Recent years have seen a rapid expansion of the number of psychologists and applied social scientists working or training in applied settings such as mental health, health promotion, education, work and organisations, management and marketing. Increasingly, the professionals, researchers and students working in these areas are seeking to describe and understand the links between causal beliefs and behaviour, that is, attributions in action. In the context of such applied work, the collection and analysis of qualitative data is often required. This book deals with a powerful, practical and well-tried method the Leeds Attributional Coding System (LACS) for extracting and coding causal beliefs from qualitative interview data. The method has been developed and used over the last ten years, in a variety of applied contexts. The authors have provided here a practical and accessible introduction to the method, illustrated by examples, case studies and useful applications in a range of applied settings. This book provides<br> * an overview of attribution theory and causal beliefs, from a practitioner perspective<br> * an introduction to a tried and tested tool for coding qualitative interview data<br> * clear and explanatory examples of the method in action, as well as useful exercises<br> * case studies from a variety of fields including clinical, organisational and marketing settings
Unifying information theory and digital communication through the language of lattice codes, this book provides a detailed overview for students, researchers and industry practitioners. It covers classical work by leading researchers in the field of lattice codes and complementary work on dithered quantization and infinite constellations, and then introduces the more recent results on 'algebraic binning' for side-information problems, and linear/lattice codes for networks. It shows how high dimensional lattice codes can close the gap to the optimal information theoretic solution, including the characterisation of error exponents. The solutions presented are based on lattice codes, and are therefore close to practical implementations, with many advanced setups and techniques, such as shaping, entropy-coding, side-information and multi-terminal systems. Moreover, some of the network setups shown demonstrate how lattice codes are potentially more efficient than traditional random-coding solutions, for instance when generalising the framework to Gaussian networks.
The wine world can be intimidating to people who are just starting out. French wines can add an additional layer of complexity given the different, and less familiar, ways the wines are classified.
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