Preventing failure at school

This paper considers early educational interventions aimed at preventing school failure. It begins with a typology of prevention and intervention, then goes on to consider four specific examples of preventive interventions all aimed at avoiding or alleviating academic failure at school. (1) The High Scope pre-school programme for disadvantaged children aimed at providing a ‘better start at school'. (2) Reading Recovery, a targeted intervention aimed at getting children who show early reading difficulties ‘back on course'. (3) A Focused Literacy Programme for improving the reading of all children in disadvantaged neighbourhoods of the inner city. (4) ‘Parent involvement' programmes to improve the reading of all children in primary school through support for literacy at home. Each of the intervention strategies is described and criticised in terms of effectiveness, acceptability and generalisation. Copyright © 1999 John Wiley & Sons, Ltd.     

Do Drivers of Small Cars Take Less Risk in Everyday Driving?

Previously reported observed data on risky everyday driving are brought together and reanalyzed in order to focus on the relation between risky driving and the size of the car being driven, as indicated by car mass. The measures of risky driving include separation between vehicles in heavy freeway traffic and speed on a two lane road. Observed seat belt use provides a third measure of driver risk. Confounding effects arising from the observed association between car mass and driver age are taken into account by segmenting the data into three driver age groups. Driver risk taking is found to increase with increasing car mass for each of these three aspects of everyday driving. The implications of these results with respect to driver fatality rates are discussed in terms of a simple model relating observed risky driving to the likelihood of involvement in a severe crash.     

A new class of measurement-error models,with applications to dietary data

Measurement-error modelling occurs when one cannot observe a covariate, but instead has possibly replicated surrogate versions of this covariate measured with error. The vast majority of the literature in measurement-error modelling assumes (typically with good reason) that given the value of the true but unobserved (latent) covariate, the replicated surrogates are unbiased for latent covariate and conditionally independent. In the area of nutritional epidemiology, there is some evidence from biomarker studies that this simple conditional independence model may break down due to two causes: (a) systematic biases depending on a person's body mass index, and (b) an additional random component of bias, so that the error structure is the same as a one-way random-effects model. We investigate this problem in the context of (1) estimating distribution of usual nutrient intake, (2) estimating the correlation between a nutrient instrument and usual nutrient intake, and (3) estimating the true relative risk from an estimated relative risk using the error-prone covariate. While systematic bias due to body mass index appears to have little effect, the additional random effect in the variance structure is shown to have a potentially important effect on overall results, both on corrections for relative risk estimates and in estimating the distribution of usual nutrient intake. However, the effect of dietary measurement error on both factors is shown via examples to depend strongly on the data set being used. Indeed, one of our data sets suggests that dietary measurement error may be masking a strong risk of fat on breast cancer, while for a second data set this masking is not so clear. Until further understanding of dietary measurement is available, measurement-error corrections must be done on a study-specific basis, sensitivity analyses should be conducted, and even then results of nutritional epidemiology studies relating diet to disease risk should be interpreted cautiously.     

The Structure of Mental Health: Higher-Order Confirmatory Factor Analyses of Psychological Distress and Well-Being Measures

Social Indicators Research - This paper addresses the question of whether psychological distress and subjective well-being are the opposite poles of the same axis of mental health or independent...     

A biweight approach to estimate currency exchange rate: the Nigerian example

In this article, we propose a biweight approach to a real-life location problem, namely, the estimation of a realistic exchange rate for the Nigerian currency, naira (for easy reference, we denote the exchange rate parameter byθ).

Our proposal is essentially a critic of the methods being used by the Central Bank of Nigeria (CBN) to derive its estimate θ_CBN of θ. The CBN generates the necessary data by periodically organizing a foreign exchange market (FEM) where it sells a certain amount of US dollars to authorized foreign exchange dealers. (The amount of dollars available for sale is usually inadequate to meet aggregate demand, so there is literally a 'scramble' among dealers for a 'slice of the cake'.) During each session of FEM, each dealer quotes: (a) how much naira (variable Y) it will pay for US$1, and (b) the amount of US dollars (variable X) it wants to buy. The CBN estimates, based on observations of Y, have been found to be unstable and part of the problem seems to lie with the fact that a few atypical or outlier values are generated at FEM sessions and CBN estimation methods are not resistant to these extreme values.

This article presents a robust/resistant model which is designed to tackle the problem of outliers head on: we exploit the resistance property of the biweight to help reduce the influence of any outlier on the final biweight estimate θ_bw. Furthermore, we use the biweight weight, in conjunction with X, as an instrument to check against generation of outliers at FEM.