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Multilevel Analysis
This practical introduction helps readers apply multilevel techniques to their research. Noted as an accessible introduction, the book also includes advanced extensions, making it useful as both an introduction and as a reference to students, researchers, and methodologists. Basic models and examples are discussed in non-technical terms with an emphasis on understanding the methodological and statistical issues involved in using these models. The estimation and interpretation of multilevel models is demonstrated using realistic examples from various disciplines. For example, readers will find data sets on stress in hospitals, GPA scores, survey responses, street safety, epilepsy, divorce, an...
Brexit Britain
In June 2016, more than 17 million people voted for Britain to leave the European Union. The fallout of this momentous referendum has been tumultuous and unpredictable. Now, from the authors of the highly-acclaimed Brexit: Why Britain Voted to Leave the European Union (Cambridge University Press, 2017), comes the definitive guide to the transformation of British politics in the years following the Brexit vote. By charting the impact of Brexit on three major elections – the 2017 and 2019 general elections as well as the 2019 European Parliament elections – this book reveals the deeper currents reshaping modern Britain. The authors draw upon many years of unique and unprecedented data from their own surveys, giving key insights into how and why Brexit has changed British electoral politics. The book is written in a clear and accessible style, appealing to students, scholars and anyone interested in the impact of Brexit on Britain today.
Handbook of Advanced Multilevel Analysis
This new handbook is the definitive resource on advanced topics related to multilevel analysis. The editors assembled the top minds in the field to address the latest applications of multilevel modeling as well as the specific difficulties and methodological problems that are becoming more common as more complicated models are developed. Each chapter features examples that use actual datasets. These datasets, as well as the code to run the models, are available on the book’s website http://www.hlm-online.com . Each chapter includes an introduction that sets the stage for the material to come and a conclusion. Divided into five sections, the first provides a broad introduction to the field ...
Systematic Mixed-Methods Research for Social Scientists
This textbook provides clear and accessible guidance on the importance and practical application of mixed-methods research. Professor Olsen presents a range of multiple mixed-methods techniques using quantified data. Critical realism underpins key arguments. She offers detailed examples based on wide experience with international applied social-science projects. The book shows readers how to join quantitative and qualitative data together. Detailed methods include: using multiple-level data; constructing new indices based on mixing survey responses and personal interviews; and using focus groups alongside a large survey. The book provides readers with linkages of data between different softw...
Springer Handbook of Engineering Statistics
In today’s global and highly competitive environment, continuous improvement in the processes and products of any field of engineering is essential for survival. This book gathers together the full range of statistical techniques required by engineers from all fields. It will assist them to gain sensible statistical feedback on how their processes or products are functioning and to give them realistic predictions of how these could be improved. The handbook will be essential reading for all engineers and engineering-connected managers who are serious about keeping their methods and products at the cutting edge of quality and competitiveness.
Handbook of Multilevel Analysis
This book presents the state of the art in multilevel analysis, with an emphasis on more advanced topics. These topics are discussed conceptually, analyzed mathematically, and illustrated by empirical examples. Multilevel analysis is the statistical analysis of hierarchically and non-hierarchically nested data. The simplest example is clustered data, such as a sample of students clustered within schools. Multilevel data are especially prevalent in the social and behavioral sciences and in the biomedical sciences. The chapter authors are all leading experts in the field. Given the omnipresence of multilevel data in the social, behavioral, and biomedical sciences, this book is essential for empirical researchers in these fields.
Multilevel Analysis
Applauded for its clarity, this accessible introduction helps readers apply multilevel techniques to their research. The book also includes advanced extensions, making it useful as both an introduction for students and as a reference for researchers. Basic models and examples are discussed in nontechnical terms with an emphasis on understanding the methodological and statistical issues involved in using these models. The estimation and interpretation of multilevel models is demonstrated using realistic examples from various disciplines including psychology, education, public health, and sociology. Readers are introduced to a general framework on multilevel modeling which covers both observed and latent variables in the same model, while most other books focus on observed variables. In addition, Bayesian estimation is introduced and applied using accessible software.
Power Analysis of Trials with Multilevel Data
Power Analysis of Trials with Multilevel Data is a valuable reference for anyone who wants to perform power calculations on trials with hierarchical data. It provides a thorough overview of power analysis, familiarizing you with terminology and notation, outlining the key concepts of statistical power and power analysis, and covering all common hierarchical designs.
