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Highly recommended by JASA, Technometrics, and other leading
statistical journals, the first two editions of this bestseller
showed how to easily perform complex linear mixed model (LMM)
analyses via a variety of software programs. Linear Mixed Models: A
Practical Guide Using Statistical Software, Third Edition continues
to lead readers step-by-step through the process of fitting LMMs.
The third edition provides a comprehensive update of the available
tools for fitting linear mixed-effects models in the newest
versions of SAS, SPSS, R, Stata, and HLM. All examples have been
updated, with a focus on new tools for visualization of results and
interpretation. New conceptual and theoretical developments in
mixed-effects modeling have been included, and there is a new
chapter on power analysis for mixed-effects models.
Features:*Dedicates an entire chapter to the key theories
underlying LMMs for clustered, longitudinal, and repeated measures
data *Provides descriptions, explanations, and examples of software
code necessary to fit LMMs in SAS, SPSS, R, Stata, and HLM
*Contains detailed tables of estimates and results, allowing for
easy comparisons across software procedures *Presents step-by-step
analyses of real-world data sets that arise from a variety of
research settings and study designs, including hypothesis testing,
interpretation of results, and model diagnostics *Integrates
software code in each chapter to compare the relative advantages
and disadvantages of each package *Supplemented by a website with
software code, datasets, additional documents, and updates Ideal
for anyone who uses software for statistical modeling, this book
eliminates the need to read multiple software-specific texts by
covering the most popular software programs for fitting LMMs in one
handy guide. The authors illustrate the models and methods through
real-world examples that enable comparisons of model-fitting
options and results across the software procedures.
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.
Interviewer Effects from a Total Survey Error Perspective presents
a comprehensive collection of state-of-the-art research on
interviewer-administered survey data collection. Interviewers play
an essential role in the collection of the high-quality survey data
used to learn about our society and improve the human condition.
Although many surveys are conducted using self-administered modes,
interviewer-administered modes continue to be optimal for surveys
that require high levels of participation, include
difficult-to-survey populations, and collect biophysical data.
Survey interviewing is complex, multifaceted, and challenging.
Interviewers are responsible for locating sampled units, contacting
sampled individuals and convincing them to cooperate, asking
questions on a variety of topics, collecting other kinds of data,
and providing data about respondents and the interview environment.
Careful attention to the methodology that underlies survey
interviewing is essential for interviewer-administered data
collections to succeed. In 2019, survey methodologists, survey
practitioners, and survey operations specialists participated in an
international workshop at the University of Nebraska-Lincoln to
identify best practices for surveys employing interviewers and
outline an agenda for future methodological research. This book
features 23 chapters on survey interviewing by these worldwide
leaders in the theory and practice of survey interviewing. Chapters
include: The legacy of Dr. Charles F. Cannell's groundbreaking
research on training survey interviewers and the theory of survey
interviewing Best practices for training survey interviewers
Interviewer management and monitoring during data collection The
complex effects of interviewers on survey nonresponse Collecting
survey measures and survey paradata in different modes Designing
studies to estimate and evaluate interviewer effects Best practices
for analyzing interviewer effects Key gaps in the research
literature, including an agenda for future methodological research
Chapter appendices available to download from
https://digitalcommons.unl.edu/sociw/ Written for managers of
survey interviewers, survey methodologists, and students interested
in the survey data collection process, this unique reference uses
the Total Survey Error framework to examine optimal approaches to
survey interviewing, presenting state-of-the-art methodological
research on all stages of the survey process involving
interviewers. Acknowledging the important history of survey
interviewing while looking to the future, this one-of-a-kind
reference provides researchers and practitioners with a roadmap for
maximizing data quality in interviewer-administered surveys.
Interviewer Effects from a Total Survey Error Perspective presents
a comprehensive collection of state-of-the-art research on
interviewer-administered survey data collection. Interviewers play
an essential role in the collection of the high-quality survey data
used to learn about our society and improve the human condition.
Although many surveys are conducted using self-administered modes,
interviewer-administered modes continue to be optimal for surveys
that require high levels of participation, include
difficult-to-survey populations, and collect biophysical data.
Survey interviewing is complex, multifaceted, and challenging.
Interviewers are responsible for locating sampled units, contacting
sampled individuals and convincing them to cooperate, asking
questions on a variety of topics, collecting other kinds of data,
and providing data about respondents and the interview environment.
Careful attention to the methodology that underlies survey
interviewing is essential for interviewer-administered data
collections to succeed. In 2019, survey methodologists, survey
practitioners, and survey operations specialists participated in an
international workshop at the University of Nebraska-Lincoln to
identify best practices for surveys employing interviewers and
outline an agenda for future methodological research. This book
features 23 chapters on survey interviewing by these worldwide
leaders in the theory and practice of survey interviewing. Chapters
include: The legacy of Dr. Charles F. Cannell's groundbreaking
research on training survey interviewers and the theory of survey
interviewing Best practices for training survey interviewers
Interviewer management and monitoring during data collection The
complex effects of interviewers on survey nonresponse Collecting
survey measures and survey paradata in different modes Designing
studies to estimate and evaluate interviewer effects Best practices
for analyzing interviewer effects Key gaps in the research
literature, including an agenda for future methodological research
Chapter appendices available to download from
https://digitalcommons.unl.edu/sociw/ Written for managers of
survey interviewers, survey methodologists, and students interested
in the survey data collection process, this unique reference uses
the Total Survey Error framework to examine optimal approaches to
survey interviewing, presenting state-of-the-art methodological
research on all stages of the survey process involving
interviewers. Acknowledging the important history of survey
interviewing while looking to the future, this one-of-a-kind
reference provides researchers and practitioners with a roadmap for
maximizing data quality in interviewer-administered surveys.
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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