Handbook of applied multivariate statistics and mathematical modeling 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 Multivariate statistics and mathematical modeling Howard E.A. Tinsley and Steven D. Brown Role of theory and experimental design in multivariate analysis and mathematical modeling John Hetherington Scale construction and psychometric considerations Rene V. Dawis Interrater reliability and agreement Howard E.A. Tinsley and David J. Weiss Interpreting and reporting results Mark Hallahan and Robert Rosenthal Issues in the use and application of multiple regression analysis Anre Venter and Scott E. Maxwell Multivariate analysis of variance and covariance Carl J Huberty and Martha D. Petoskey Discriminant analysis Michael T. Brown and Lori R. Wicker Canonical correlation analysis Robert M. Thorndike Exploratory factor analysis Robert Cudeck Cluster analysis Paul A. Gore Jr. Multidimensional scaling Mark L. Davison and Stephen G. Sireci Time series designs and analyses Melvin M. Mark Charles S. Reichardt and Lawrence J. Sanna Poisson regression logistic regression and loglinear models for random counts Peter B. Imrey Structural equation modeling : uses and issues Lisabeth F. Dilalla Confirmatory factor analysis Rick H. Hoyle Multivariate meta analysis Betsy J. Becker Generalizability theory George A. Marcoulides Item response models for the analysis of educational and psychological test data Ronald K. Hambleton Frederic Robin and Dehui Xing Multitrait multimethod analysis Levent Dumenci Using random coefficient linear models for the analysis of hierarchically nested data Ita G.G. Kreft Analysis of circumplex models Terence J.G. Tracey Using covariance structure analysis to model change over time John B. Willett and Margaret K. Keiley 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