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This book focuses on quantitative survey methodology, data collection and cleaning methods. Providing starting tools for using and analyzing a file once a survey has been conducted, it addresses fields as diverse as advanced weighting, editing, and imputation, which are not well-covered in corresponding survey books. Moreover, it presents numerous empirical examples from the author's extensive research experience, particularly real data sets from multinational surveys.
List of contents
Introduction.- Concept of survey and key survey terms.- Designing a questionnaire and survey modes.- Sampling principles and missingness mechanisms.- Design effects at sampling phase.- Sampling design data file.- Missingness, its reasons and treatment.- Weighting adjustments due to unit missingness.- Special cases in weighting.- Statistical editing.- Introduction to statistical imputation.- Imputation methods for single variables.- Summary and key tasks of survey data cleaning.- Basic survey data analysis.-
About the author
Seppo Laaksonen is a professor of statistics at the University of Helsinki, Finland, and has worked at various survey institutes including Statistics Finland, Eurostat and The Finnish Center for Social and Health Research. The former scientific secretary (2001-2003) and vice president of the International Association of Survey Statisticians (2007-2009), he has been a member of the sampling expert team of the European Social Survey since 2001. He has also been involved in a number of European research projects and is a consultant for surveys in Moldova, Ethiopia, Slovenia, the United Kingdom and Hungary.
Summary
This book focuses on quantitative survey methodology, data collection and cleaning methods. Providing starting tools for using and analyzing a file once a survey has been conducted, it addresses fields as diverse as advanced weighting, editing, and imputation, which are not well-covered in corresponding survey books. Moreover, it presents numerous empirical examples from the author's extensive research experience, particularly real data sets from multinational surveys.
