Assessment of problem behaviour by foster parents and their foster children

This study investigates the level of agreement between foster parents and foster children about problem behaviour and how this is associated with the breakdown of a foster care placement. The study took a sample of foster parents and their foster children (n= 60), who were aged 11–17 years, and analysed the severity of problem behaviour displayed by the foster children within 3 months of placement in the foster family and a year and a half later. The level of agreement was established with the intra‐class correlation coefficient, which represents both the differences in scores and the differences in ranking. This coefficient shows that there is a statistically significant difference in the level of agreement on the Internalizing Problems scale of the Child Behaviour Checklist between the placements that broke down and those that continued. The level of agreement is associated with the severity score, which in turn is associated with the outcome of the foster care placement. To avoid placement breakdown, problem behaviour should also be assessed from the perspective of the foster child.     

Forthcoming in Sociological Forum

Table of Contents

Forthcoming in Sociological Forum     

High dimensional extension of the growth curve model and its application in genetics

Recent advances in technology have allowed researchers to collect large scale complex biological data, simultaneously, often in matrix format. In genomic studies, for instance, measurements from tens to hundreds of thousands of genes are taken from individuals across several experimental groups. In time course microarray experiments, gene expression is measured at several time points for each individual across the whole genome resulting in a high-dimensional matrix for each gene. In such experiments, researchers are faced with high-dimensional longitudinal data. Unfortunately, traditional methods for longitudinal data are not appropriate for high-dimensional situations. In this paper, we use the growth curve model and introduce test useful for high-dimensional longitudinal data and evaluate its performance using simulations. We also show how our approach can be used to filter genes in time course genomic experiments. We illustrate this using publicly available genomic data, involving experiments comparing normal human lung tissue with vanadium pentoxide treated human lung tissue, designed with the aim of understanding the susceptibility of individuals working in petro-chemical factories to airway re-modelling. Using our method, we were able to filter out 1053 (about 5 %) genes as non-noise genes from a pool of  22,277. Although our focus is on hypothesis testing, we also provided modified maximum likelihood estimator for the mean parameter of the growth curve model and assessed its performance through bias and mean squared error.     

Suicide and happiness: Seven tests of the connection

Indirectly, this paper examines the empirical connections between suicide and happiness by looking at the connection of each with seven standard demographic characteristics. They are sex, age, race, parental status, marital status, religiosity and employed status. These seven are chosen because a lot of data are available. We then examine the relationship of these same seven variables to suicide. Our findings indicate that marital status, religiosity and employment status have a (predicted) similar effect on suicide and happiness. Parenthood has an unclear relationship with suicide and happiness. Finally, sex, age and race have dissimilar effects on suicide and happiness. On the basis of this admittedly preliminary analysis, it would be impossible to conclude that happiness and suicide are closely (if inversely) related. First, there is the chance that suicides or happiness levels have been systematically misreported. Second, there may be a problem with our lumping together happiness and satisfaction. Third, there may be a problem with the seven particular independent variables we examined. Had we examined a different seven (or seventy) we might have drawn a different conclusion. In particular, we might have done better with comparative (or cognitive) variables derived from multiple discrepancies theory (MDT), than with demographic ones. In the end, the connection between happiness and suicide is far from certain. More research is needed.     

Rate of consistency of one sample tests of location

Let X₁,…,X_n be a sample from a population with continuous distribution function F(x?θ) such that F(x)+F(-x)=1 and 0<F(x)<1, x?R¹. It is shown that the power- function of a monotone test of H: θ=θ₀ against K: θ>θ₀ cannot tend to 1 as θ?θ₀ → ∞ more than n times faster than the tails of F tend to 0. Some standard as well as robust tests are considered with respect to this rate of convergence.     

Second order asymptotic relations of M-estimators and R-estimators in two-sample location model

Let T_M be an M-estimator (maximum likelihood type estimator) and T_R be an R-estimator (Hodges-Lehmann's estimator) of the shift parameter Δ in the two-sample location model. The asymptotic representation of √N(T_M-T_R) up to a term of the order O_p(N^{-$\text{1}\text{4}$}) is derived which is valid if the functions Ψ and ? generating T_M and T_R, respectively, decompose into an absolutely continuous and a step-function components; the order O_p(N^{-$\text{1}\text{4}$}) cannot be improved unless the discontinuous components vanish. As a consequence, the conditions under which √N(T_M-T_R)=O_p(N^{-$\text{1}\text{4}$}) are obtained. The main tool for obtaining the results is the second order asymptotic linearity of the pertaining linear rank statistics which is proved here under the assumption that the score-generating function ? has some jump-discontinuities.