The ‘First Generation’ in Historical Perspective: Canadian Students in the 1960s

Over the past few years a number of studies have focused on the disadvantages confronted by students who are the first in their families to attend university. Their liabilities include relatively low levels of preparedness, a lack of involvement in campus activities, and low levels of academic achievement. Rather than accepting the universality of this characterization, in this article, these negative characteristics and experiences were viewed as one ‘ideal type.’ Using this ideal type as a reference point, the current study focused on a period in Canadian history in which first generation students were the norm. In an examination of Glendon College, York University, located in Toronto Canada, in the mid 1960s, it was found that the experiences of the first generation did not fit the ideal type. Those who likely were the first in their families to attend university were prepared for their studies, involved in campus activities, and earned good grades. Possible explanations for this deviation from the ideal type include the buoyancy of the economy in the mid-sixties, an expanding university sector, the size and relative intimacy of the College, the way in which high schools prepared students for university, and stringent admission requirements.     

Public transport and people with impairments – exploring non-use of public transport through the case of Oslo,Norway

abstract

Despite the attention given to universal design in recent years, many people with impairments still refrain from travelling on public transport. This explorative case study, conducted in the greater Oslo region in Norway, aims to add knowledge on why this is so. Findings indicate that insecurity while travelling and expectations of problems along the way are significant barriers. For many it is the sum of all challenges combined, from experience or anticipation that leads to non-use of public transport. The study shows that universal design policy, as of today, does not lead to accessible public transport systems that rendered impairments irrelevant. If society could safeguard the individuals’ travel needs, this might have profound societal repercussions through increased workforce participation and value added in society. Only then can society make the best use of all its inhabitants while championing inclusiveness and equality.     

Tracing disorder across theories of autism,empathy, and mental health care

Abstract

Categories of mental disorders are generally understood through a biomedical paradigm of clinical research, diagnosis, and intervention. Here, diagnoses operate as professional tools, facilitating care organization and information exchange across diverse social contexts. In this article, I focus on how the diagnosis of autism-spectrum disorder operates in this manner. Autism conceived as a biomedical disorder is then contrasted with proposals by the neurodiversity movement, who understand core qualities of autism as alternative expressions of otherwise normal processes of brain development. Finally, I supplement these conversations with insights from Gerald Edelman’s theory of neural plasticity and Felix Guattari’s paradigm of ethico-aesthetic care. Understood together, these allow mental disorders and community care generally to be reconceived in terms of networks of expressive, embodied, and dynamically embedded rhythms that transcend individual persons. This serves, additionally, to illustrate a concept of empathy that traverses neurological, psychological, and sociocultural domains.     

Consultants' forum: should post hoc sample size calculations be done?

Pre‐study sample size calculations for clinical trial research protocols are now mandatory. When an investigator is designing a study to compare the outcomes of an intervention, an essential step is the calculation of sample sizes that will allow a reasonable chance (power) of detecting a pre‐determined difference (effect size) in the outcome variable, at a given level of statistical significance. Frequently studies will recruit fewer patients than the initial pre‐study sample size calculation suggested. Investigators are faced with the fact that their study may be inadequately powered to detect the pre‐specified treatment effect and the statistical analysis of the collected outcome data may or may not report a statistically significant result. If the data produces a “non‐statistically significant result” then investigators are frequently tempted to ask the question “Given the actual final study size, what is the power of the study, now, to detect a treatment effect or difference?” The aim of this article is to debate whether or not it is desirable to answer this question and to undertake a power calculation, after the data have been collected and analysed. Copyright © 2008 John Wiley & Sons, Ltd.     

A copula-based Markov chain model for the analysis of binary longitudinal data

A fully parametric first-order autoregressive (AR(1)) model is proposed to analyse binary longitudinal data. By using a discretized version of a copula, the modelling approach allows one to construct separate models for the marginal response and for the dependence between adjacent responses. In particular, the transition model that is focused on discretizes the Gaussian copula in such a way that the marginal is a Bernoulli distribution. A probit link is used to take into account concomitant information in the behaviour of the underlying marginal distribution. Fixed and time-varying covariates can be included in the model. The method is simple and is a natural extension of the AR(1) model for Gaussian series. Since the approach put forward is likelihood-based, it allows interpretations and inferences to be made that are not possible with semi-parametric approaches such as those based on generalized estimating equations. Data from a study designed to reduce the exposure of children to the sun are used to illustrate the methods.     

