Multiple Regression in Behavioral Research: Explanation and Prediction

Abstract

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.

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