A guide to the basic issues in data analysis, such as the construction of variables, the characterization of distributions and the notions of inference. It also seeks to enhance debate in the field by tackling advanced topics such as models of change, causality, panel models and network analysis.
'This book provides an excellent reference guide to basic theoretical arguments, practical quantitative techniques and the methodologies that the majority of social science researchers are likely to require for postgraduate study and beyond' - "Environment and Planning". 'The book provides researchers with guidance in, and examples of, both quantitative and qualitative modes of analysis, written by leading practitioners in the field. The editors give a persuasive account of the commonalities of purpose that exist across both modes, as well as demonstrating a keen awareness of the different things that each offers the practising researcher' - Clive Seale, Brunel University.'With the appearance of this handbook, data analysts no longer have to consult dozens of disparate publications to carry out their work. The essential tools for an intelligent telling of the data story are offered here, in thirty chapters written by recognized experts ' - Michael Lewis-Beck, F Wendell Miller Distinguished Professor of Political Science, University of Iowa. 'This is an excellent guide to current issues in the analysis of social science data.; I recommend it to anyone who is looking for authoritative introductions to the state of the art. Each chapter offers a comprehensive review and an extensive bibliography and will be invaluable to researchers wanting to update themselves about modern developments' - Professor Nigel Gilbert, Pro Vice-Chancellor and Professor of Sociology, University of Surrey.This is a book that will rapidly be recognized as the bible for social researchers. It provides a first-class, reliable guide to the basic issues in data analysis, such as the construction of variables, the characterization of distributions and the notions of inference. Scholars and students can turn to it for teaching and applied needs with confidence. The book also seeks to enhance debate in the field by tackling more advanced topics such as models of change, causality, panel models and network analysis. Specialists will find much food for thought in these chapters. A distinctive feature of the book is the breadth of coverage. No other book provides a better one-stop survey of the field of data analysis.; In 30 specially commissioned chapters the editors aim to encourage readers to develop an appreciation of the range of analytic options available, so they can choose a research problem and then develop a suitable approach to data analysis.
Introduction: Common Threads among Techniques of Data Analysis - Melissa Hardy and Alan Bryman PART ONE: FOUNDATIONS Constructing Variables - Alan Bryman and Duncan Cramer Summarizing Distributions - Melissa Hardy Inference - Lawrence Hazelrigg Strategies for Analysis of Incomplete Data - Mortaza Jamshidian Feminist Issues in Data Analysis - Mary Maynard Historical Analysis - Dennis Smith PART TWO: THE GENERAL LINEAR MODEL AND EXTENSIONS Multiple Regression Analysis - Ross M. Stolzenberg Incorporating Categorical Information into Regression Models: The Utility of Dummy Variables - Melissa Hardy and John Reynolds Analyzing Contingent Effects in Regression Models - James Jaccard and Tonya Dodge Regression Models for Categorical Outcomes - J Scott Long and Simon Cheng Log-Linear Analysis - Douglas L Anderton and Eric Cheney PART THREE: LONGITUDINAL MODELS Modeling Change - Nancy Brandon Tuma Analyzing Panel Data: Fixed- and Random-Effects Models - Trond Petersen Longitudinal Analysis for Continuous Outcomes: Random Effects Models and Latent Trajectory Models - Guang Guo and John Hipp Event History Analysis - Paul Allison Sequence Analysis and Optimal Matching Techniques for Social Science Data - Heather MacIndoe and Andrew Abbott PART FOUR: NEW DEVELOPMENTS IN MODELING Sample Selection Bias Models - Vincent Kang Fu, Christopher Winship and Robert D Mare Structural Equation Modeling - Jodie B Ullman and Peter M Bentler Multilevel Modelling - William Browne and Jon Rasbash Causal Inference in Sociological Studies - Christopher Winship and Michael Sobel The Analysis of Social Networks - Ronald L Breiger PART FIVE: ANALYZING QUALITATIVE DATA Tools for Qualitative Data Analysis - Raymond M Lee and Nigel G Fielding Content Analysis - Roberto P Franzosi Semiotics and Data Analysis - Peter K Manning Conversation Analysis - Steven E Clayman and Virginia Teas Gill Discourse Analysis - Jonathan Potter Grounded Theory - Nick Pidgeon and Karen Henwood The Uses of Narrative in Social Science Research - Barbara Czarniawska Qualitative Research and the Postmodern Turn - Sara Delamont and Paul Atkinson