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Applied Multiple Regression : Correlation Analysis for the Behavioral Sciences[¾çÀå]

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This classic text on multiple regression is noted for its nonmathematical, 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 understanding the rest of the text. The third edition features an increased emphasis on graphics and the use of confidence intervals and effect size measures, and an accompanying website with data for most of the numerical examples along with the computer code for SPSS, SAS, and SYSTAT, at www.psypress.com/9780805822236 .

Applied Multiple Regression serves as both a textbook for graduate students and as a reference tool for researchers in psychology, education, health sciences, communications, business, sociology, political science, anthropology, and economics. An introductory knowledge of statistics is required. Self-standing chapters minimize the need for researchers to refer to previous chapters.

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Prefacep. xxv
Introductionp. 1
Multiple Regression/Correlation as a General Data-Analytic Systemp. 1
A Comparison of Multiple Regression/Correlation and Analysis of Variance Approachesp. 4
Multiple Regression/Correlation and the Complexity of Behavioral Sciencep. 6
Orientation of the Bookp. 10
Computation, the Computer, and Numerical Resultsp. 14
The Spectrum of Behavioral Sciencep. 16
Plan for the Bookp. 16
Summaryp. 18
Bivariate Correlation and Regressionp. 19
Tabular and Graphic Representations of Relationshipsp. 19
The Index of Linear Correlation Between Two Variables: The Pearson Product Moment Correlation Coefficientp. 23
Alternative Formulas for the Product Moment Correlation Coefficientp. 28
Regression Coefficients: Estimating Y From Xp. 32
Regression Toward the Meanp. 36
The Standard Error of Estimate and Measures of the Strength of Associationp. 37
Summary of Definitions and Interpretationsp. 41
Statistical Inference With Regression and Correlation Coefficientsp. 41
Precision and Powerp. 50
Factors Affecting the Size of rp. 53
Summaryp. 62
Multiple Regression/Correlation With Two or More Independent Variablesp. 64
Introduction: Regression and Causal Modelsp. 64
Regression With Two Independent Variablesp. 66
Measures of Association With Two Independent Variablesp. 69
Patterns of Association Between Y and Two Independent Variablesp. 75
Multiple Regression/Correlation With k Independent Variablesp. 79
Statistical Inference With k Independent Variablesp. 86
Statistical Precision and Power Analysisp. 90
Using Multiple Regression Equations in Predictionp. 95
Summaryp. 99
Data Visualization, Exploration, and Assumption Checking: Diagnosing and Solving Regression Problems Ip. 101
Introductionp. 101
Some Useful Graphical Displays of the Original Datap. 102
Assumptions and Ordinary Least Squares Regressionp. 117
Detecting Violations of Assumptionsp. 125
Remedies: Alternative Approaches When Problems Are Detectedp. 141
Summaryp. 150
Data-Analytic Strategies Using Multiple Regression/Correlationp. 151
Research Questions Answered by Correlations and Their Squaresp. 151
Research Questions Answered by B Or [beta]p. 154
Hierarchical Analysis Variables in Multiple Regression/Correlationp. 158
The Analysis of Sets of Independent Variablesp. 162
Significance Testing for Setsp. 171
Power Analysis for Setsp. 176
Statistical Inference Strategy in Multiple Regression/Correlationp. 182
Summaryp. 190
Quantitative Scales, Curvilinear Relationships, and Transformationsp. 193
Introductionp. 193
Power Polynomialsp. 196
Orthogonal Polynomialsp. 214
Nonlinear Transformationsp. 221
Nonlinear Regressionp. 251
Nonparametric Regressionp. 252
Summaryp. 253
Interactions Among Continuous Variablesp. 255
Introductionp. 255
Centering Predictors and the Interpretation of Regression Coefficients in Equations Containing Interactionsp. 261
Simple Regression Equations and Simple Slopesp. 267
Post Hoc Probing of Interactionsp. 272
Standardized Estimates for Equations Containing Interactionsp. 282
Interactions as Partialed Effects: Building Regression Equations With Interactionsp. 284
Patterns of First-Order and Interactive Effectsp. 285
Three-Predictor Interactions in Multiple Regressionp. 290
Curvilinear by Linear Interactionsp. 292
Interactions Among Sets of Variablesp. 295
Issues in the Detection of Interactions: Reliability, Predictor Distributions, Model Specificationp. 297
Summaryp. 300
Categorical or Nominal Independent Variablesp. 302
Introductionp. 302
Dummy-Variable Codingp. 303
