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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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Preface | p. xxv |
Introduction | p. 1 |
Multiple Regression/Correlation as a General Data-Analytic System | p. 1 |
A Comparison of Multiple Regression/Correlation and Analysis of Variance Approaches | p. 4 |
Multiple Regression/Correlation and the Complexity of Behavioral Science | p. 6 |
Orientation of the Book | p. 10 |
Computation, the Computer, and Numerical Results | p. 14 |
The Spectrum of Behavioral Science | p. 16 |
Plan for the Book | p. 16 |
Summary | p. 18 |
Bivariate Correlation and Regression | p. 19 |
Tabular and Graphic Representations of Relationships | p. 19 |
The Index of Linear Correlation Between Two Variables: The Pearson Product Moment Correlation Coefficient | p. 23 |
Alternative Formulas for the Product Moment Correlation Coefficient | p. 28 |
Regression Coefficients: Estimating Y From X | p. 32 |
Regression Toward the Mean | p. 36 |
The Standard Error of Estimate and Measures of the Strength of Association | p. 37 |
Summary of Definitions and Interpretations | p. 41 |
Statistical Inference With Regression and Correlation Coefficients | p. 41 |
Precision and Power | p. 50 |
Factors Affecting the Size of r | p. 53 |
Summary | p. 62 |
Multiple Regression/Correlation With Two or More Independent Variables | p. 64 |
Introduction: Regression and Causal Models | p. 64 |
Regression With Two Independent Variables | p. 66 |
Measures of Association With Two Independent Variables | p. 69 |
Patterns of Association Between Y and Two Independent Variables | p. 75 |
Multiple Regression/Correlation With k Independent Variables | p. 79 |
Statistical Inference With k Independent Variables | p. 86 |
Statistical Precision and Power Analysis | p. 90 |
Using Multiple Regression Equations in Prediction | p. 95 |
Summary | p. 99 |
Data Visualization, Exploration, and Assumption Checking: Diagnosing and Solving Regression Problems I | p. 101 |
Introduction | p. 101 |
Some Useful Graphical Displays of the Original Data | p. 102 |
Assumptions and Ordinary Least Squares Regression | p. 117 |
Detecting Violations of Assumptions | p. 125 |
Remedies: Alternative Approaches When Problems Are Detected | p. 141 |
Summary | p. 150 |
Data-Analytic Strategies Using Multiple Regression/Correlation | p. 151 |
Research Questions Answered by Correlations and Their Squares | p. 151 |
Research Questions Answered by B Or [beta] | p. 154 |
Hierarchical Analysis Variables in Multiple Regression/Correlation | p. 158 |
The Analysis of Sets of Independent Variables | p. 162 |
Significance Testing for Sets | p. 171 |
Power Analysis for Sets | p. 176 |
Statistical Inference Strategy in Multiple Regression/Correlation | p. 182 |
Summary | p. 190 |
Quantitative Scales, Curvilinear Relationships, and Transformations | p. 193 |
Introduction | p. 193 |
Power Polynomials | p. 196 |
Orthogonal Polynomials | p. 214 |
Nonlinear Transformations | p. 221 |
Nonlinear Regression | p. 251 |
Nonparametric Regression | p. 252 |
Summary | p. 253 |
Interactions Among Continuous Variables | p. 255 |
Introduction | p. 255 |
Centering Predictors and the Interpretation of Regression Coefficients in Equations Containing Interactions | p. 261 |
Simple Regression Equations and Simple Slopes | p. 267 |
Post Hoc Probing of Interactions | p. 272 |
Standardized Estimates for Equations Containing Interactions | p. 282 |
Interactions as Partialed Effects: Building Regression Equations With Interactions | p. 284 |
Patterns of First-Order and Interactive Effects | p. 285 |
Three-Predictor Interactions in Multiple Regression | p. 290 |
Curvilinear by Linear Interactions | p. 292 |
Interactions Among Sets of Variables | p. 295 |
Issues in the Detection of Interactions: Reliability, Predictor Distributions, Model Specification | p. 297 |
Summary | p. 300 |
Categorical or Nominal Independent Variables | p. 302 |
Introduction | p. 302 |
Dummy-Variable Coding | p. 303 |
Unweighted Effects Coding | p. 320 |
Weighted Effects Coding | p. 328 |
Contrast Coding | p. 332 |
Nonsense Coding | p. 341 |
Coding Schemes in the Context of Other Independent Variables | p. 342 |
Summary | p. 351 |
Interactions With Categorical Variables | p. 354 |
Nominal Scale by Nominal Scale Interactions | p. 354 |
