Promoting the Visibility of Educational Research through an Institutional Repository

Established in 1994 through the amalgamation of several teacher training colleges, The Hong Kong Institute of Education (HKIEd) is the major multidisciplinary teacher education provider in the Hong Kong SAR. Despite this, the Institute does not have a particularly high research profile when compared with its peer institutions in Hong Kong and around the world. Its research publishing achieves modest exposure and impact in international educational research literature. The Institute has a goal to attain the title of a “university of education” and has identified an improvement in its research output and profile as critical to achieving this. In this context the HKIEd Library embarked on the redevelopment of its institutional repository, changing its direction from being an archive of institutional publications to one which brought together and offered access to the sum total of published output of the Institute since its foundation, in a deliberate effort to promote Institute research. This paper explores the particular approach taken by the Library to the development of the institutional repository, how the repository contributes directly to and aligns with the research strategies of the Institute, and the impact the Repository has had so far on improving the profile of research at HKIEd.     

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.     

Bosnia and the Middle East: Current Political,Economic and Cultural Ties

This article will review and analyze Bosnia’s political, economic and cultural ties with the Middle East, most especially Turkey, Iran, Saudi Arabia and Israel, as well as the evolution of Muslim identity in that republic, especially since the late 1960s when there was an internal liberalization process in part influenced by Yugoslavia’s policy on non-alignment. Today, Bosnians of all ethnic and religious backgrounds must contend with internal divisions and tensions exacerbated by the 1992–1995 war while utilizing a very cumbersome political system that may have brought peace at the expense of unity. Bosnia’s geographical location draws that country toward the rest of Europe while history and the legacy of the Ottoman Empire, also draws Muslims toward the Middle East.     

W.E.B. Du Bois and Filipino/a American exposure programs to the Philippines: race class analysis in an epoch of ‘global apartheid’

The article highlights the ongoing relevance of W.E.B. Du Bois for the global analysis of race and class. Engaging scholarly debates that have ensued within the educational subfields of critical race theory (CRT) and (revolutionary) critical pedagogy, the article explores how a deeper engagement with Du Bois’s ideas contributes theoretically and methodologically to these two subfields. Of particular focus is Du Bois’s conceptualization of a ‘guiding hundredth,’ which he forwarded as a corrective to his ideas of a ‘talented tenth.’ The article also offers a case study analysis of the film Sounds of a New Hope, which documents a hip hop exposure program to the Philippines. The case study draws upon Du Bois’s ‘guiding hundredth’ for a twenty-first century context as a Filipino American cultural worker utilizes hip hop to articulate, analyze, and alter the lived experiences for Filipino/a Americans in a global diaspora.