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Man has been seeking an ideal existence for a very long time. In this existence, justice, love, and peace are no longer words, but actual experiences. How ever, with the American preemptive invasion and occupation of Afghanistan and Iraq and the subsequent prisoner abuse, such an existence seems to be farther and farther away from reality. The purpose of this work is to stop this dangerous trend by promoting justice, love, and peace through a change of the paradigm that is inconsistent with justice, love, and peace. The strong paradigm that created the strong nation like the U.S. and the strong man like George W. Bush have been the culprit, rather than the contributor, of the above three universal ideals. Thus, rather than justice, love, and peace, the strong paradigm resulted in in justice, hatred, and violence. In order to remove these three and related evils, what the world needs in the beginning of the third millenium is the weak paradigm. Through the acceptance of the latter paradigm, the golden mean or middle paradigm can be formulated, which is a synergy of the weak and the strong paradigm. In order to understand properly the meaning of these paradigms, however, some digression appears necessary.

We provide straightforward new nonparametric methods for testing conditional independence using local polynomial quantile regression, allowing weakly dependent data. Inspired by Hausman's (1978) specification testing ideas, our methods essentially compare two collections of estimators that converge to the same limits under correct specification (conditional independence) and that diverge under the alternative. To establish the properties of our estimators, we generalize the existing nonparametric quantile literature not only by allowing for dependent heterogeneous data but also by establishing a weak consistency rate for the local Bahadur representation that is uniform in both the conditioning variables and the quantile index. We also show that, despite our nonparametric approach, our tests can detect local alternatives to conditional independence that decay to zero at the parametric rate. Our approach gives the first nonparametric tests for time-series conditional independence that can detect local alternatives at the parametric rate. Monte Carlo simulations suggest that our tests perform well in finite samples. We apply our test to test for a key identifying assumption in the literature on nonparametric, nonseparable models by studying the returns to schooling.

This paper looks at developments in the use of learning technologies in UK higher education, particularly in recent years. It examines the agenda items for learning and teaching associated with the use of new technologies that have emerged in the context of the current agenda for quality assessment and assurance. The paper considers the ways in which the two agendas work in a complementary manner, and the ways in which they create tensions. Finally, it considers the likely path of future developments and considers a way forward in which existing tensions may be reconciled.

This paper aims to develop a model of measuring student engagement in a blended-massive open online course (MOOC) context. MOOCs are those that are delivered, usually by leading universities, with a promise to provide free high-quality education to an unlimited number of learners. They offer an opportunity for “blended” course design, where instructors can integrate MOOC content within face-to-face educational activities and components.

Three internationally recognized existing frameworks, the National Survey of Student Engagement (NSSE), UK Engagement Survey (UKES) and Student Engagement Questionnaire (SEQ), were combined into a single model. The model was confirmed by a panel of 35 expert practitioners. A trial evaluation using the model was carried out on 13 students who had participated in a course that had adopted a blended-MOOC format.

The confirmed model has nine indicators which provide information about distinct aspects of student engagement.

It is considered to be the first model designed to measure student engagement within the blended-MOOC context.

The asymptotic bias and variance of a general class of local polynomial estimators of M-regression functions are studied over the whole compact support of the multivariate covariate under a minimal assumption on the support. The support assumption ensures that the vicinity of the boundary of the support will be visited by the multivariate covariate. The results show that like in the univariate case, multivariate local polynomial estimators have good bias and variance properties near the boundary. For the local polynomial regression estimator, we establish its asymptotic normality near the boundary and the usual optimal uniform convergence rate over the whole support. For local polynomial quantile regression, we establish a uniform linearization result which allows us to obtain similar results to the local polynomial regression. We demonstrate both theoretically and numerically that with our uniform results, the common practice of trimming local polynomial regression or quantile estimators to avoid “the boundary effect” is not needed.

Using data from the National Longitudinal Survey of Youth (1998) this article analyses the labour market status of African‐American women in management positions. The results show that among supervisors with a high school and college education, black women earn lower wages than black men even after controlling for detailed background, personal, and human capital characteristics. The lower earnings of black female supervisors can partly be attributed to the fact that they are segregated in predominantly female jobs. Additionally, in contrast to black males and white females, black females do not earn significant wage premiums associated with supervisory duties.

Defines racial harassment and the forms in which it generally appears. Looks at the issue from the employer’s perspective, advising liabilities. Continues by putting the employee’s point of view. Expels some common myths and lists some useful recent case law.

This paper examines Aman Ullah’s contributions to robust inference, finite sample econometrics, nonparametrics and semiparametrics, and panel and spatial models. His early works on robust inference and finite sample theory were mostly motivated by his thesis advisor, Professor Anirudh Lal Nagar. They eventually led to his most original rethinking of many statistics and econometrics models that developed into the monograph Finite Sample Econometrics published in 2004. His desire to relax distributional and functional-form assumptions lead him in the direction of nonparametric estimation and he summarized his views in his most influential textbook Nonparametric Econometrics (with Adrian Pagan) published in 1999 that has influenced a whole generation of econometricians. His innovative contributions in the areas of seemingly unrelated regressions, parametric, semiparametric and nonparametric panel data models, and spatial models have also inspired a larger literature on nonparametric and semiparametric estimation and inference and spurred on research in robust estimation and inference in these and related areas.

Many human resources managers and others have dealt with changing worker motivations and declining productivity amidst change. However, when dramatic socio‐political events occur in a culture or globally, they often bring on new hopes and expectations for workers which may or may not be fully understood by ‘Mr. Company’, a patriarchal moniker South African workers often use when talking about the management of their employing company. South Africa's blue‐collar workers before and after apartheid are an example of the need to more frequently examine the impact of change on worker attitudes and of the perception of that change by management. This study revisits South Africa after apartheid to survey a sample of black workers and their white supervisors from three mining operations for changes in their attitudes since a baseline study performed by Harari & Beaty in the mid 1980's. The implications, to Mr. ‘Company's’ human resource managers and managers of other multinational enterprises, of these dramatic changes and resultant rise in workers' expectations are examined in light of the new, Nelson Mandela and African National Congress led, South African government's recommendations for enhancing black quality of life, involvement, and development.