Forecast mean squared error reductionin the VAR(1) process

When VAR models are used to predict future outcomes, the forecast error can be substantial. Through imposition of restrictions on the off-diagonal elements of the parameter matrix, however, the information in the process may be condensed to the marginal processes. In particular, if the cross-autocorrelations in the system are small and only a small sample is available, then such a restriction may reduce the forecast mean squared error considerably.

In this paper, we propose three different techniques to decide whether to use the restricted or unrestricted model, i.e. the full VAR(1) model or only marginal AR(1) models. In a Monte Carlo simulation study, all three proposed tests have been found to behave quite differently depending on the parameter setting. One of the proposed tests stands out, however, as the preferred one and is shown to outperform other estimators for a wide range of parameter settings.     

On estimating the distribution of data envelopment analysis efficiency scores: an application to nursing homes’ care planning process

Data envelopment analysis (DEA) is a deterministic econometric model for calculating efficiency by using data from an observed set of decision-making units (DMUs). We propose a method for calculating the distribution of efficiency scores. Our framework relies on estimating data from an unobserved set of DMUs. The model provides posterior predictive data for the unobserved DMUs to augment the frontier in the DEA that provides a posterior predictive distribution for the efficiency scores. We explore the method on a multiple-input and multiple-output DEA model. The data for the example are from a comprehensive examination of how nursing homes complete a standardized mandatory assessment of residents.     

A new estimation method for Weibull-type tails based on the mean excess function

Studying the right tail of a distribution, one can classify the distributions into three classes based on the extreme value index γ$\gamma$. The class γ>0$\gamma >0$ corresponds to Pareto-type or heavy tailed distributions, while γ<0$\gamma <0$ indicates that the underlying distribution has a finite endpoint. The Weibull-type distributions form an important subgroup within the Gumbel class with γ=0$\gamma =0$. The tail behaviour can then be specified using the Weibull tail index. Classical estimators of this index show severe bias. In this paper we present a new estimation approach based on the mean excess function, which exhibits improved bias and mean squared error. The asserted properties are supported by simulation experiments and asymptotic results. Illustrations with real life data sets are provided.     

Bayesian credible intervals for response surface optima

In response surface methodology, one is usually interested in estimating the optimal conditions based on a small number of experimental runs which are designed to optimally sample the experimental space. Typically, regression models are constructed from the experimental data and interrogated in order to provide a point estimate of the independent variable settings predicted to optimize the response. Unfortunately, these point estimates are rarely accompanied with uncertainty intervals. Though classical frequentist confidence intervals can be constructed for unconstrained quadratic models, higher order, constrained or nonlinear models are often encountered in practice. Existing techniques for constructing uncertainty estimates in such situations have not been implemented widely, due in part to the need to set adjustable parameters or because of limited or difficult applicability to constrained or nonlinear problems. To address these limitations a Bayesian method of determining credible intervals for response surface optima was developed. The approach shows good coverage probabilities on two test problems, is straightforward to implement and is readily applicable to the kind of constrained and/or nonlinear problems that frequently appear in practice.     

A re-evaluation of random-effects meta-analysis

Summary.  Meta-analysis in the presence of unexplained heterogeneity is frequently undertaken by using a random-effects model, in which the effects underlying different studies are assumed to be drawn from a normal distribution. Here we discuss the justification and interpretation of such models, by addressing in turn the aims of estimation, prediction and hypothesis testing. A particular issue that we consider is the distinction between inference on the mean of the random-effects distribution and inference on the whole distribution. We suggest that random-effects meta-analyses as currently conducted often fail to provide the key results, and we investigate the extent to which distribution-free, classical and Bayesian approaches can provide satisfactory methods. We conclude that the Bayesian approach has the advantage of naturally allowing for full uncertainty, especially for prediction. However, it is not without problems, including computational intensity and sensitivity to a priori judgements. We propose a simple prediction interval for classical meta-analysis and offer extensions to standard practice of Bayesian meta-analysis, making use of an example of studies of 'set shifting' ability in people with eating disorders.