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Doelstelling: De doelstelling van deze scriptie is nagaan wat het beeld is van Spanje en meerbepaald van het Baskisch conflict in de Nederlandstalige populaire pers. Concreet wordt onderzocht hoe en in welke mate aandacht besteed wordt aan Spanje en het Baskisch conflict. Middelen of methode: De scriptie bestaat uit twee delen. Het eerste deel is een kwantitatieve analyse van de gegevens, het tweede deel is een kwalitatieve analyse. In beide delen worden de gegevens gevonden in de populaire pers vergeleken met die gevonden in de kwaliteitspers. Resultaten: Uit de kwantitatieve analyse blijkt duidelijk dat de kwaliteitspers meer aandacht besteedt aan Spanje. De kwalitatieve analyse toont aan dat ook aan het Baskisch conflict meer aandacht besteed wordt in de kwaliteitspers, meerbepaald aan de politieke kant van het conflict. Daarnaast kon vastgesteld worden dat in de populaire pers meer 'emotionele' thema's behandeld worden en dat die 'emotionele' toets ook terug te vinden is binnen een meer politiek thema, zoals het Baskisch conflict.

Kwalitatief onderzoek. --- Kwantitatief onderzoek. --- Maatschappij en instellingen.

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departement Onderzoek 10

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Missing data arise in almost all scientific disciplines. In many cases, the treatment of missing data in an analysis is carried out in a casual and ad-hoc manner, leading, in many cases, to invalid inference and erroneous conclusions. In the past 20 years or so, there has been a serious attempt to understand the underlying issues and difficulties that come about from missing data and their impact on subsequent analysis. There has been a great deal written on the theory developed for analyzing missing data for finite-dimensional parametric models. This includes an extensive literature on likelihood-based methods and multiple imputation. More recently, there has been increasing interest in semiparametric models which, roughly speaking, are models that include both a parametric and nonparametric component. Such models are popular because estimators in such models are more robust than in traditional parametric models. The theory of missing data applied to semiparametric models is scattered throughout the literature with no thorough comprehensive treatment of the subject. This book combines much of what is known in regard to the theory of estimation for semiparametric models with missing data in an organized and comprehensive manner. It starts with the study of semiparametric methods when there are no missing data. The description of the theory of estimation for semiparametric models is at a level that is both rigorous and intuitive, relying on geometric ideas to reinforce the intuition and understanding of the theory. These methods are then applied to problems with missing, censored, and coarsened data with the goal of deriving estimators that are as robust and efficient as possible. Anastasios A. Tsiatis is the Drexel Professor of Statistics at North Carolina State University. His research has focused on developing statistical methods for the design and analysis of clinical trials, censored survival analysis, group sequential methods, surrogate markers, semiparametric methods with missing and censored data and causal inference and has been the major Ph.D. advisor for more than 30 students working in these areas. He is a Fellow of the American Statistical Association and the Institute of Mathematical Statistics. He is the recipient of the Spiegelman Award and the Snedecor Award. He has been an Associate Editor of the Annals of Statistics and Statistics and Probability Letters and is currently an Associate Editor for Biometrika.

Statistical science --- statistisch onderzoek

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Factorial design plays a fundamental role in efficient and economic experimentation with multiple input variables and is extremely popular in various fields of application, including engineering, agriculture, medicine and life sciences. Factorial experiments are often used in case studies in quality management and Design for Six Sigma (DFSS). The last twenty years have witnessed a significant growth of interest in optimal factorial designs, under possible model uncertainty, via the minimum aberration and related criteria. The present book gives, for the first time in book form, a comprehensive and up-to-date account of this modern theory. Many major classes of designs are covered in the book. While maintaining a high level of mathematical rigor, it also provides extensive design tables for research and practical purposes. In order to equip the readers with the necessary background, some foundational concepts and results are developed in Chapter 2. Apart from being useful to researchers and practitioners, the book can form the core of a graduate level course in experimental design. It can also be used for courses in combinatorial designs or combinatorial mathematics. Rahul Mukerjee is a Professor of Statistics at the Indian Institute of Management Calcutta. Formerly, he was a Professor at the Indian Statistical Institute. He is a co-author of four other research monographs including two from Springer and one from Wiley. A Fellow of the Institute of Mathematical Statistics and the Indian National Science Academy, Professor Mukerjee has served on the editorial boards of several international journals. He is a recipient of the S.S. Bhatnagar Award, the most well-known scientific honor from the Government of India. C. F. Jeff Wu is Coca Cola Chair Professor in Engineering Statistics at Georgia Institute of Technology. Prior to 2003, he taught statistics at U. of Wisconsin, U. of Waterloo and U. of Michigan. He wrote with M. Hamada the applied design text Experiments: Planning, Analysis and Parameter Design Optimization by Wiley in 2000. He has served on various editorial boards. For his work in theory and methodology, including major work on design of experiments, he has won numerous awards and professional fellowships, including the COPSS Award and membership on the U.S. National Academy of Engineering.

Statistical science --- statistisch onderzoek

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ECLAS --- Cultuurlandschappen --- Landschapsarchitectuur ; onderzoek