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Highly recommended by the Journal of Official Statistics, The
American Statistician, and other journals, Applied Survey Data
Analysis, Second Edition provides an up-to-date overview of
state-of-the-art approaches to the analysis of complex sample
survey data. Building on the wealth of material on practical
approaches to descriptive analysis and regression modeling from the
first edition, this second edition expands the topics covered and
presents more step-by-step examples of modern approaches to the
analysis of survey data using the newest statistical software.
Designed for readers working in a wide array of disciplines who use
survey data in their work, this book continues to provide a useful
framework for integrating more in-depth studies of the theory and
methods of survey data analysis. An example-driven guide to the
applied statistical analysis and interpretation of survey data, the
second edition contains many new examples and practical exercises
based on recent versions of real-world survey data sets. Although
the authors continue to use Stata for most examples in the text,
they also continue to offer SAS, SPSS, SUDAAN, R, WesVar, IVEware,
and Mplus software code for replicating the examples on the book's
updated website.
Multiple Imputation in Practice: With Examples Using IVEware
provides practical guidance on multiple imputation analysis, from
simple to complex problems using real and simulated data sets. Data
sets from cross-sectional, retrospective, prospective and
longitudinal studies, randomized clinical trials, complex sample
surveys are used to illustrate both simple, and complex analyses.
Version 0.3 of IVEware, the software developed by the University of
Michigan, is used to illustrate analyses. IVEware can multiply
impute missing values, analyze multiply imputed data sets,
incorporate complex sample design features, and be used for other
statistical analyses framed as missing data problems. IVEware can
be used under Windows, Linux, and Mac, and with software packages
like SAS, SPSS, Stata, and R, or as a stand-alone tool. This book
will be helpful to researchers looking for guidance on the use of
multiple imputation to address missing data problems, along with
examples of correct analysis techniques.
Multiple Imputation in Practice: With Examples Using IVEware
provides practical guidance on multiple imputation analysis, from
simple to complex problems using real and simulated data sets. Data
sets from cross-sectional, retrospective, prospective and
longitudinal studies, randomized clinical trials, complex sample
surveys are used to illustrate both simple, and complex analyses.
Version 0.3 of IVEware, the software developed by the University of
Michigan, is used to illustrate analyses. IVEware can multiply
impute missing values, analyze multiply imputed data sets,
incorporate complex sample design features, and be used for other
statistical analyses framed as missing data problems. IVEware can
be used under Windows, Linux, and Mac, and with software packages
like SAS, SPSS, Stata, and R, or as a stand-alone tool. This book
will be helpful to researchers looking for guidance on the use of
multiple imputation to address missing data problems, along with
examples of correct analysis techniques.
Highly recommended by the Journal of Official Statistics, The
American Statistician, and other journals, Applied Survey Data
Analysis, Second Edition provides an up-to-date overview of
state-of-the-art approaches to the analysis of complex sample
survey data. Building on the wealth of material on practical
approaches to descriptive analysis and regression modeling from the
first edition, this second edition expands the topics covered and
presents more step-by-step examples of modern approaches to the
analysis of survey data using the newest statistical software.
Designed for readers working in a wide array of disciplines who use
survey data in their work, this book continues to provide a useful
framework for integrating more in-depth studies of the theory and
methods of survey data analysis. An example-driven guide to the
applied statistical analysis and interpretation of survey data, the
second edition contains many new examples and practical exercises
based on recent versions of real-world survey data sets. Although
the authors continue to use Stata for most examples in the text,
they also continue to offer SAS, SPSS, SUDAAN, R, WesVar, IVEware,
and Mplus software code for replicating the examples on the book's
updated website.
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