This book focuses on survey theory and applications, providing insight and innovative solutions to face problems in data collection and integration, complex sample design, opinion questionnaire design, and statistical estimation.
Formal rigour and simple language, together with real-life examples, will make the book suitable to both practitioners involved in applied research and to academics interested in scientific developments in the survey field.
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
In this refreshing book, experienced author and academic Neil Burdess shows that statistics are not the result of some mysterious "black magic", but rather the result of some very basic arithmetic. Getting rid of confusing x's and y's, he shows that it's the intellectual questions that come before and after the calculations that are important: (i) What are the best statistics to use with your data? and (ii) What do the calculated statistics tell you?
Statistics: A Short, Clear Guide aims to help students make sense of the logic of statistics and to decide how best to use statistics to analyse their own data. What's more, it is not reliant on students having access to any particular kind of statistical software package.
This is a very useful book for any student in the social sciences doing a statistics course or needing to do statistics for themselves for the first time.
Most books and articles about research in criminology and criminal justice focus on how the research was carried out: the data that were used, the methods that were applied, the results that were achieved. While these are all important, they do not present a complete picture. Envisioning Criminology: Researchers on Research as a Process of Discovery aims to fill that gap by providing nuance--the “back story” of why researchers selected particular problems, how they approached those problems, and how their background, training, and experience affected the approaches they took.
As the contributions in this book demonstrate, research is not a cut-and-dried process, as all too many methods books imply, but a living, breathing–and in some ways quirky–process that is influenced by non-“scientific” factors. The path taken by a researcher is important, and an appreciation of his or her background, experience, knowledge–and the setbacks and triumphs of performing the research–provides a much more complete picture of how research is done.
The twenty-eight chapters in this book describe the back stories of their authors, which serve to enlighten readers about the interplay between the personal and the methodological. While primarily aimed as a textbook, this work will also be of interest to researchers in Criminology and Criminal Justice, and related Social and Behavioral Science fields as an account of how seminal researchers in the field developed their key contributions.
It introduces the intuitive thinking behind standard procedures, explores the process of informal reasoning, and uses conceptual frameworks to provide a foundation for students new to statistics. It showcases the expertise we have all developed from living in a data saturated society, increases our statistical literacy and gives us the tools needed to approach statistical mathematics with confidence.
Key topics include:Variability Standard Distributions Correlation Relationship Sampling Inference
An engaging, informal introduction this book sets out the conceptual tools required by anyone undertaking statistical procedures for the first time or for anyone needing a fresh perspective whilst studying the work of others.
NEW TO THIS EDITION
Demonstrating the importance of quantifying, or testing, theory is frequently overlooked in university courses. Too often the sole focus is either an exploration social theory or a routine of performing quantitative methodological procedures. Students seldom receive exposure to practical applications that clearly illustrate the use of the latter to test the former. The unfortunate consequence is that students often fail to grasp the vital relationship between theory and methods, which is the basis of future sociological research.
The majority of single author sociological methods books exist in the form of undergraduate texts. Because sociologists are versed in the basics of quantitative and qualitative methodology, solo academics can reasonably author introductory texts that glean the necessary basics of both quantitative and qualitative methods. However, the same is not true of providing adequate intermediate and advanced level methodological instruction.
There is a considerable market for edited volumes of qualitative methodology. The practical benefit of such collections—both for instructors and students—is a selection of diverse topics in which researchers devote considerable attention to specific qualitative procedures. In short, an assortment of contributors can better provide intensive applications of different qualitative procedures that address unique research questions, and in a variety of settings. The end product typically incorporates a useful breadth of sociological topics, but with the requisite methodological depth (i.e. attention to procedure and depth of analysis) that is otherwise difficult for any single author to accomplish. To date, edited volumes of qualitative research are abundant, while similar quantitative compilations are rare.
Key FeaturesReaders learn how to construct geometric spaces from relevant data, formulate questions of interest, and link statistical interpretation to geometric representations.They also learn how to perform structured data analysis and to draw inferential conclusions from MCA.The text uses real examples to help explain concepts.The authors stress the distinctive capacity of MCA to handle full-scale research studies.
This supplementary text is appropriate for any graduate-level, intermediate, or advanced statistics course across the social and behavioral sciences, as well as for individual researchers.
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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.
Beginning with a presentation of random variables and the expected value of a random variable, the book covers such topics as: the definition of reliability as a coefficient and possible uses of a coefficient; the notion of parallel tests so as to make possible the estimation of a reliability coefficient for a set of measurements; what to do when parallel tests are not available; what factors affect the reliability coefficient; and how to estimate the
No prior knowledge of statistics or SPSS is assumed, and everything in the book is carefully explained in a helpful and user-friendly way using worked examples. This book is the perfect companion for students from a range of disciplines including psychology, business, communication, education, health, humanities, marketing and nursing – many of whom are unaware that this extremely helpful program is available at their institution for their use.
The first section reviews basic components of applied demography as a context for understanding and addressing societal issues. It details the methods, techniques and data sources applied by demographers in a variety of areas. Coverage includes cohort analysis, data standardization, population estimation, and the use of geographic in- formation systems (GIS). The second section focuses on the substantive areas in which demography is currently applied. The topics covered include business demography, health demography, political demography, educational demography, and applications to urban and regional planning. The book illustrates the many ways in which demographers contribute to the formulation of public policy and the resolution of societal issues.
