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.
Auflage
Sprache
Verlagsort
Verlagsgruppe
Zielgruppe
Für höhere Schule und Studium
Für Beruf und Forschung
Editions-Typ
Illustrationen
Maße
Höhe: 239 mm
Breite: 193 mm
Dicke: 43 mm
Gewicht
ISBN-13
978-0-03-072831-0 (9780030728310)
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Schweitzer Klassifikation
Part I: Foundations of Multiple Regression Analysis. Overview. Simple Linear Regression and Correlation. Regression Diagnostics. Computers and Computer Programs. Elements of Multiple Regression Analysis: Two Independent Variables. General Method of Multiple Regression Analysis: Matrix Operations. Statistical Control: Partial and Semi-Partial Correlation. Prediction. Part II: Multiple Regression Analysis. Variance Partitioning. Analysis of Effects. A Categorical Independent Variable: Dummy, Effect, And Orthogonal Coding. Multiple Categorical Independent Variables and Factorial Designs. Curvilinear Regression Analysis. Continuous and Categorical Independent Variables I: Attribute-Treatment Interaction, Comparing Regression Equations. Continuous and Categorical Independent Variables II: Analysis of Covariance. Elements of Multilevel Analysis. Categorical Dependent Variable: Logistic Regression. Part III: Structural Equation Models. Structural Equation Models with Observed Variables: Path Analysis. Structural Equation Models with Latent Variables. Part IV: Multivariate Analysis. Regression, Discriminant, And Multivariate Analysis of Variance: Two Groups. Canonical, Discriminant, And Multivariate Analysis of Variance: Extensions. Appendices.