Research Methods & Encyclopedias in Social Sciences

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This wide-ranging, jargon-free dictionary contains over 2,300 entries on all aspects of statistics, including terms used in computing, mathematics, and probability. It also includes biographical information on over 200 key figures in the field and coverage of statistical journals and societies.

Here, by popular demand, is the updated edition to Joel Best's classic guide to understanding how numbers can confuse us. In his new afterword, Best uses examples from recent policy debates to reflect on the challenges to improving statistical literacy. Since its publication ten years ago, Damned Lies and Statistics has emerged as the go-to handbook for spotting bad statistics and learning to think critically about these influential numbers.

This step-by-step guide is written for R and latent variable model (LVM) novices. Utilizing a path model approach and focusing on the lavaan package, this book is designed to help readers quickly understand LVMs and their analysis in R.

Many theories in the social sciences predict spatial dependence or the similarity of behaviors at neighboring locations. Spatial Analysis for the Social Sciences demonstrates how researchers can diagnose and model this spatial dependence and draw more valid inferences as a result.

tatistics: A Short, Clear Guide is an accessible, humorous and easy introduction to statistics for social science students. 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.

Statistics for the Behavioral and Social Sciences offers a practical approach that demystifies statistics. The text will help you understand the fundamental concepts of statistics by emphasizing definitional formulas rather than rote memorization.

The second edition of Statistics for the Social Sciences prepares students from a wide range of disciplines to interpret and learn the statistical methods critical to their field of study. By using the General Linear Model (GLM), the author builds a foundation that enables students to see how statistical methods are interrelated enabling them to build on the basic skills.

Reflecting a sea change in how empirical research has been conducted over the past three decades, Foundations of Agnostic Statistics presents an innovative treatment of modern statistical theory for the social and health sciences. This book develops the fundamentals of what the authors call agnostic statistics, which considers what can be learned about the world without assuming that there exists a simple generative model that can be known to be true.

In this fully updated edition of Using Basic Statistics in the Behavioral and Social Sciences, Annabel Ness Evans presents introductory statistics in a practical, conceptual, and humorous way, reducing the anxiety that many students experience in introductory courses. Avoiding complex notation and derivations, the book focuses on helping readers develop an understanding of the underlying logic of statistics, rather than rote memorization.