Dr Keming Yang is Senior Lecturer of Sociology at School of Applied Social Sciences, University of Durham, UK. He holds a PhD in Sociology and an MA in Statistics from Columbia University (New York, USA). As a Fellow and Chartered Statistician at Royal Statistical Society (RSS), he has been teaching statistics to social science students at both undergraduate and postgraduate levels for more than ten years in the UK. His main publication in statistics is Making Sense of Statistical Methods in Social Research (Sage, 2010). He is also the author of Entrepreneurship in China (Ashgate, 2007), Capitalists in a Communist Regime (Palgrave Macmillan, 2012), and some papers on loneliness, in which he employs statistical methods for answering important substantive research questions.

The Position of Statistical Methods in Social Research
Introduction
The Use of Statistical Methods in Social Research
Cases and Variables
The Logic of Sampling
Estimating and Measuring One Important Thing
Studying the Relationship between Two Variables
Linear Regression Models and Their Generalizations
Time Matters
Statistical Case-Oriented Methods
Methods for Analyzing Latent Variables
Causal Analysis

Making Sense of Statistical Methods in Social Research is a critical introduction to the use of statistical methods in social research. It provides a unique approach to statistics that concentrates on helping social researchers think about the conceptual basis for the statistical methods they're using.

Whereas other statistical methods books instruct students in how to get through the statistics-based elements of their chosen course with as little mathematical knowledge as possible, this book aims to improve students' statistical literacy, with the ultimate goal of turning them into competent researchers.

Making Sense of Statistical Methods in Social Research contains careful discussion of the conceptual foundation of statistical methods, specifying what questions they can, or cannot, answer. The logic of each statistical method or procedure is explained, drawing on the historical development of the method, existing publications that apply the method, and methodological discussions. Statistical techniques and procedures are presented not for the purpose of showing how to produce statistics with certain software packages, but as a way of illuminating the underlying logic behind the symbols.

The limited statistical knowledge that students gain from straight forward 'how-to' books makes it very hard for students to move beyond introductory statistics courses to postgraduate study and research. This book should help to bridge this gap.