Bruce Frey's There's a Stat for That! is a brief, straightforward, and to-the-point guide to deciding which statistical analysis to use and when to use it. Designed for consultants, researchers, students, and those who already have the resources to tell them how to perform the analyses, this text explains why a particular statistical approach is the right one to use. The book affirms that regardless of the group design, once the variables are chosen and the measurement strategy is worked out, one can rest assured that there is a stat for that!
With its emphasis on practical and conceptual aspects, rather than mathematics or formulas, this accessible book has established itself as the go-to resource on confirmatory factor analysis (CFA). Detailed, worked-through examples drawn from psychology, management, and sociology studies illustrate the procedures, pitfalls, and extensions of CFA ......
Providing a modern treatment of regression analysis, linear models and closely related methods, this book introduces students to one of the most useful and widely used statistical tools for social research.
This book provides a new approach to statistics in plain English, and walks readers through basic concepts, as well as some of the most complex statistical models in use. The Second Edition contains new chapters on "big data" on the one hand, and on small data situations at the other end of the spectrum; on missing data; and on effect sizes. The two "characters" in the text (a high school principal, and a director of public health) return in the Second Edition, and their needs to make sense of the data available to them have been updated with reference to contemporary concerns in the fields of education and health.
With its emphasis on practical and conceptual aspects, rather than mathematics or formulas, this accessible book has established itself as the go-to resource on confirmatory factor analysis (CFA). Detailed, worked-through examples drawn from psychology, management, and sociology studies illustrate the procedures, pitfalls, and extensions of CFA ......
The updated Second Edition of Alan C. Elliott and Wayne A. Woodward's "cut to the chase" IBM SPSS guide quickly explains the when, where, and how of statistical data analysis as it is used for real-world decision making in a wide variety of disciplines. This one-stop reference provides succinct guidelines for performing an analysis using SPSS software, avoiding pitfalls, interpreting results, and reporting outcomes. Written from a practical perspective, IBM SPSS by Example, Second Edition provides a wealth of information-from assumptions and design to computation, interpretation, and presentation of results-to help users save time, money, and frustration. New to this edition: Step-by-step SPSS instructions have been integrated into every example. An all-new chapter describes the three methods used by SPSS to create graphics. A new Chapter 10 on Factor Analysis has been added. Examples in every chapter have been enhanced with added discussion and more detail. Additional screen shots and a redesigned format make the book easier to read and information easier to locate. All examples have been updated to reflect features in the latest version of SPSS.
'The editors of the new SAGE Handbook of Regression Analysis and Causal Inference have assembled a wide-ranging, high-quality, and timely collection of articles on topics of central importance to quantitative social research, many written by leaders in the field. Everyone engaged in statistical analysis of social-science data will find something of interest in this book.' - John Fox, Professor, Department of Sociology, McMaster University 'The authors do a great job in explaining the various statistical methods in a clear and simple way - focussing on fundamental understanding, interpretation of results, and practical application - yet being precise in their exposition.' - Ben Jann, Executive Director, Institute of Sociology, University of Bern 'Best and Wolf have put together a powerful collection, especially valuable in its separate discussions of uses for both cross-sectional and panel data analysis.' -Tom Smith, Senior Fellow, NORC, University of Chicago Edited and written by a team of leading international social scientists, this Handbook provides a comprehensive introduction to multivariate methods. The Handbook focuses on regression analysis of cross-sectional and longitudinal data with an emphasis on causal analysis, thereby covering a large number of different techniques including selection models, complex samples, and regression discontinuities. Each Part starts with a non-mathematical introduction to the method covered in that section, giving readers a basic knowledge of the method's logic, scope and unique features. Next, the mathematical and statistical basis of each method is presented along with advanced aspects. Using real-world data from the European Social Survey (ESS) and the Socio-Economic Panel (GSOEP), the book provides a comprehensive discussion of each method's application, making this an ideal text for PhD students and researchers embarking on their own data analysis.
These four volumes provide a collection of key publications on categorical data analysis, carefully put together so that the reader can easily navigate, understand and put in context the major concepts and methods of analysing categorical data. The major work opens with a series of papers that address general issues in CDA, and progresses with publications which follow a logical movement from the statistics for analysing a single categorical variable, to those for studying the relationships between two and more categorical variables, and to categorical variables in some of more advanced methods, such as latent class analysis. Edited and introduced by a leading voice in the field, this collection helpfully includes both theoretical and applied items on its theme, in order to help the reader understand the methods and use them in empirical research. Volume 1: Basic Concepts and Principles Volume 2: Statistical Methods for Analysing Associations Volume 3: Log-Linear and Logistic Regression Models Volume 4: Advanced and Graphical Statistical Methods
