The Collected Works of John W. Tukey


Book Description

This book includes a collection of John W. Tukey's papers that demonstrate a number of numerical methods and graphical methods, such as box plots, stem-and-leaf diagrams, and point cloud rotation, for graphics and exploratory data analysis.




Bourdieu and Data Analysis


Book Description

Uniquely amongst the numerous publications to appear on the work of the French social theorist Pierre Bourdieu, this book deals with data analysis, examining a range of techniques and instruments. After an introductory chapter outlining the key principles of Bourdieu's theory, the book presents detailed examples of data being collected and analysed in a Bourdieusian way across various social science contexts. Both qualitative and quantitative methods are addressed, including analysis of the strengths and weaknesses of each method, as are common data collection procedures such as interview, observation and questionnaire. Examples of Multiple Correspondence Analysis are an important feature of the book, since this was an approach particularly favoured by Bourdieu. In each case study, the pros and cons of different approaches are highlighted and the qualitative/quantitative debate is thoroughly explored. Overall, the book offers readers a blueprint to develop their own methodological plans for using Bourdieu in research practice.




The Philosophy of Quantitative Methods


Book Description

The Philosophy of Quantitative Methods focuses on the conceptual foundations of research methods within the behavioral sciences. In particular, it undertakes a close philosophical examination of a variety of quantitative research methods that are prominent in (or relevant for) the conduct of research in these fields. By doing so, the deep structure of these methods is examined in order to overcome the non-critical approaches typically found in the existing literature today. In this book, Brian D. Haig focuses on the more well-known research methods such as exploratory data analysis, statistical significant testing, Bayesian confirmation theory and statistics, meta-analysis, and exploratory factor analysis. These methods are then examined with a philosophy consistent of scientific realism. In addition, each chapter provides a helpful Further Reading section in order to better assist the reader in extending their own thinking and research methods specific to their needs.




The Collected Works of John W. Tukey


Book Description

These papers illustrate important features characteristic of John Tukey's work, namely the desire to look beyond or beneath conventional set structures, the wish to detect and deal with anomalous behavior, and great technical ingenuity.




The SAGE Handbook of Case-Based Methods


Book Description

This handbook provides a clear examination of case-oriented research. It defines case-based social research as a subfield of methodology.




Handbook of Psychology, Research Methods in Psychology


Book Description

Includes established theories and cutting-edge developments. Presents the work of an international group of experts. Presents the nature, origin, implications, an future course of major unresolved issues in the area.




Theory-Based Data Analysis for the Social Sciences


Book Description

This book presents the elaboration model for the multivariate analysis of observational quantitative data. This model entails the systematic introduction of "third variables" to the analysis of a focal relationship between one independent and one dependent variable to ascertain whether an inference of causality is justified. Two complementary strategies are used: an exclusionary strategy that rules out alternative explanations such as spuriousness and redundancy with competing theories, and an inclusive strategy that connects the focal relationship to a network of other relationships, including the hypothesized causal mechanisms linking the focal independent variable to the focal dependent variable. The primary emphasis is on the translation of theory into a logical analytic strategy and the interpretation of results. The elaboration model is applied with case studies drawn from newly published research that serve as prototypes for aligning theory and the data analytic plan used to test it; these studies are drawn from a wide range of substantive topics in the social sciences, such as emotion management in the workplace, subjective age identification during the transition to adulthood, and the relationship between religious and paranormal beliefs. The second application of the elaboration model is in the form of original data analysis presented in two Analysis Journals that are integrated throughout the text and implement the full elaboration model. Using real data, not contrived examples, the text provides a step-by-step guide through the process of integrating theory with data analysis in order to arrive at meaningful answers to research questions.




Handbook of Psychology, Research Methods in Psychology


Book Description

Includes established theories and cutting-edge developments. Presents the work of an international group of experts. Presents the nature, origin, implications, an future course of major unresolved issues in the area.




Understanding Clinical Data Analysis


Book Description

This textbook consists of ten chapters, and is a must-read to all medical and health professionals, who already have basic knowledge of how to analyze their clinical data, but still, wonder, after having done so, why procedures were performed the way they were. The book is also a must-read to those who tend to submerge in the flood of novel statistical methodologies, as communicated in current clinical reports, and scientific meetings. In the past few years, the HOW-SO of current statistical tests has been made much more simple than it was in the past, thanks to the abundance of statistical software programs of an excellent quality. However, the WHY-SO may have been somewhat under-emphasized. For example, why do statistical tests constantly use unfamiliar terms, like probability distributions, hypothesis testing, randomness, normality, scientific rigor, and why are Gaussian curves so hard, and do they make non-mathematicians getting lost all the time? The book will cover the WHY-SOs.