‘A mockery of equality’: an exploratory investigation into disabled activists’ views of the Paralympic Games

This article offers an exploratory analysis of the opinions of disabled activists towards the Paralympic Games. With the use of a qualitative online survey, the work focuses on the perceptions of disabled individuals (n = 32) who are not Paralympic athletes but are affiliated to the disability rights group, the United Kingdom Disabled People’s Council. Working on the premise that the views of disabled activists have been excluded from Paralympic sports discourse to date, the results illustrate a nuanced yet negative view of the Games to contrast with an existing, yet overly positive, academic narrative. Participants were particularly cynical of the portrayal and production of the Games and its Paralympic athletes as they perceived that the wider population of disabled people is misrepresented. The overwhelming perception in this preliminary analysis suggests that the Paralympic Games can be counterproductive to disability rights beyond sport.     

Estimation of proportions and ratios from line transect data

If the population of interest in a line transect survey consists of distinct sub-populations, or classes, line transect data can be used to estimate the proportion of the population in each class, or the ratio of the number of individuals in one class to another class. However, if the various classes have unequal overall detectability then the observed ratio or proportion is biased and should be adjusted. Estimators for the desired proportions and ratios are proposed and studied, and procedures for examining the equal detectability hypotheses are discussed.     

Partially pooled covariance matrix estimation in discriminant analysis

The Linear Discriminant Rule (LD) is theoretically justified for use in classification when the population within-groups covariance matrices are equal, something rarely known in practice. As an alternative, the Quadratic Discriminant Rule (QD) avoids assuming equal covariance matrices, but requires the estimation of a large number of parameters. Hence, the performance of QD may be poor if the training set sizes are small or moderate. In fact, simulation studies have shown that in the two-groups case LD often outperforms QD for small training sets even when the within -groups covariance matrices differ substantially. The present article shows this to be true when there are more than two groups, as well. Thus, it would seem reasonable and useful to develop a data-based method of classification that, in effect, represents a compromise between QD and LD. In this article we develop such a method based on an empirical Bayes formulation in which the within-groups covariance matrices are assumed to be outcomes of a common prior distribution whose parameters are estimated from the data. Two classification rules are developed under this framework and, through the use of extensive simulations, are compared to existing methods when the number of groups is moderate.     

Rail safety in Britain: An occupational health psychology perspective

As occupational health psychology matures, it will become involved in a more detailed way in areas ofspecific concern, and it will outgrow its recent over-dependence on a small number of very generalconcerns such as work stress and burnout. One example of all area of potential interest andopportunity is that afforded by rail safety. This paper discusses the position in Britain. It outlines thehistory and nature of safety management in the rail industry following privatisation in the 1990s. Itthen describes recent changes in safety thinking that could represent a mini-paradign shift and thatopen the door to the more active involvement of occupational health psychologists.     

Community Context,Women's Natal Kin Ties,and Demand for Children: Macro-Micro Linkages in Social Demography1

Abstract Recent research in many areas of social demography has begun to address the implications of cultural, social, and economic context for individual–level preferences and behavior. We expand on this theme by arguing that multiple levels of context may simultaneously direct individual–level strategies. We focus on the relationship between women's natal kin ties and their demand for children, a substantive area in which context is thought to be particularly important. We use a combination of ethnographic and survey data to measure contextual characteristics, women's ties to their natal families, and couples' fertility preferences and behavior. Our results demonstrate that particularly supportive relationships with natal kin have more influence on fertility preferences and behavior than contact with natal kin, although both dimensions are important. The results also show that even within the same cultural context, radically different community environments can produce opposite consequences of ties to natal kin.     

Stress, the press, the baby and the bathwater

The most important service rendered by the press is that of educating people to approach printed matter with distrust.

Samuel Butler, 1835-1902 English novelist and satirist     

Some results on commutation matrices,with statistical applications

The commutation matrix P _mn changes the order of multiplication of a Kronecker matrix product. The vec operator stacks columns of a matrix one under another in a single column. It is possible to express the vec of a Kronecker matrix product in terms of a Kronecker product of vecs of matrices. The commutation matrix plays an important role here. “Super-vec-operators” like vec A ? vec A vec ( A ? A ), and vec{( A ? A ) P _nn} are very convenient. Several of their properties are being studied. Both the traditional commutation matrix and vec operator and the newer concepts developed from these are applied to multivariate statistical and related problems.     

The efficient cross-validation of principal components applied to principal component regression

The cross-validation of principal components is a problem that occurs in many applications of statistics. The naive approach of omitting each observation in turn and repeating the principal component calculations is computationally costly. In this paper we present an efficient approach to leave-one-out cross-validation of principal components. This approach exploits the regular nature of leave-one-out principal component eigenvalue downdating. We derive influence statistics and consider the application to principal component regression.