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Books > Medicine > General issues > Public health & preventive medicine > Epidemiology & medical statistics
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Multivariate Applications in Substance Use Research - New Methods for New Questions (Paperback)
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Multivariate Applications in Substance Use Research - New Methods for New Questions (Paperback)
Series: Multivariate Applications Series
Expected to ship within 12 - 17 working days
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This edited volume introduces the latest advances in quantitative
methods and illustrates ways to apply these methods to important
questions in substance use research. The goal is to provide a forum
for dialogue between methodologists developing innovative
multivariate statistical methods and substance use researchers who
have produced rich data sets. Reflecting current research trends,
the book examines the use of longitudinal techniques to measure
processes of change over time. Researchers faced with the task of
studying the causes, course, treatment, and prevention of substance
use and abuse will find this volume helpful for applying these
techniques to make optimal use of their data. This innovative
volume: introduces the use of latent curve methods for describing
individual trajectories of adolescent substance use over time;
explores methods for analyzing longitudinal data for individuals
nested within groups, such as families, classrooms, and treatment
groups; demonstrates how different patterns of missing data
influence the interpretation of results; reports on some recent
advances in longitudinal growth modeling; illustrates methods to
assess mediation when there are multiple mediating pathways
underlying an intervention effect; describes methods to identify
moderating relations in structural equation models; demonstrates
the use of structural equation models to evaluate a preventive
intervention; applies epidemic modeling techniques to understand
the spread of substance use in society; illustrates the use of
latent transition analysis to model substance use as a series of
stages; and applies logistic regression to prospectively predict
smoking cessation.
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