The influence of age and trait attributions on recruitment selection

The role of interpersonal attraction into the recruitment selection is gaining research attention. Early work in the domain of the influence of attraction in organisations suggested that men are given more resources, such as higher salaries and promotions. However, recent research has found women have an automatic in-group bias. It was suggested that female interviewers are more likely to hire another female. In contrast, male interviewers were found to be equally as likely to hire men as women. To resolve these two conflicting findings a behavioural experiment was set up looking at gender, attractiveness and recruitment selection. Forty participants, twenty male and twenty female, of varying ages (18-65) were recruited through age stratified sampling. Participants took on the role of manager of a medium sized company and were shown twenty photographs of faces previously rated for attractiveness. On initial viewing participants were asked to decide whether they would firstly hire the person and secondly give as many reasons for their decision. Findings from this research show that in all age groups male and female participants gave females (especially attractive females) more jobs, except in the case of the 18-21 year old females who gave attractive males more jobs. On examining the reasons behind the participant’s decisions, it was evident that if you appeared confident, friendly, youthful and attractive you were 46% more likely to receive the job. However, if you were perceived to be untrustworthy, lazy, arrogant and unintelligent you were 49% more likely not to receive the job. These findings shed light on the various processes that may underpin human resource decisions in an organisational setting.

Robust automatic mapping algorithms in a network monitoring scenario

Automatically generating maps of a measured variable of interest can be problematic. In this work we focus on the monitoring network context where observations are collected and reported by a network of sensors, and are then transformed into interpolated maps for use in decision making. Using traditional geostatistical methods, estimating the covariance structure of data collected in an emergency situation can be difficult. Variogram determination, whether by method-of-moment estimators or by maximum likelihood, is very sensitive to extreme values. Even when a monitoring network is in a routine mode of operation, sensors can sporadically malfunction and report extreme values. If this extreme data destabilises the model, causing the covariance structure of the observed data to be incorrectly estimated, the generated maps will be of little value, and the uncertainty estimates in particular will be misleading. Marchant and Lark [2007] propose a REML estimator for the covariance, which is shown to work on small data sets with a manual selection of the damping parameter in the robust likelihood. We show how this can be extended to allow treatment of large data sets together with an automated approach to all parameter estimation. The projected process kriging framework of Ingram et al. [2007] is extended to allow the use of robust likelihood functions, including the two component Gaussian and the Huber function. We show how our algorithm is further refined to reduce the computational complexity while at the same time minimising any loss of information. To show the benefits of this method, we use data collected from radiation monitoring networks across Europe. We compare our results to those obtained from traditional kriging methodologies and include comparisons with Box-Cox transformations of the data. We discuss the issue of whether to treat or ignore extreme values, making the distinction between the robust methods which ignore outliers and transformation methods which treat them as part of the (transformed) process. Using a case study, based on an extreme radiological events over a large area, we show how radiation data collected from monitoring networks can be analysed automatically and then used to generate reliable maps to inform decision making. We show the limitations of the methods and discuss potential extensions to remedy these.