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Multiple regression in behavioral research
by:Elazar J. Pedhazur
Multiple Regression in Behavioral Research adopts a data-analysis approach to multiple regression. Pedhazur integrates design and analysis, and emphasizes learning by example and critiques of published research. Features: * Pedhazur places strong emphasis on the application of regression analysis to various research problems. He discusses major research...
Multiple Regression in Behavioral Research adopts a data-analysis approach to multiple regression. Pedhazur integrates design and analysis, and emphasizes learning by example and critiques of published research. Features: * Pedhazur places strong emphasis on the application of regression analysis to various research problems. He discusses major research studies and journal articles in conjunction with specific methods presented. This emphasis gives students a critical appreciation of how methods are used and abused in various research areas. * Chapters 11-15 discuss various coding methods for categorical variables. This detailed explanation teaches students the properties of different coding methods and their suitability for specific tasks. * Multiple Regression introduces path analysis and structural equation models, including indices of fit and decomposition of effects. This information, in Part 3, exposes students to recent developments in the field. New to this edition: * Pedhazur demonstrates applications of all analytic approaches with detailed analyses of simple numerical examples. He uses the most recent versions of BMDP, MINITAB, SAS, and SPSS, provides examples of the computer outputs, and comments on them in detail. * The latest versions of EQS and LISREL programs for the structural equation modeling are also introduced and used for analyzing causal models with single indicators (path analysis) and multiple indicators or unobserved variables. * New chapters include Chapter 3, Regression Diagnostics, Chapter 16, Elements of Multilevel Analysis and Chapter 17, Categorical Dependent Variable: Logistic Regression.
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