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Power Analysis of Trials with Multilevel Data covers using power
and sample size calculations to design trials that involve nested
data structures. The book gives a thorough overview of power
analysis that details terminology and notation, outlines key
concepts of statistical power and power analysis, and explains why
they are necessary in trial design. It guides you in performing
power calculations with hierarchical data, which enables more
effective trial design. The authors are leading experts in the
field who recognize that power analysis has attracted attention
from applied statisticians in social, behavioral, medical, and
health science. Their book supplies formulae that allow
statisticians and researchers in these fields to perform
calculations that enable them to plan cost-efficient trials. The
formulae can also be applied to other sciences. Using power
analysis in trial design is increasingly important in a scientific
community where experimentation is often expensive, competition for
funding among researchers is intense, and agencies that finance
research require proposals to give thorough justification for
funding. This handbook shows how power analysis shapes trial
designs that have high statistical power and low cost, using
real-life examples. The book covers multiple types of trials,
including cluster randomized trials, multisite trials, individually
randomized group treatment trials, and longitudinal intervention
studies. It also offers insight on choosing which trial is best
suited to a given project. Power Analysis of Trials with Multilevel
Data helps you craft an optimal research design and anticipate the
necessary sample size of data to collect to give your research
maximum effectiveness and efficiency.
Applauded for its clarity, this accessible introduction helps
readers apply multilevel techniques to their research. The book
also includes advanced extensions, making it useful as both an
introduction for students and as a reference for researchers. Basic
models and examples are discussed in nontechnical terms with an
emphasis on understanding the methodological and statistical issues
involved in using these models. The estimation and interpretation
of multilevel models is demonstrated using realistic examples from
various disciplines including psychology, education, public health,
and sociology. Readers are introduced to a general framework on
multilevel modeling which covers both observed and latent variables
in the same model, while most other books focus on observed
variables. In addition, Bayesian estimation is introduced and
applied using accessible software.
Power Analysis of Trials with Multilevel Data covers using power
and sample size calculations to design trials that involve nested
data structures. The book gives a thorough overview of power
analysis that details terminology and notation, outlines key
concepts of statistical power and power analysis, and explains why
they are necessary in trial design. It guides you in performing
power calculations with hierarchical data, which enables more
effective trial design. The authors are leading experts in the
field who recognize that power analysis has attracted attention
from applied statisticians in social, behavioral, medical, and
health science. Their book supplies formulae that allow
statisticians and researchers in these fields to perform
calculations that enable them to plan cost-efficient trials. The
formulae can also be applied to other sciences. Using power
analysis in trial design is increasingly important in a scientific
community where experimentation is often expensive, competition for
funding among researchers is intense, and agencies that finance
research require proposals to give thorough justification for
funding. This handbook shows how power analysis shapes trial
designs that have high statistical power and low cost, using
real-life examples. The book covers multiple types of trials,
including cluster randomized trials, multisite trials, individually
randomized group treatment trials, and longitudinal intervention
studies. It also offers insight on choosing which trial is best
suited to a given project. Power Analysis of Trials with Multilevel
Data helps you craft an optimal research design and anticipate the
necessary sample size of data to collect to give your research
maximum effectiveness and efficiency.
Applauded for its clarity, this accessible introduction helps
readers apply multilevel techniques to their research. The book
also includes advanced extensions, making it useful as both an
introduction for students and as a reference for researchers. Basic
models and examples are discussed in nontechnical terms with an
emphasis on understanding the methodological and statistical issues
involved in using these models. The estimation and interpretation
of multilevel models is demonstrated using realistic examples from
various disciplines including psychology, education, public health,
and sociology. Readers are introduced to a general framework on
multilevel modeling which covers both observed and latent variables
in the same model, while most other books focus on observed
variables. In addition, Bayesian estimation is introduced and
applied using accessible software.
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