Unweighted Effects Codingp. 320
Weighted Effects Codingp. 328
Contrast Codingp. 332
Nonsense Codingp. 341
Coding Schemes in the Context of Other Independent Variablesp. 342
Summaryp. 351
Interactions With Categorical Variablesp. 354
Nominal Scale by Nominal Scale Interactionsp. 354
Interactions Involving More Than Two Nominal Scalesp. 366
Nominal Scale by Continuous Variable Interactionsp. 375
Summaryp. 388
Outliers and Multicollinearity: Diagnosing and Solving Regression Problems IIp. 390
Introductionp. 390
Outliers: Introduction and Illustrationp. 391
Detecting Outliers: Regression Diagnosticsp. 394
Sources of Outliers and Possible Remedial Actionsp. 411
Multicollinearityp. 419
Remedies for Multicollinearityp. 425
Summaryp. 430
Missing Datap. 431
Basic Issues in Handling Missing Datap. 431
Missing Data in Nominal Scalesp. 435
Missing Data in Quantitative Scalesp. 442
Summaryp. 450
Multiple Regression/Correlation and Causal Modelsp. 452
Introductionp. 452
Models Without Reciprocal Causationp. 460
Models With Reciprocal Causationp. 467
Identification and Overidentificationp. 468
Latent Variable Modelsp. 469
A Review of Causal Model and Statistical Assumptionsp. 475
Comparisons of Causal Modelsp. 476
Summaryp. 477
Alternative Regression Models: Logistic, Poisson Regression, and the Generalized Linear Modelp. 479
Ordinary Least Squares Regression Revisitedp. 479
Dichotomous Outcomes and Logistic Regressionp. 482
Extensions of Logistic Regression to Multiple Response Categories: Polytomous Logistic Regression and Ordinal Logistic Regressionp. 519
Models for Count Data: Poisson Regression and Alternativesp. 525
Full Circle: Parallels Between Logistic and Poisson Regression, and the Generalized Linear Modelp. 532
Summaryp. 535
Random Coefficient Regression and Multilevel Modelsp. 536
Clustering Within Data Setsp. 536
Analysis of Clustered Data With Ordinary Least Squares Approachesp. 539
The Random Coefficient Regression Modelp. 543
Random Coefficient Regression Model and Multilevel Data Structurep. 544
Numerical Example: Analysis of Clustered Data With Random Coefficient Regressionp. 550
Clustering as a Meaningful Aspect of the Datap. 553
Multilevel Modeling With a Predictor at Level 2p. 553
An Experimental Design as a Multilevel Data Structure: Combining Experimental Manipulation With Individual Differencesp. 555
Numerical Example: Multilevel Analysisp. 556
Estimation of the Multilevel Model Parameters: Fixed Effects, Variance Components, and Level 1 Equationsp. 560
Statistical Tests in Multilevel Modelsp. 563
Some Model Specification Issuesp. 564
Statistical Power of Multilevel Modelsp. 565
Choosing Between the Fixed Effects Model and the Random Coefficient Modelp. 565
Sources on Multilevel Modelingp. 566
Multilevel Models Applied to Repeated Measures Datap. 566
Summaryp. 567
Longitudinal Regression Methodsp. 568
Introductionp. 568
Analyses of Two-Time-Point Datap. 569
Repeated Measure Analysis of Variancep. 573
Multilevel Regression of Individual Changes Over Timep. 578
Latent Growth Models: Structural Equation Model Representation of Multilevel Datap. 588
Time Varying Independent Variablesp. 595
Survival Analysisp. 596
Time Series Analysisp. 600
Dynamic System Analysisp. 602
Statistical Inference and Power Analysis in Longitudinal Analysesp. 604
Summaryp. 605
Multiple Dependent Variables: Set Correlationp. 608
Introduction to Ordinary Least Squares Treatment of Multiple Dependent Variablesp. 608
Measures of Multivariate Associationp. 610
Partialing in Set Correlationp. 613
Tests of Statistical Significance and Statistical Powerp. 615
Statistical Power Analysis in Set Correlationp. 617
Comparison of Set Correlation With Multiple Analysis of Variancep. 619
New Analytic Possibilities With Set Correlationp. 620
Illustrative Examplesp. 621
Summaryp. 627
Appendices
The Mathematical Basis for Multiple Regression/Correlation and Identification of the Inverse Matrix Elementsp. 631
Determination of the Inverse Matrix and Applications Thereofp. 636
Appendix Tablesp. 643
Referencesp. 655
Glossaryp. 671
Statistical Symbols and Abbreviationsp. 683
Author Indexp. 687
Subject Indexp. 691
Table of Contents provided by Rittenhouse. All Rights Reserved.

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Noted for its non-mathematical, applied, and data-analytic approach, this classic text on multiple regression provides clear illustrations of the principles and provides worked examples of the types of applications that are possible. The CD contains data for most of the numerical examples.

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