Applied Multiple Regression/Correlation Analysis for the Behavioral Sciences

Name: Applied Multiple Regression/Correlation Analysis for the Behavioral Sciences
Availability: InStock
ISBN: 9780805822236

ISBN-10: 0805822232

ISBN-13: 9780805822236

Edition: 3rd 2003 (Revised)

Authors: Jacob Cohen, Patricia Cohen, Stephen G. West, Leona S. Aiken

List price: $140.00

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Description:

This classic text on multiple regression is noted for its non-mathematical, applied, and data-analytic approach. Readers profit from its verbal-conceptual exposition and frequent use of examples. The applied emphasis provides clear illustrations of the principles and provides worked examples of the types of applications that are possible. Researchers learn how to specify regression models that directly address their research questions. An overview of the fundamental ideas of multiple regression and a review of bivariate correlation and regression and other elementary statistical concepts provide a strong foundation for a solid understanding of the rest of the text. The third edition…

Book details

List price: $140.00
Edition: 3rd
Copyright year: 2003
Publisher: Routledge
Publication date: 8/1/2002
Binding: Hardcover
Pages: 736
Size: 7.28" wide x 10.08" long x 2.01" tall
Weight: 3.542
Language: English

Stephen G. West (PhD, University of Texas at Austin) is Professor of Quantitative and Social Psychology at the University of Arizona. His current quantitative research interests include field research methods, structural equation modeling, multiple regression analysis, mediational analysis, graphics and exploratory data analysis, and longitudinal data analysis. Current social psychology research interests include personality research, applied social, prevention-related issues in health, mental health. He is the editor ofnbsp; Psychological Methods, published by APA.

Leona S. Aiken (PhD, Purdue University)nbsp;is Professor and Chair of Social and Quantitative Psychology at Arizona State University. Her research interests include both quantitative methods and health psychology. In quantitative methods, she is known for her work in continuous variable interactions in multiple regression.nbsp;She is also interested in the use of design approaches and mediational analysis to untangle the effects of individual components in multi-component interventions. In health psychology, she is interested in adoption of health protective behaviors across the life span, particularly among women, both from the perspectives of psychosocial models of the putative…



Preface



Introduction



Multiple Regression/Correlation as a General Data-Analytic System



A Comparison of Multiple Regression/Correlation and Analysis of Variance Approaches



Multiple Regression/Correlation and the Complexity of Behavioral Science



Orientation of the Book



Computation, the Computer, and Numerical Results



The Spectrum of Behavioral Science



Plan for the Book



Summary



Bivariate Correlation and Regression



Tabular and Graphic Representations of Relationships



The Index of Linear Correlation Between Two Variables: The Pearson Product Moment Correlation Coefficient



Alternative Formulas for the Product Moment Correlation Coefficient



Regression Coefficients: Estimating Y From X



Regression Toward the Mean



The Standard Error of Estimate and Measures of the Strength of Association



Summary of Definitions and Interpretations



Statistical Inference With Regression and Correlation Coefficients



Precision and Power



Factors Affecting the Size of r



Summary



Multiple Regression/Correlation With Two or More Independent Variables



Introduction: Regression and Causal Models



Regression With Two Independent Variables



Measures of Association With Two Independent Variables



Patterns of Association Between Y and Two Independent Variables



Multiple Regression/Correlation With k Independent Variables



Statistical Inference With k Independent Variables



Statistical Precision and Power Analysis



Using Multiple Regression Equations in Prediction



Summary



Data Visualization, Exploration, and Assumption Checking: Diagnosing and Solving Regression Problems I



Introduction



Some Useful Graphical Displays of the Original Data



Assumptions and Ordinary Least Squares Regression



Detecting Violations of Assumptions



Remedies: Alternative Approaches When Problems Are Detected



Summary



Data-Analytic Strategies Using Multiple Regression/Correlation



Research Questions Answered by Correlations and Their Squares



Research Questions Answered by B Or [beta]



Hierarchical Analysis Variables in Multiple Regression/Correlation



The Analysis of Sets of Independent Variables



Significance Testing for Sets



Power Analysis for Sets



Statistical Inference Strategy in Multiple Regression/Correlation



Summary



Quantitative Scales, Curvilinear Relationships, and Transformations



Introduction



Power Polynomials



Orthogonal Polynomials



Nonlinear Transformations



Nonlinear Regression



Nonparametric Regression



Summary



Interactions Among Continuous Variables



Introduction



Centering Predictors and the Interpretation of Regression Coefficients in Equations Containing Interactions



