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Contemporary societal problems are complex, intractable, and costly. Aiming to ameliorate them, social scientists formulate policies and programs, and conduct research testing the efficacy of the interventions. All too often the results are disappointing; partly because the theories guiding these studies are inappropriate, the study designs are flawed, and the empirical databases covering their research questions are sparse. This book confronts these problems of research by following this process: analyze the roots of the social problem both theoretically and empirically; formulate a study design that captures the nuances of the problem; gather appropriate empirical data operationalizing the study design; model these data using multilevel statistical methods to uncover potential causes and any biases to their implied effects; use the results by refining theory and by formulating evidence-based policy recommendations for implementation and testing.
Applying this process, the chapters focus on these social problems: political extremism; global human development; violence against religious minorities; computerization of work; reform of urban schools; and the utilization and costs of health care. Because these chapters exemplify the usefulness of multilevel modeling for the quantification of effects and causal inference, they can serve as vivid exemplars for the teaching of students. This use of examples reverses the usual procedure for introducing statistical methods. Rather than beginning with a new statistical model bearing on statistical theory and searching for illustrative data, each core chapter begins with a pressing social problem. The specific problem motivates theoretical analysis, gathering of relevant data, and application of appropriate statistical procedures. Readers can use the provided data sets and syntaxes to replicate, critique, and advance the analyses, thereby developing their ability to produce future applications of multilevel modeling.
The chapters address the multilevel data structures of these social problems by grouping observations on the micro units (level-1) by more macro-units (level-2) (e.g., school children are grouped by their classroom), and by conducting multilevel statistical modeling in contextual, longitudinal, and meta-analyses. Each core chapter applies a qualitative typology to nest the variance between the macro units, thereby crafting a "mixed-methods" approach that combines qualitative attributes with quantitative measures
Quotient Space Based Problem Solving provides an in-depth treatment of hierarchical problem solving, computational complexity, and the principles and applications of multi-granular computing, including inference, information fusing, planning, and heuristic search. Drawing upon years of academic research and using numerous examples and illustrative applications, the authors, Ling Zhang and Bo Zhang provide a unique guide to computerized problem solving and granular computing. This book is a valuable guide to graduate students, research fellows, and academics specializing in artificial intelligence or concerned with computerized problem solving and granular computing. It explains the theory of hierarchical problem solving, its computational complexity, and discusses the principle and applications of multi-granular computing. It describes a human-like, theoretical framework using quotient space theory, that will be of interest to researchers in artificial intelligence. It provides many applications and examples in the engineering and computer science area. It includes complete coverage of planning, heuristic search and coverage of strictly mathematical models.
For one/two semester courses in Engineering and Computer Science at the freshman/sophomore level.
Engineering Problem Solving With C++, Fourth Edition provides a clear, concise introduction to engineering problem solving with C++ as well as the object-oriented features of the C++ programming language. The authors’ proven five-step problem solving methodology is presented and then incorporated in every chapter of the text. The chapters in this text are designed to give the instructor flexibility in the ordering of topics with chapter topics covering the essentials of mathematical computations, character data, control structures, functions, arrays, classes, and pointers. Outstanding engineering and scientific applications are used throughout; all applications are centered around the theme of engineering challenges in the 21st century with an emphasis on incorporating real-world engineering and scientific examples and problems.
"[This] is a great self-help book for framing the research effort and guiding the reader through the research process. I can't name another book of this type on the market.
Doing Qualitative Research Using Your Computer is a practical, hands-on guide to using commonly available everyday technology, including Microsoft software, to manage and streamline research projects.
Chris Hahn uses straight-forward, everyday language to walk readers through this process, drawing on a wide range of examples to demonstrate how easy it is to use such software. This guide is full of useful hints and tips on how to manage research more efficiently and effectively, including:
Doing Qualitative Research Using Your Computer is ideal for those students or researchers who don't want to invest in expensive specialised software packages and who are looking for a result that is more tailored to their individual needs. It will be an invaluable companion for anyone embarking on their own research project.
This version of the author's DE text will include a new chapter on Linear Boundary Value Problems for instructors who want to add this coverage to their DE course.
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