Drawing upon more than 30 years of experience in working with
statistics, Dr. Richard J. Harris has updated A Primer of
Multivariate Statistics to provide a model of balance between
how-to and why. This classic text covers multivariate techniques
with a taste of latent variable approaches. Throughout the book
there is a focus on the importance of describing and testing one's
interpretations of the emergent variables that are produced by
multivariate analysis. This edition retains its conversational
writing style while focusing on classical techniques. The book
gives the reader a feel for why one should consider diving into
more detailed treatments of computer-modeling and latent-variable
techniques, such as non-recursive path analysis, confirmatory
factor analysis, and hierarchical linear modeling. Throughout the
book there is a focus on the importance of describing and testing
one's interpretations of the emergent variables that are produced
by multivariate analysis.
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