The utilization of prevailing evaluation policy in the selection of performance measures

This study considers the problem of inferring a functional evaluation criterion from existing performance measure policies. The objective is to develop a rational basis by which to adaptively evaluate a current best set of performance measures from alternative sets. The analysis is based on a matrix approach for relating system characteristics to performance measures across a set of activities. A procedure for ranking measure combinations which are candidate implementation alternatives is devised using this matrix representation and cost information. An example illustrating the procedure is presented.     

Latin hypercube sampling with multidimensional uniformity

Complex models can only be realized a limited number of times due to large computational requirements. Methods exist for generating input parameters for model realizations including Monte Carlo simulation (MCS) and Latin hypercube sampling (LHS). Recent algorithms such as maximinLHS seek to maximize the minimum distance between model inputs in the multivariate space. A novel extension of Latin hypercube sampling (LHSMDU) for multivariate models is developed here that increases the multidimensional uniformity of the input parameters through sequential realization elimination. Correlations are considered in the LHSMDU sampling matrix using a Cholesky decomposition of the correlation matrix. Computer code implementing the proposed algorithm supplements this article. A simulation study comparing MCS, LHS, maximinLHS and LHSMDU demonstrates that increased multidimensional uniformity can significantly improve realization efficiency and that LHSMDU is effective for large multivariate problems.     

Using the Shapley Decomposition to Disentangle the Impact of Circumstances and Efforts on Health Inequality

Following previous work on inequality in health opportunity, this paper attempts to determine the contribution of circumstances, efforts and lifestyle, and demographic variables to the overall inequality in health in Luxembourg. Health is measured via the answers given to a question on self-assessed health and is considered as an ordinal variable. The educational level of each parent, the financial situation of the family during childhood, the area of birth of the individual, and of his/her parents, and the year of immigration are considered as circumstances. Effort and lifestyle variables are proxied by information on the educational level of the individual, whether he/she smoked and whether he/she had a physical activity on a regular basis. Sex and age are considered as demographic variables. To solve the issue of correlation between circumstances and effort and lifestyle the estimation is implemented in two stages. In the first stage, each effort and lifestyle variable is regressed in a separate equation against the vector of circumstances and demographic variables and, in a second stage, the individual health status is regressed against the vector of circumstances and demographic variables along with the estimated residuals of the effort equations described previously. The respective impacts of the three categories of explanatory variables (circumstances, effort and lifestyle and demographic variables) on health inequality are derived via a Shapley decomposition of the likelihood ratio of the health ordered logit regression. It appears that differences in circumstances and effort and lifestyle explain each around a quarter of this likelihood ratio.     

Gender differences in faculty attrition in the USA

In this study, we examined possible gender differences in reasons for faculty attrition during the last 20 years at a small women's liberal arts college. The attrition rate was calculated from archival data collected through old college directories. As in earlier studies, the attrition rate was higher for women (31%) than for men (18.5%). Reasons for leaving the college were assessed in an online survey completed by 45 out of 66 tenure-track or tenured professors who left (excluding retirees). Work–family conflict was the most frequently cited reason for leaving, with disproportionately more women than men giving this reason. Open-ended responses revealed that work–family conflict typically referred to the difficulty of coordinating two careers. Female faculty who left were also significantly more dissatisfied than males with the support for balancing work and family life at the college. The need for colleges to create spousal policies to help faculty coordinate dual careers was discussed.     

The Radical Perspective in Sociology

The objective of this paper is to clarify some of the perspectives of the discipline of sociology, to identify radical viewpoints and how they have evolved and converged, and to indicate some of the consequences of a radical sociology and its prospects. A radical sociology calls for a radical perspective, namely, a structural perspective. Such a perspective requires uncovering economic and political realities in the attempt to understand social structure. Intellectual relevance should not merely be cloaked in terms of efficacy demands which derive from state welfarism and militarism, but should be the extension of a radical and structuralist perspective.     

"I like to treat others as others would treat me": the development of prosocial selves in an urban youth organization

The development of moral identity is linked to a sense of self that is prosocial and connected to others. Youth organizations, if designed appropriately, may provide a setting for social interactions and relationships in which youth can enact and receive validation for moral behaviors and develop prosocial selves. This chapter reports on findings from a four-year study of identity construction within an urban Boys and Girls Club. The author conducted interviews and photography projects with seventeen youth ages twelve to eighteen, all of whom were active club members. Over half described their race or ethnicity as black or African American, while others were Hispanic, Afro-Latino, white, or other. All of them either lived in the housing project near the club or had close ties to it. Both boys and girls describe themselves as rooted in a rich relational milieu that promoted prosocial identities. The importance of respect emerged as a key theme in the teens' narratives about themselves and their activities. The author found that the club served as a site for the development of prosocial traits in an environment characterized by respectful and supportive relationships.     

Max‐infinitely divisible models and inference for spatial extremes

For many environmental processes, recent studies have shown that the dependence strength is decreasing when quantile levels increase. This implies that the popular max‐stable models are inadequate to capture the rate of joint tail decay, and to estimate joint extremal probabilities beyond observed levels. We here develop a more flexible modeling framework based on the class of max‐infinitely divisible processes, which extend max‐stable processes while retaining dependence properties that are natural for maxima. We propose two parametric constructions for max‐infinitely divisible models, which relax the max‐stability property but remain close to some popular max‐stable models obtained as special cases. The first model considers maxima over a finite, random number of independent observations, while the second model generalizes the spectral representation of max‐stable processes. Inference is performed using a pairwise likelihood. We illustrate the benefits of our new modeling framework on Dutch wind gust maxima calculated over different time units. Results strongly suggest that our proposed models outperform other natural models, such as the Student‐t copula process and its max‐stable limit, even for large block sizes.     

Effects of a single outlier on arma identification

Fox (1972), Box and Tiao (1975), and Abraham and Box (1979) have proposed methods for detecting outliers in time series whose ARMA form is known (or identified). We show that the existence of a single aberrant observation, innovation, or intervention causes an ARMA model to be misidentified using unadjusted autocorrelation (acf) and partial autocorrelation estimates. The magnitude, location, type of outlier, and in some cases the ARMA's parameters, affect the identification outcome. We use variance inflation, signal-to-noise ratios, and acf critical values to determine an ARMA model's susceptibility to misidentifi-cation. Numerical and simulation examples suggest how to iteratively use the outlier detection methods in practice.