This book presents various recently developed and traditional statistical techniques, which are increasingly being applied in social science research. The social sciences cover diverse phenomena arising in society, the economy and the environment, some of which are too complex to allow concrete statements; some cannot be defined by direct observations or measurements; some are culture- (or region-) specific, while others are generic and common. Statistics, being a scientific method - as distinct from a 'science' related to any one type of phenomena - is used to make inductive inferences regarding various phenomena. The book addresses both qualitative and quantitative research (a combination of which is essential in social science research) and offers valuable supplementary reading at an advanced level for researchers.

Written by a sociologist, a graph theorist, and a statistician, this title provides social network analysts and students with a solid statistical foundation from which to analyze network data. Clearly demonstrates how graph-theoretic and statistical techniques can be employed to study some important parameters of global social networks. The authors uses real life village-level social networks to illustrate the practicalities, potentials, and constraints of social network analysis ( SNA ). They also offer relevant sampling and inferential aspects of the techniques while dealing with potentially large networks.

Intended Audience This supplemental text is ideal for a variety of graduate and doctoral level courses in social network analysis in the social, behavioral, and health sciences "

This book presents various recently developed and traditional statistical techniques, which are increasingly being applied in social science research. The social sciences cover diverse phenomena arising in society, the economy and the environment, some of which are too complex to allow concrete statements; some cannot be defined by direct observations or measurements; some are culture- (or region-) specific, while others are generic and common. Statistics, being a scientific method - as distinct from a 'science' related to any one type of phenomena - is used to make inductive inferences regarding various phenomena. The book addresses both qualitative and quantitative research (a combination of which is essential in social science research) and offers valuable supplementary reading at an advanced level for researchers.

This book is a follow up of "Theory of Optimal Designs" by K.R.Shah and Bikas K.Sinha published in this series. Unlike the previous book, this one covers a wide range of topics in both discrete and continuous optimal designs. The topics discussed include designs for regression models, covariates models, models with trend effects, and models with competition effects. An extensive study on a new optimality criterion is also presented. The prerequisites are a basic course in the design and analysis of experiments and some familiarity with the concepts of optimality criteria. Erkki P. Liski is Professor of Statistics at the University of Tampere in Finland. He has been head of various research projects on theory and applications of statistics with the Academy of Finland. Nripes K. Mandal is Professor of Statistics at Calcutta University in India. He is closely associated with all academic activities of the Calcutta Statistical Association. Kirti R. Shah is Professor of Statistics at the University of Waterloo in Canada. He is currently the president of the International Indian Statistical Association. Bikas K. Sinha is Professor of Statistics at the Indian Statistical Institute in Calcutta, India. He has served on the editorial board of a number of international statistical journals.