Secure CheckoutPersonal information is secured with SSL technology.
Free ShippingFree global shipping
No minimum order.
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.
Scientific researchers, academics, and research professionals in applied settings in the biological and social sciences
Introduction. H.E.A. Tinsley and S.D. Brown, Multivariate Statistics and Mathematical Modeling. J. Hetherington, Role of Theory and Experimental Design in Multivariate Analysis and Mathematical Modeling. R.V. Dawis, Scale Construction and Psychometric Considerations. H.E.A. Tinsley and D.J. Weiss, Interrater Reliability and Agreement. M. Hallahan and R. Rosenthal, Interpreting and Reporting Results. A. Venter and S.E. Maxwell, Issues in the Use and Application of Multiple Regression Analysis. C.J. Huberty and M.D. Petoskey, Multivariate Analysis of Variance and Covariance. M.T. Brown and L.R. Wicker, Discriminant Analysis. R.M. Thorndike, Canonical Correlation Analysis. R. Cudeck, Exploratory Factor Analysis. P.A. Gore, Jr., Cluster Analysis. M.L. Davison and S.G. Sireci, Multidimensional Scaling. M.M. Mark, C.S. Reichardt, and L.J. Sanna, Time-Series Designs and Analyses. P.B. Imrey, Poisson Regression, Logistic Regression, and Loglinear Models for Random Counts. L.F. Dilalla, Structural Equation Modeling: Uses and Issues. R.H. Hoyle, Confirmatory Factor Analysis. B.J. Becker, Multivariate Meta-analysis. G.A. Marcoulides, Generalizability Theory. R.K. Hambelton, F. Robin, and D. Xing, Item Response Models for the Analysis of Educational and Psychological Test Data. L. Dumenci, Multitrait-Multimethod Analysis. I.G.G. Kreft, Using Random Coefficient Linear Models for the Analysis of Hierarchically Nested Data. T.J.G. Tracey, Analysis of Circumplex Models. J.B. Willett and M.K. Keiley, Using Covariance Structure Analysis to Model Change over Time. Author Index. Subject Index.
- No. of pages:
- © Academic Press 2000
- 26th April 2000
- Academic Press
- Hardcover ISBN:
- eBook ISBN:
University of Florida, Gainesville, U.S.A.
Loyola University of Chicago, Wilmette, Illinois, U.S.A.
Elsevier.com visitor survey
We are always looking for ways to improve customer experience on Elsevier.com.
We would like to ask you for a moment of your time to fill in a short questionnaire, at the end of your visit.
If you decide to participate, a new browser tab will open so you can complete the survey after you have completed your visit to this website.
Thanks in advance for your time.