M. Dolores Ugarte

Enhancement of self-regulation, assertiveness, and empathy

Abstract This study examined the effects of teaching self-regulation strategies and social skills to 40 middle school students in a compulsory secondary education setting, who presented difficulties in self-reflection, self-inquiry, assertiveness, and empathy. A quasi-experimental design with pre- and post-test measurements was employed. Intervention consisted of the performance of tasks, called ‘Portfolio’, related to the criteria skills during the school course. Significant differences between the experimental and the control groups were observed in the measurement of the criteria variables. Results are discussed in terms of the implications concerning how teachers can implement self-regulatory activities in their daily classroom practice to meet the educational needs of students with social problems.

Immediate and long-term effects of a cognitive intervention on intelligence, self-regulation, and academic achievement

The main purpose of this study was to evaluate the effects of a package of activities, knows as “Portfolio”, on cognitive functioning, self-regulation, and academic achievement. The study was carried out with a group of 40 students from Compulsory Secondary Education (mean age 13 years old) during 12 hours distributed over two school years. A quasi-experimental pretest-posttest-1 and posttest-2 design was employed. Treatment consisted of the administration of specifically selected tasks, assessed in previous studies, from the psycho-pedagogical Instrumental Enrichment Program, the Philosophy for Children Program, and Project Intelligence. The students were evaluated in the criteria variables at the beginning and at the end of treatment, and once again two years later. The results indicate that the procedure was effective in all the variables studied and that gains observed at posttest-1 were maintained for at least two years after the intervention. Some relevant conclusions and suggestions at the educational and scientific level are commented upon.RésuméLe but principal de cette étude était d’évaluer les effets d’un paquet d’activités, connu comme “Portefeuille”, dans le functionnement cognitif, autorégulation, et succès académique. L’étude a été effectuée avec un groupe de 40 étudiants d’éducation Secondaire Obligatoire d’école. Un dessin quasi expérimental pretest-posttest-1 et du posttest-2 a été employé. Le traitement a consisté à l’administration d’une tâche spécifiquement sélectionée, avaluée travers d’études antérieures, du Programme psychopédagogique de l’Enrichissement Intrumental, du Programme Philosophie pour les Enfants, et du Projet d’Inttelligence. Les étudiants ont été évalués sur les variables du critère au début et à la fin du traitement, et encore une fois deux années plus tard. Les résultats indiquent que la procédure était efficace dans toutes les variables étudiées et que les gains observées dans le posttest-1 ont été maintenus pour au moins deux années après l’intervention. Quelques conclusions pertinentes et suggestions au niveau pédagogique et scientifique sont faites.

Analyzing Censored Spatial Data

Spatial data that are incomplete because of observations arising below or above a detection limit occur in many settings, for example, in mining, hydrology, and pollution monitoring. These observations are referred to as censored observations. For example, in a life test, censoring may occur at random times because of accident or breakdown of equipment. Also, censoring may occur when failures are discovered only at periodic inspections. Because the informational content of censored observations is less than that of uncensored ones, censored data create difficulties in an analysis, particularly when such data are spatially dependent. Traditional methodology applicable for uncensored data needs to be adapted to deal with censorship. In this paper we propose an adaptation of the traditional methodology using the so-called Expectation-Maximization (EM) algorithm. This approach permits estimation of the drift coefficients of a spatial linear model when censoring is present. As a by-product, predictions of unobservable values of the response variable are possible. Some aspects of the spatial structure of the data related to the implicit correlation also are discussed. We illustrate the results with an example on uranium concentrations at various depths.

Assessing the covariance function in geostatistics

In geostatistics, one of the crucial problems is the choice of the covariance function. In this paper we show how to improve the cross-validation criterion, traditionally used for evaluating the fit of a covariance function, in the case of unequally spaced data.

Spatio-temporal trends in gastric cancer mortality in Spain: 1975–2008

AIM OF THE STUDY There has been a downward trend in gastric cancer mortality worldwide. In Spain, a marked spatial aggregation of areas with excess mortality due to this cause has long been reported. This paper sought to analyse the evolution of gastric cancer mortality risk in Spanish provinces and explore the possible attenuation of the geographical pattern. METHODS We studied a series of gastric cancer mortality data by province, year of death, sex and age group using a conditional autoregressive (CAR) model that incorporated space, time and spatio-temporal interactions. RESULTS Gastric cancer mortality risk decreased in all Spanish provinces in both males and females. Overall, decreasing trends were more pronounced during the first years of the study period, largely due to a sharper fall in gastric cancer mortality risk among the older population. Recent decades have witnessed a slowing in the rate of decrease, especially among the younger age groups. In most areas, risk declined at a similar rate, thus serving to maintain interprovincial differences and the persistence of the geographical pattern, though with some differences. The north and northwest provinces were the areas with higher mortality risks in both sexes and age groups over the entire study period. CONCLUDING STATEMENT Despite the decline in gastric cancer mortality risk observed for the 50 Spanish provinces studied, geographical differences still persist in Spain, and the cluster of excess mortality in the north-west of the country remains in evidence.

