International and intercultural issues in English teaching textbooks: the case of Spain

The sociocultural content of foreign language textbooks has become a concern of scholars and practitioners owing to the fact that the traditional emphasis on purely linguistic issues has been expanded to embrace a language in context approach. This paper studies the English‐speaking communities that are described in English language teaching textbooks marketed in Spain. It examines to what extent an international and/or intercultural approach is a constituent element in their design.     

Führung durch Selbstführung

This paper outlines the conceptual bases, strategies, and psychological attributes of leadership by self-leadership. In addition, relations to other theories of leadership are drawn. Dimensions and correlates of self-leadership competence are described and discussed. Various approaches for developing self-leadership competence are discussed as well.     

Social Antecedents of Medical Fraud and Over-Servicing: What Makes A Doctor Criminal?

Medical fraud and overservicing are estimated to cost the Australian community between $130 and $200 million per annum, a figure far greater than the national cost of burglary and almost the same as the total property loss from all conventional crime. An examination of the social antecedents of medical fraud and overservicing suggests that the predisposition of some doctors to engage in these practices occurs because of the following: (1) medical training and professional socialization that orientate student doctors away from altruistic health issues towards narrower self-interested professional concerns; (2) career expectations of a high pattern of material consumption that are often frustrated by an increasingly competitive medical market place; and (3) professional medical organizations that lobby for national health policies which reflect the mercenary self-interest of doctors rather than the health interest of the nation.     

Non-ignorable missing covariate data in survival analysis: a case-study of an International Breast Cancer Study Group trial

Summary.  Non-ignorable missing data, a serious problem in both clinical trials and observational studies, can lead to biased inferences. Quality-of-life measures have become increasingly popular in clinical trials. However, these measures are often incompletely observed, and investigators may suspect that missing quality-of-life data are likely to be non-ignorable. Although several recent references have addressed missing covariates in survival analysis, they all required the assumption that missingness is at random or that all covariates are discrete. We present a method for estimating the parameters in the Cox proportional hazards model when missing covariates may be non-ignorable and continuous or discrete. Our method is useful in reducing the bias and improving efficiency in the presence of missing data. The methodology clearly specifies assumptions about the missing data mechanism and, through sensitivity analysis, helps investigators to understand the potential effect of missing data on study results.     

Efficient designs based on orthogonal arrays of type I and type II for experiments using units ordered over time or space

For a wide variety of applications, experiments are based on units ordered over time or space. Models for these experiments generally may include one or more of: correlations, systematic trends, carryover effects and interference effects. Since the standard optimal block designs may not be efficient in these situations, orthogonal arrays of type I and type II, which were introduced in 1961 by C.R. Rao [Combinatorial arrangements analogous to orthogonal arrays, Sankhya A 23 (1961) 283–286], have been recently used to construct optimal and efficient designs for many of these experiments. Results in this area are unified and the salient features are outlined.     

Secondary analysis of dangerousness among death sentenced capital murderers

The theory of incapacitation involves reducing an offender's ability or capacity to commit further crimes. Capital punishment accomplishes this goal. An executed murderer never murders again. However, we do not execute all murderers, only capital murderers. This policy produces several research questions. Do capital murderers present a special risk to society? Are capital murderers more likely to murder or commit other violent crimes again than other murderers or the average citizen? To answer these questions, many states require a prediction of future dangerousness of a newly convicted murderer. To what extent has the judgment of future dangerousness matched actuarial data of subsequent murders and serious crimes? Using a secondary analysis, this investigation attempted to assemble available data of postconviction dangerousness of death sentenced capital murderers to create a more comprehensive actuarial account of subsequent dangerousness and to present the data in a common format used by the Federal Bureau of Investigation and the Bureau of Justice Statistics. Across 14 studies identified with relevant data, there were 13 instances of subsequent murder and 462 serious crime or prison rule violations.     

A semi-parametric approach to robust parameter design

Parameter design or robust parameter design (RPD) is an engineering methodology intended as a cost-effective approach for improving the quality of products and processes. The goal of parameter design is to choose the levels of the control variables that optimize a defined quality characteristic. An essential component of RPD involves the assumption of well estimated models for the process mean and variance. Traditionally, the modeling of the mean and variance has been done parametrically. It is often the case, particularly when modeling the variance, that nonparametric techniques are more appropriate due to the nature of the curvature in the underlying function. Most response surface experiments involve sparse data. In sparse data situations with unusual curvature in the underlying function, nonparametric techniques often result in estimates with problematic variation whereas their parametric counterparts may result in estimates with problematic bias. We propose the use of semi-parametric modeling within the robust design setting, combining parametric and nonparametric functions to improve the quality of both mean and variance model estimation. The proposed method will be illustrated with an example and simulations.     

Adaptive tests of regression functions via multiscale generalized likelihood ratios

Many applications of nonparametric tests based on curve estimation involve selecting a smoothing parameter. The author proposes an adaptive test that combines several generalized likelihood ratio tests in order to get power performance nearly equal to whichever of the component tests is best. She derives the asymptotic joint distribution of the component tests and that of the proposed test under the null hypothesis. She also develops a simple method of selecting the smoothing parameters for the proposed test and presents two approximate methods for obtaining its P‐value. Finally, she evaluates the proposed test through simulations and illustrates its application to a set of real data.