Relative Distribution Methods In The Social Sciences
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Relative Distribution Methods in the Social Sciences
Author: Mark S. Handcock
language: en
Publisher: Springer Science & Business Media
Release Date: 2006-05-10
This monograph presents methods for full comparative distributional analysis based on the relative distribution. This provides a general integrated framework for analysis, a graphical component that simplifies exploratory data analysis and display, a statistically valid basis for the development of hypothesis-driven summary measures, and the potential for decomposition - enabling the examination of complex hypotheses regarding the origins of distributional changes within and between groups. Written for data analysts and those interested in measurement, the text can also serve as a textbook for a course on distributional methods.
The SAGE Encyclopedia of Social Science Research Methods
"This defining work will be valuable to readers and researchers in social sciences and humanities at all academic levels. As a teaching resource it will be useful to instructors and students alike and will become a standard reference source. Essential for general and academic collections."--CHOICE"Appreciative users of this volume will be students, faculty, and researchers in academic, special, and large public libraries, for whom it is recommended."--LIBRARY JOURNALSAGE Reference is proud to announce The SAGE Encyclopedia of Social Science Research Methods, a three-volume resource that is a first of its kind, developed by the leading publisher of social science research methods books and journals. This unique multi-volume reference set offers readers an all-encompassing education in the ways of social science researchers. Written to be accessible to general readers, entries do not require any advanced knowledge or experience to understand the purposes and basic principles of any of the methods. The Encyclopedia features two major types of entries: definitions, consisting of a paragraph or two, provide a quick explanation of a methodological term; and topical treatments or essays discussing the nature, history, application/example and implication of using a certain method. Also included are suggested readings and references for future study. To help provide a more complete explanation than is often achieved within the scope of a single article, key terms and concepts appear in SMALL CAPITAL LETTERSto refer readers to related terms explained elsewhere. In addition to epistemological issues that influence the nature of research questions and assumptions, The SAGE Encyclopedia of Social Science Research Methods tackles topics not normally viewed as part of social science research methodology, from philosophical issues such as poststructuralismto advanced statistical techniques. In covering the full range of qualitative and quantitative data analyses, this key reference offers an integrated approach that allows the reader to choose the most appropriate and robust techniques to apply to each situation. Many entries treat traditional topics in a novel way, stimulating both interest and new perspectives. One example is the entry Econometrics, by Professor DamodarGujarati. Following a process which many educators preach but seldom practice, Gujarati walks the reader twice through the research process from economic theory to data and models to analysis, once in principle and a second time with an example. In using the ordinary process of economic research to achieve an extraordinary impact, he leaves the reader thinking not only about methods and models but also the fundamental purpose of econometrics. Topics Covered Analysis of Variance Association and Correlation Basic Qualitative Research Basic Statistics Causal Modeling (Structural Equations) Discourse/Conversation Analysis Econometrics Epistemology Ethnography Evaluation Event History Analysis Experimental Design Factor Analysis & Related Techniques Feminist Methodology Generalized Linear Models Historical/Comparative Interviewing in Qualitative Research Latent Variable Model Life History/Biography LoglinearModels (Categorical Dependent Variables) Longitudinal Analysis Mathematics and Formal Models Measurement Level Measurement Testing & Classification Multiple Regression Multilevel Analysis Qualitative Data Analysis Sampling in Surveys Sampling in Qualitative Research Scaling Significance Testing Simple Regression Survey Design Time Series Key Features Over 900 entries arranged A to Z Each entry is written by a leading authority in the field, covering both quantitative and qualitative methods Covers all disciplines within the social sciences Contains both concise definitions and in-depth essays Three volumes and more than 1500 pages
Introduction to Applied Bayesian Statistics and Estimation for Social Scientists
Author: Scott M. Lynch
language: en
Publisher: Springer Science & Business Media
Release Date: 2007-06-30
"Introduction to Applied Bayesian Statistics and Estimation for Social Scientists" covers the complete process of Bayesian statistical analysis in great detail from the development of a model through the process of making statistical inference. The key feature of this book is that it covers models that are most commonly used in social science research - including the linear regression model, generalized linear models, hierarchical models, and multivariate regression models - and it thoroughly develops each real-data example in painstaking detail. The first part of the book provides a detailed introduction to mathematical statistics and the Bayesian approach to statistics, as well as a thorough explanation of the rationale for using simulation methods to construct summaries of posterior distributions. Markov chain Monte Carlo (MCMC) methods - including the Gibbs sampler and the Metropolis-Hastings algorithm - are then introduced as general methods for simulating samples from distributions. Extensive discussion of programming MCMC algorithms, monitoring their performance, and improving them is provided before turning to the larger examples involving real social science models and data.