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This comprehensive text introduces regression, the general linear
model, structural equation modeling, the hierarchical linear model,
growth curve models, panel data, and event history models, and
includes discussion of published implementations of each technique
showing how it was used to address substantive and interesting
research questions. It takes a step-by-step approach in the
presentation of each topic, using mathematical derivations where
necessary, but primarily emphasizing how the methods involved can
be implemented, are used in addressing representative substantive
problems than span a number of disciplines, and can be interpreted
in words. The book demonstrates the analyses in STATA and SAS.
Generalizing the Regression Model provides students with a bridge
from the classroom to actual research practice and application.
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