Multivariate Data Analysis 8th Edition by Joseph F Hair
By: Joseph Hair
Publisher: Cengage/Nelson
Print ISBN: 9781473756540, 1473756545
eText ISBN: 9780176882990, 0176882995
Edition: 8th
Copyright year: 2018
in more than four decades since the first edition of Multivariate Data Analysis, the fields of multivariate statistics,
and analytics in general, have evolved dramatically in several different directions for both academic and applied
researchers. In the methodological domain, we have seen a continued evolution of the more ?traditional? statistical
methods such as multiple regression, ANOVA/MANOVA and exploratory factor analysis. These methods have
been extended not only in their capabilities (e.g., additional options for variable selection, measures of variable
importance, etc.), but also in their application (e.g., multi-level models and causal inference techniques). These
?traditional? methods have been augmented by a new generation of techniques represented by structural equation
modeling and partial least squares. These methods integrate many of the former methods (e.g., multiple regression
and exploratory factor analysis) into new analytical techniques to perform confirmatory factor analysis and
structural model estimation. But perhaps most exciting has been the integration of methods from the fields of data
mining, machine learning and neural networks. These fields of study have remained separate for too long, representing
different ?cultures? in terms of approaches to data analysis. But as we discuss in Chapter 1 and throughout
the text, these two fields provide complementary approaches, each of which has advantages and disadvantages. We
hope that by acknowledging these complementarities we can in some small way increase the rate of integration
between the two fields.
