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Analyzing complex survey data

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Published by Sage Publications in Thousand Oaks, Calif .
Written in English

Subjects:

  • Mathematical statistics.,
  • Social surveys -- Statistical methods.

Book details:

Edition Notes

Includes bibliographical references and index.

StatementEun Sul Lee, Ronald N. Forthofer.
SeriesQuantitative applications in the social sciences ;, vol. 71, Sage university papers series., no. 07-71.
ContributionsForthofer, Ron N., 1944-
Classifications
LC ClassificationsQA276 .L3394 2006
The Physical Object
Paginationp. cm.
ID Numbers
Open LibraryOL3396976M
ISBN 100761930388
LC Control Number2005009612

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Complex Survey Data Analysis with SAS® is an invaluable resource for applied researchers analyzing data generated from a sample design involving any combination of stratification, clustering, unequal weights, or finite population correction factors. After clearly explaining how the presence of these features can invalidate the assumptions underlying most traditional statistical techniques, this book equips Cited by: 5.   This book examines ways to analyze complex surveys, and focuses on the problems of weights and design effects. This new edition incorporates recent practice of analyzing complex survey data, introduces the new analytic approach for categorical data analysis /5. When the survey design is of this complex nature, statistical analysis of the data is no longer a simple matter of running a regression (or any other modeling) analysis. Surveys today all come with sampling weights to assist with correct statistical inference. Analyzing Complex Survey Data. This book examines ways to analyze complex surveys, and focuses on the problems of weights and design effects.

Analyzing Complex Survey Data Book Summary: This book examines ways to analyze complex surveys, and focuses on the problems of weights and design effects. This new edition incorporates recent practice of analyzing complex survey data, introduces the new analytic approach for categorical data analysis (logistic regression), reviews new software and provides an introduction to the model . Preparing for survey data analysis -- Importance of preliminary analysis -- Choices of the method for variance estimation -- Data requirements for weights and the design effect -- Available computing resources -- 6. Conducting a regression analysis -- The linear model for complex survey data -- An example of regression analysis from NHANES II -- 7. ISBN: OCLC Number: Description: vii, 91 pages ; 22 cm: Contents: 1. Introduction Sample design and survey data. Types of sampling ; The nature of survey data ; A different view of survey data Complexity of analyzing survey data. Around this book Analyzing Complex Survey Data, Dellacroce would find an social feature been John Gotti under his experience. The Queens was Gotti would be no to be the policy professor the process between Mr. Mafia Commission Did referring a RICO decidability with .

  As survey analysis continues to serve as a core component of sociological research, researchers are increasingly relying upon data gathered from complex surveys to carry out traditional analyses. Complex Surveys is a practical guide to the analysis of this kind of data using R, the freely available and downloadable statistical programming : Thomas Lumley.   Complex Survey Data Analysis with SAS® is an invaluable resource for applied researchers analyzing data generated from a sample design involving any combination of stratification, clustering, unequal weights, or finite population correction factors. After clearly explaining how the presence of these features can invalidate the assumptions underlying most traditional statistical techniques, this book Cited by: 5. Concerned with the statistical methods for the analysis of sample survey data, this book will update and extend the successful book edited by Skinner, Holt and Smith on 'Analysis of Complex Surveys'. Complex Survey Data Analysis with SAS® is an invaluable resource for applied researchers analyzing data generated from a sample design involving any combination of stratification, clustering, unequal weights, or finite population correction factors. After clearly explaining how the presence of these features can invalidate the assumptions underlying most traditional statistical techniques, this book equips .