Multivariate statistics and mathematical models provide flexible and powerful tools essential in most disciplines. Nevertheless, many practicing researchers lack an adequate knowledge of these techniques, or did once know the techniques, but have not been able to keep abreast of new developments. The Handbook of Applied Multivariate Statistics and Mathematical Modeling explains the appropriate uses of multivariate procedures and mathematical modeling techniques, and prescribe practices that enable applied researchers to use these procedures effectively without needing to concern themselves with the mathematical basis. The Handbook emphasizes using models and statistics as tools. The objective of the book is to inform readers about which tool to use to accomplish which task. Each chapter begins with a discussion of what kinds of questions a particular technique can and cannot answer. As multivariate statistics and modeling techniques are useful across disciplines, these examples include issues of concern in biological and social sciences as well as the humanities.
This book discusses as well the eigenstructures and quadratic forms. The final chapter deals with the geometric aspects of linear transformations. This book is a valuable resource for students.
This book is aimed at students who require a course on applied multivariate statistics unified by the concept of conditional independence and researchers concerned with applying graphical modelling techniques.
This book brings the power of multivariate statistics to graduate-level practitioners, making these analytical methods accessible without lengthy mathematical derivations.
This book is distinguished by its use of latent variable modeling in later chapters to address multivariate analysis questions that are of special interest to behavioral and social science students.
... (6) part and partial correlations, (7) collinearity diagnostics, (8) Durbin-Watson, and (9) Casewise diagnostics. For this example, we apply an alpha level of .05, thus we will leave the default confidence interval percentage at 95.
This book brings the power of multivariate statistics to graduate-level practitioners, making these analytical methods accessible without lengthy mathematical derivations.
Becker, J.B. Multivariate meta-analysis. In Handbook of Applied Multivariate Statistics and Mathematical Modelling; Tinsley, H.E.A.; Brown, S., Eds.; Academic Press: San Diego, CA, 2000. Hedges, L.V.; Olkin, I. Statistical Methods for ...
... J., 101 Wall, T.D., 163 Wampold,B.E., 123 Warr,P.B., 163 Wassermann, W., 122 Watkins, K.E., 194, 327–348, 352, 376, 377, 378 Watson, C.J., 131 Weaver, A., 249 Wegener, D.T., 182 Weick, K.E., 223, 225, 226, 305, 353,354,362,363,370, ...
... W R (1978) Discrete Discriminant Analysis, Wiley, New York Gore, P A (2000) Cluster Analysis in (eds) H E A Tinsley and S D Brown, Handbook of Applied Multivariate Statistics and Mathematical Modelling, pp 297–321, Academic Press, ...
In the next chapter , we will discuss how variables are put together in constructing a causal diagram . ... Research Design and Methods : A Process Approach . ... Contemporary Social Research Methods : A Text Using Microcase .