Simple Regression Equations and Simple Slopes



Post Hoc Probing of Interactions



Standardized Estimates for Equations Containing Interactions



Interactions as Partialed Effects: Building Regression Equations With Interactions



Patterns of First-Order and Interactive Effects



Three-Predictor Interactions in Multiple Regression



Curvilinear by Linear Interactions



Interactions Among Sets of Variables



Issues in the Detection of Interactions: Reliability, Predictor Distributions, Model Specification



Summary



Categorical or Nominal Independent Variables



Introduction



Dummy-Variable Coding



Unweighted Effects Coding



Weighted Effects Coding



Contrast Coding



Nonsense Coding



Coding Schemes in the Context of Other Independent Variables



Summary



Interactions With Categorical Variables



Nominal Scale by Nominal Scale Interactions



Interactions Involving More Than Two Nominal Scales



Nominal Scale by Continuous Variable Interactions



Summary



Outliers and Multicollinearity: Diagnosing and Solving Regression Problems II



Introduction



Outliers: Introduction and Illustration



Detecting Outliers: Regression Diagnostics



Sources of Outliers and Possible Remedial Actions



Multicollinearity



Remedies for Multicollinearity



Summary



Missing Data



Basic Issues in Handling Missing Data



Missing Data in Nominal Scales



Missing Data in Quantitative Scales



Summary



Multiple Regression/Correlation and Causal Models



Introduction



Models Without Reciprocal Causation



Models With Reciprocal Causation



Identification and Overidentification



Latent Variable Models



A Review of Causal Model and Statistical Assumptions



Comparisons of Causal Models



Summary



Alternative Regression Models: Logistic, Poisson Regression, and the Generalized Linear Model



Ordinary Least Squares Regression Revisited



Dichotomous Outcomes and Logistic Regression



Extensions of Logistic Regression to Multiple Response Categories: Polytomous Logistic Regression and Ordinal Logistic Regression



Models for Count Data: Poisson Regression and Alternatives



Full Circle: Parallels Between Logistic and Poisson Regression, and the Generalized Linear Model



Summary



Random Coefficient Regression and Multilevel Models



Clustering Within Data Sets



Analysis of Clustered Data With Ordinary Least Squares Approaches



The Random Coefficient Regression Model



Random Coefficient Regression Model and Multilevel Data Structure



Numerical Example: Analysis of Clustered Data With Random Coefficient Regression



Clustering as a Meaningful Aspect of the Data



Multilevel Modeling With a Predictor at Level 2



An Experimental Design as a Multilevel Data Structure: Combining Experimental Manipulation With Individual Differences



Numerical Example: Multilevel Analysis



Estimation of the Multilevel Model Parameters: Fixed Effects, Variance Components, and Level 1 Equations



Statistical Tests in Multilevel Models



Some Model Specification Issues



Statistical Power of Multilevel Models



Choosing Between the Fixed Effects Model and the Random Coefficient Model



Sources on Multilevel Modeling



Multilevel Models Applied to Repeated Measures Data



Summary



Longitudinal Regression Methods



Introduction



Analyses of Two-Time-Point Data



Repeated Measure Analysis of Variance



Multilevel Regression of Individual Changes Over Time



Latent Growth Models: Structural Equation Model Representation of Multilevel Data



Time Varying Independent Variables



Survival Analysis



Time Series Analysis



Dynamic System Analysis



Statistical Inference and Power Analysis in Longitudinal Analyses



Summary



Multiple Dependent Variables: Set Correlation



Introduction to Ordinary Least Squares Treatment of Multiple Dependent Variables



Measures of Multivariate Association



Partialing in Set Correlation



Tests of Statistical Significance and Statistical Power



Statistical Power Analysis in Set Correlation



Comparison of Set Correlation With Multiple Analysis of Variance



New Analytic Possibilities With Set Correlation



Illustrative Examples



Summary


Appendices



The Mathematical Basis for Multiple Regression/Correlation and Identification of the Inverse Matrix Elements



Determination of the Inverse Matrix and Applications Thereof


Appendix Tables


References


Glossary


Statistical Symbols and Abbreviations


Author Index


Subject Index