its usage requires a complete data matrix. Missing data, however, are common in many applications. Mainstream approaches to clustering missing data reduce the missing data problem to a complete data formulation through either deletion or imputation but these solutions may incur significant costs. Our k-POD method presents a simple extension of k-means clustering for missing data that works even when the missingness mechanism is unknown, when external information is unavailable, and when there is significant missingness in the data.

[Received November 2014. Revised August 2015.]