This book will prove to be equally useful for students conducting small research projects in the social sciences or related professional/applied areas, researchers planning systematic data collection for applied purposes and policy makers who want to understand and analyse the research with whose conclusions they are presented.
Excel, a widely available computer program for students and managers, is also an effective teaching and learning tool for quantitative analyses in social science courses. Its powerful computational ability and graphical functions make learning statistics much easier than in years past. However, Excel 2013 for Social Science Statistics: A Guide to Solving Practical Problems is the first book to capitalize on these improvements by teaching students and managers how to apply Excel to statistical techniques necessary in their courses and work.
Each chapter explains statistical formulas and directs the reader to use Excel commands to solve specific, easy-to-understand social science problems. Practice problems are provided at the end of each chapter with their solutions in an Appendix. Separately, there is a full Practice Test (with answers in an Appendix) that allows readers to test what they have learned.Includes 167 illustrations in colorSuitable for upper undergraduates or graduate students
This book is for use in a two-semester graduate course sequence covering basic univariate and bivariate statistics and regression models for nominal and ordinal outcomes, in addition to covering ordinary least squares regression.
Key features of the book include:
interweaving the teaching of statistical concepts with examples developed for the course from publicly-available social science data or drawn from the literature
thorough integration of teaching statistical theory with teaching data processing and analysis
teaching of both SAS and Stata "side-by-side" and use of chapter exercises in which students practice programming and interpretation on the same data set and course exercises in which students can choose their own research questions and data set.
This book is for a two-semester course. For a one-semester course, see http://www.routledge.com/9780415991544/
Help is here! This book unpacks these statistical techniques in easy-to-understand language with fully annotated examples using the statistical software Stata. The techniques are explained without reliance on equations and algebra so that new users will understand when to use these approaches and how they are really just special applications of ordinary regression. Using real life data, the authors show you how to model random intercept models and random coefficient models for cross-sectional data in a way that makes sense and can be retained and repeated.
This book is the perfect answer for anyone who needs a clear, accessible introduction to multilevel modeling.
This fully revised, expanded and updated Second Edition of the best-selling textbook by Jane Fielding and Nigel Gilbert provides a comprehensive yet accessible guide to quantitative data analysis. Designed to help take the fear out of the use of numbers in social research, this textbook introduces students to statistics as a powerful means of revealing patterns in human behaviour.
The textbook covers everything typically included in an introductory course on social statistics for students in the social sciences and the authors have taken the opportunity of this Second Edition to bring the data sources as current as possible. The book is full of up-to-date examples and useful and clear illustrations using the latest SPSS software.
While maintaining the student-friendly elements of the first, such as chapter summaries, exercises at the end of each chapter, and a glossary of key terms, new features to this edition include:
- Updated examples and references
SPSS coverage and screen-shots now incorporate the current version 14.0 and are used to demonstrate the latest social statistics datasets;
- Additions to content include a brand new section on developing a coding frame and an additional discussion of weighting counts as a means of analyzing published statistics;
- Enhanced design aids navigation which is further simplified by the addition of core objectives for each chapter and bullet-pointed chapter summaries;
- The updated Website at http:/www.soc.surrey.ac.uk/uss/index.html reflects changes made to the text and provides updated datasets;
A valuable and practical guide for students dealing with the large amounts of data that are typically collected in social surveys, the Second Edition of Understanding Social Statistics is an essential textbook for courses on statistics and quantitative research across the social sciences.
Advances in Comparative Survey Methodology examines the most recent advances in methodology and operations as well as the technical developments in international survey research. With contributions from a panel of international experts, the text includes information on the use of Big Data in concert with survey data, collecting biomarkers, the human subject regulatory environment, innovations in data collection methodology and sampling techniques, use of paradata across the survey lifecycle, metadata standards for dissemination, and new analytical techniques.
This important resource:Contains contributions from key experts in their respective fields of study from around the globe Highlights innovative approaches in resource poor settings, and innovative approaches to combining survey and other data Includes material that is organized within the total survey error framework Presents extensive and up-to-date references throughout the book
Written for students and academic survey researchers and market researchers engaged in comparative projects, this text represents a unique collaboration that features the latest methodologies and research on global comparative surveys.
In this logical, easy-to-follow and exceptionally clear book, David Flora provides a comprehensive survey of the major statistical procedures currently used. His innovative model-based approach teaches you how to:Understand and choose the right statistical model to fit your data Match substantive theory and statistical models Apply statistical procedures hands-on, with example data analyses Develop and use graphs to understand data and fit models to data Work with statistical modeling principles using any software package Learn by applying, with input and output files for R, SAS, SPSS, and Mplus.
Statistical Methods for the Social and Behavioural Sciences: A Model Based Approach is the essential guide for those looking to extend their understanding of the principles of statistics, and begin using the right statistical modeling method for their own data. It is particularly suited to second or advanced courses in statistical methods across the social and behavioural sciences.