Interactions Involving More Than Two Nominal Scales | p. 366 |
Nominal Scale by Continuous Variable Interactions | p. 375 |
Summary | p. 388 |
Outliers and Multicollinearity: Diagnosing and Solving Regression Problems II | p. 390 |
Introduction | p. 390 |
Outliers: Introduction and Illustration | p. 391 |
Detecting Outliers: Regression Diagnostics | p. 394 |
Sources of Outliers and Possible Remedial Actions | p. 411 |
Multicollinearity | p. 419 |
Remedies for Multicollinearity | p. 425 |
Summary | p. 430 |
Missing Data | p. 431 |
Basic Issues in Handling Missing Data | p. 431 |
Missing Data in Nominal Scales | p. 435 |
Missing Data in Quantitative Scales | p. 442 |
Summary | p. 450 |
Multiple Regression/Correlation and Causal Models | p. 452 |
Introduction | p. 452 |
Models Without Reciprocal Causation | p. 460 |
Models With Reciprocal Causation | p. 467 |
Identification and Overidentification | p. 468 |
Latent Variable Models | p. 469 |
A Review of Causal Model and Statistical Assumptions | p. 475 |
Comparisons of Causal Models | p. 476 |
Summary | p. 477 |
Alternative Regression Models: Logistic, Poisson Regression, and the Generalized Linear Model | p. 479 |
Ordinary Least Squares Regression Revisited | p. 479 |
Dichotomous Outcomes and Logistic Regression | p. 482 |
Extensions of Logistic Regression to Multiple Response Categories: Polytomous Logistic Regression and Ordinal Logistic Regression | p. 519 |
Models for Count Data: Poisson Regression and Alternatives | p. 525 |
Full Circle: Parallels Between Logistic and Poisson Regression, and the Generalized Linear Model | p. 532 |
Summary | p. 535 |
Random Coefficient Regression and Multilevel Models | p. 536 |
Clustering Within Data Sets | p. 536 |
Analysis of Clustered Data With Ordinary Least Squares Approaches | p. 539 |
The Random Coefficient Regression Model | p. 543 |
Random Coefficient Regression Model and Multilevel Data Structure | p. 544 |
Numerical Example: Analysis of Clustered Data With Random Coefficient Regression | p. 550 |
Clustering as a Meaningful Aspect of the Data | p. 553 |
Multilevel Modeling With a Predictor at Level 2 | p. 553 |
An Experimental Design as a Multilevel Data Structure: Combining Experimental Manipulation With Individual Differences | p. 555 |
Numerical Example: Multilevel Analysis | p. 556 |
Estimation of the Multilevel Model Parameters: Fixed Effects, Variance Components, and Level 1 Equations | p. 560 |
Statistical Tests in Multilevel Models | p. 563 |
Some Model Specification Issues | p. 564 |
Statistical Power of Multilevel Models | p. 565 |
Choosing Between the Fixed Effects Model and the Random Coefficient Model | p. 565 |
Sources on Multilevel Modeling | p. 566 |
Multilevel Models Applied to Repeated Measures Data | p. 566 |
Summary | p. 567 |
Longitudinal Regression Methods | p. 568 |
Introduction | p. 568 |
Analyses of Two-Time-Point Data | p. 569 |
Repeated Measure Analysis of Variance | p. 573 |
Multilevel Regression of Individual Changes Over Time | p. 578 |
Latent Growth Models: Structural Equation Model Representation of Multilevel Data | p. 588 |
Time Varying Independent Variables | p. 595 |
Survival Analysis | p. 596 |
Time Series Analysis | p. 600 |
Dynamic System Analysis | p. 602 |
Statistical Inference and Power Analysis in Longitudinal Analyses | p. 604 |
Summary | p. 605 |
Multiple Dependent Variables: Set Correlation | p. 608 |
Introduction to Ordinary Least Squares Treatment of Multiple Dependent Variables | p. 608 |
Measures of Multivariate Association | p. 610 |
Partialing in Set Correlation | p. 613 |
Tests of Statistical Significance and Statistical Power | p. 615 |
Statistical Power Analysis in Set Correlation | p. 617 |
Comparison of Set Correlation With Multiple Analysis of Variance | p. 619 |
New Analytic Possibilities With Set Correlation | p. 620 |
Illustrative Examples | p. 621 |
Summary | p. 627 |
Appendices | |
The Mathematical Basis for Multiple Regression/Correlation and Identification of the Inverse Matrix Elements | p. 631 |
Determination of the Inverse Matrix and Applications Thereof | p. 636 |
Appendix Tables | p. 643 |
References | p. 655 |
Glossary | p. 671 |
Statistical Symbols and Abbreviations | p. 683 |
Author Index | p. 687 |
Subject Index | p. 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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