The NORC General Social Survey: A User's Guide / Edition 1

by James A. Davis, Tom W. Smith

Overview

The answers to questions on a wide variety of social and political issues from more than 25,000 respondents are contained in the General Social Survey (GSS) data base. The authors, who have directed the GSS since its inception, have set out to enable social scientists to exploit this large data set more effectively.

The book outlines such topics as the recurrent, replicated ‘core' items suitable for trend analyses, the annual topical modules on subjects of current interest and the international modules produced in collaboration with the International Social Survey Programme.

Product Details

ISBN-13:	9780803943674
Publisher:	SAGE Publications
Publication date:	11/08/1991
Series:	Guides to Major Social Science Data Bases , #1
Pages:	104
Product dimensions:	5.51(w) x 8.66(h) x (d)

About the Author

Professor of mathematics

Tom W. Smith specializes in the study of societal change in comparative perspective. He is director of the Center for the Study of Politics and Society at NORC, University of Chicago. Since 1980, he has been a principal investigator of the National Data Program for the Social Sciences and director of its General Social Survey (GSS).

He is also cofounder of the International Social Survey Program (ISSP). It has conducted an international survey annually since 1985 and has involved 60 nations. He was Secretary General of ISSP in 1997–2003.

Smith has taught at Purdue University, Northwestern University, the University of Chicago, Tel Aviv University, and the University of Cologne.

He was awarded the 2000 and 2003 Innovators Awards of the American Association for Public Opinion Research (AAPOR), the 2002 AAPOR Award for Exceptionally Distinguished Achievement, and the 2013 AAPOR Book Award as coeditor of Multinational, Multicultural, and Multiregional Survey Methods.

Table of Contents

Introduction
Content
Study Design
Sample Design and Weighting
Field Procedures
Data Preparation and Distribution
Data Analysis