Teachers’ Perceptions of Childcare and Preschool Expulsion

Previous research outlines the correlates of childcare expulsion in the USA, yet researchers know little about how these correlates produce expulsion. This in‐depth qualitative analysis of 30 childcare providers’ accounts of expulsion finds a patterned process to expulsion: Teachers search for causes and solutions to challenging behaviours. When interventions fail, overwhelmed teachers shift their focus from ‘struggling’ children to ‘bad families’. Once the explanation of behaviour changes from within to outside of the child, expulsion is imminent. Interventions in teachers’ understandings, not only in children's behaviours, are discussed as a possible way to reduce expulsion.     

Deaf access to justice in Northern Ireland: rethinking ‘Reasonable Adjustment’ in the Disability Discrimination Act

Abstract

This article provides findings of a qualitative study exploring the interactions of eight Deaf participants and one hearing ally with the justice system in Northern Ireland, where the Disability Discrimination Act requires solicitors to make ‘reasonable adjustments’ in order to provide effective access to Deaf clients. Three thematic categories emerged: (a) barriers to accessing justice, (b) work Deaf people do for access, and (c) the need to educate solicitors about access. A central strain ran through these themes: the idea that ‘reasonable adjustment’ must reflect the value of sign language interpreters in facilitating effective communication access for all the parties.     

A qualitative investigation of risk and protective factors for interpersonal violence in adults on the autism spectrum

Abstract

Individuals on the autism spectrum are at an increased risk of experiencing violence. This study sought to understand the opinions of adults on the spectrum regarding potential risk and protective factors for interpersonal violence. Twenty-two adults (12 men; 18 to 53 years of age) participated in semi-structured qualitative interviews. Interviews were recorded, transcribed, and analysed. The results of the qualitative analysis identified individual and contextual risk and protective factors. Protective themes focused on individual needs (awareness of the self and the environment, interpersonal knowledge/skills, skill building strategies) and supportive contexts (benefits of a trusted person, advocates, fostering acceptance and inclusion). There is a need for strategies to decrease the vulnerability to, and the impact of, interpersonal violence. Individuals on the spectrum have the capacity to provide important information regarding safety promotion and risk factors for interpersonal violence, and their expertise should play a role in guiding efforts that promote safety.     

The academic attitudes of American teenagers, 1990–2002: Cohort and gender effects on math achievement

Using data from the National Education Longitudinal Study of 1988 and the Education Longitudinal Study of 2002, I compare the academic attitudes of high school students from Generation X and the Millennial Generation. I then analyze the effects these attitudes have on mathematics achievement test scores. Compared to the earlier group, students in the later cohort were less likely to indicate academic reasons for attending school and largely perceived their friends as being less engaged academically. Students in both cohorts whose friends held academic values experienced an increase in math scores. The more students in the earlier cohort disagreed that they came to school for academic reasons, the more their math scores decreased; this relationship did not appear for the later cohort. Females in each cohort showed stronger academic attitudes than males; additionally, believing that popularity was important was associated with lower test scores for females, but not males, in the Millennial cohort.     

On pseudo-values for regression analysis in competing risks models

For regression on state and transition probabilities in multi-state models Andersen et al. (Biometrika 90:15–27, 2003) propose a technique based on jackknife pseudo-values. In this article we analyze the pseudo-values suggested for competing risks models and prove some conjectures regarding their asymptotics (Klein and Andersen, Biometrics 61:223–229, 2005). The key is a second order von Mises expansion of the Aalen-Johansen estimator which yields an appropriate representation of the pseudo-values. The method is illustrated with data from a clinical study on total joint replacement. In the application we consider for comparison the estimates obtained with the Fine and Gray approach (J Am Stat Assoc 94:496–509, 1999) and also time-dependent solutions of pseudo-value regression equations.     

VARIATIONAL BAYESIAN ANALYSIS FOR HIDDEN MARKOV MODELS

The variational approach to Bayesian inference enables simultaneous estimation of model parameters and model complexity. An interesting feature of this approach is that it also leads to an automatic choice of model complexity. Empirical results from the analysis of hidden Markov models with Gaussian observation densities illustrate this. If the variational algorithm is initialized with a large number of hidden states, redundant states are eliminated as the method converges to a solution, thereby leading to a selection of the number of hidden states. In addition, through the use of a variational approximation, the deviance information criterion for Bayesian model selection can be extended to the hidden Markov model framework. Calculation of the deviance information criterion provides a further tool for model selection, which can be used in conjunction with the variational approach.     

A likelihood based estimator for vector autoregressive processes

A onestep estimator, which is an approximation to the unconditional maximum likelihood estimator (MLE) of the coefficient matrices of a Gaussian vector autoregressive process is presented. The onestep estimator is easy to compute and can be computed using standard software. Unlike the computation of the unconditional MLE, the computation of the onestep estimator does not require any iterative optimization and the computation is numerically stable. In finite samples the onestep estimator generally has smaller mean square error than the ordinary least squares estimator. In a simple model, where the unconditional MLE can be computed, numerical investigation shows that the onestep estimator is slightly worse than the unconditional MLE in terms of mean square error but superior to the ordinary least squares estimator. The limiting distribution of the onestep estimator for processes with some unit roots is derived.     

On the Assessment of Monte Carlo Error in Simulation-Based Statistical Analyses

Statistical experiments, more commonly referred to as Monte Carlo or simulation studies, are used to study the behavior of statistical methods and measures under controlled situations. Whereas recent computing and methodological advances have permitted increased efficiency in the simulation process, known as variance reduction, such experiments remain limited by their finite nature and hence are subject to uncertainty; when a simulation is run more than once, different results are obtained. However, virtually no emphasis has been placed on reporting the uncertainty, referred to here as Monte Carlo error, associated with simulation results in the published literature, or on justifying the number of replications used. These deserve broader consideration. Here we present a series of simple and practical methods for estimating Monte Carlo error as well as determining the number of replications required to achieve a desired level of accuracy. The issues and methods are demonstrated with two simple examples, one evaluating operating characteristics of the maximum likelihood estimator for the parameters in logistic regression and the other in the context of using the bootstrap to obtain 95% confidence intervals. The results suggest that in many settings, Monte Carlo error may be more substantial than traditionally thought.     

Parametric non-mixture cure models for schedule finding of therapeutic agents

We propose a phase I clinical trial design that seeks to determine the cumulative safety of a series of administrations of a fixed dose of an investigational agent. In contrast with traditional phase I trials that are designed solely to find the maximum tolerated dose of the agent, our design instead identifies a maximum tolerated schedule that includes a maximum tolerated dose as well as a vector of recommended administration times. Our model is based on a non-mixture cure model that constrains the probability of dose limiting toxicity for all patients to increase monotonically with both dose and the number of administrations received. We assume a specific parametric hazard function for each administration and compute the total hazard of dose limiting toxicity for a schedule as a sum of individual administration hazards. Throughout a variety of settings motivated by an actual study in allogeneic bone marrow transplant recipients, we demonstrate that our approach has excellent operating characteristics and performs as well as the only other currently published design for schedule finding studies. We also present arguments for the preference of our non-mixture cure model over the existing model.