Bounded Influence Estimation in a Spatial Linear Mixed Model

Kriging is an interpolation method that consists of finding a predictor linear function of the observations, minimizing the mean squared prediction error or kriging variance. Under multivariate normality assumptions, the given predictor is the best linear unbiased predictor, but if the underlying distribution is not normal, the estimator shall not be unbiased and shall be vulnerable to outliers. In the spatial context, it is not only the presence of outliers that may spoil the predictions, but also the boundary sites, usually corners, that tend to have high leverage. Therefore, kriging predictions are very sensitive on these corners, giving rise to values extremely vulnerable to small changes in the data. To overcome this situation, a spatial linear mixed model is proposed, deriving a bounded influence estimator of the location parameters. To illustrate the results, an application to Davis topographic data is presented.

Detecting Change-Points in the Time Series of Surfaces Occupied by Pre-defined NDVI Categories in Continental Spain from 1981 to 2015

The free access to satellite images since more than 40 years ago has provoked a rapid increase of multitemporal derived information of remote sensing data that should be summarized and analyzed for future inferences. In particular, the study of trends and trend changes is of crucial interest in many studies of phenology, climatology, agriculture, hydrology, geology or many other environmental disciplines. Overall, the normalized difference vegetation index (NDVI), as a satellite derived variable, plays a crucial role because of its usefulness for vegetation and landscape characterization, land use and land cover mapping, environmental monitoring, climate change or crop prediction models. Since the eighties, it can be retrieved all over the world from different satellites. In this work we propose to analyze its temporal evolution, looking for breakpoints or change-points in trends of the surfaces occupied by four NDVI classifications made in Spain from 1981 to 2015. The results show a decrease of bare soils and semi-bare soils starting in the middle nineties or before, and a slight increase of middle-vegetation and high-vegetation soils starting in 1990 and 2000 respectively.

Longitudinal data analysis

This handbook might be considered as a concise but complete encyclopedia on longitudinal data analysis. The editors have made a great effort to produce a volume providing a comprehensive and up-to-date view of the theory and application of longitudinal data analysis. They are not only editors but authors or coauthors of 8 of the 23 chapters. One of the strengths of the book is the organizational structure and the fact that the book has been written by well-known experts in the field. Five different themes are covered in five different parts of the book. Each part begins with an introduction and overview of the theme treated there and briefly describes the contents of the subsequent chapters. The only exception is the first part that comprises a single well-written chapter dealing with a historical perspective on the advances made in longitudinal data analysis. In six different but cohesive chapters, the second part of this volume presents the more classical and well-known matter of the book: the parametric modelling of longitudinal data. Part three consists of five interesting chapters covering nonparametric and semiparametric methods for the analysis of longitudinal data. In these first three parts, the emphasis of the book has been on statistical models and methods for the analysis of longitudinal data with a single outcome. The next part (part four) deals with situations in which multiple outcomes, recorded simultaneously, are measured repeatedly within each subject over time. Of particular interest for some readers could be the discussion on recent developments in the analysis of high-dimensional multivariate longitudinal data. The last part of this book has seven chapters and is devoted to present alternative methods for handling missing data in longitudinal studies. In fact, the first six chapters of this last part cover the analysis of incomplete data, but the last chapter deals with the estimation of the causal effects of time-varying exposures. All the 23 chapters come with their own references facilitating the potential readers to enlarge their knowledge if necessary. I find this book very useful for statisticians and researchers in many fields where the interest relies on studying the change of an outcome or multiple outcomes over time. Many of the chapters include examples and case studies in different disciplines and some of this material can be found in the web site of this book (http://www.biostat.harvard.edu/ fitzmaur/lda). I would like to congratulate the editors and all the contributing authors for preparing this comprehensive handbook on many interesting and complementary aspects of the theory and applications of longitudinal

Book Review: Quantitative methods in population health. Extensions of ordinary regression

(Chapter 6) are more difficult and are appropriate for a second year student. Group III (Chapter 7) contains studies that are broad in nature, that may require several different kinds of statistical analysis and for which ‘there may not necessarily be an ‘‘answer’’ to the statistical problem’. Chapter 8 provides yet another set of case studies, this time with no analysis provided to simulate a realistic consulting project. The case studies are useful both for homework assignments in statistical methods or as consulting courses, and are of value to the less experienced statistician who may need to expand their knowledge. Appendix A contains additional reference information on professional societies, sources of data and other information, recommended journals and statistical software. An outline of a one semester statistical consulting course is also given, which may be useful either for starting a statistical consulting program at a university or as a benchmark for an existing course. It concludes with a short list of additional suggested resources that supplement the information in this book. Appendix B is a good general overview of SAS and S-plus. Appendix C is summary of statistical ‘cheat sheets’ that would be very useful in the consulting arena, particularly when a quick answer to a statistical question is needed. Overall, I strongly recommend this book. It is well written and organized, and I am pleased to add this to my collection of books and articles on the practice of statistics. Students and faculty will benefit from this as textbook, and while the book has a noticeable academic flavor, this does not diminish its usefulness as a development resource for any statistician.