Elsevier S & T

Regression Analysis for Social Sciences

von Eye, Alexander; Schuster, Christof

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Table of contents
  • Cover
  • Contentsv
  • Prefacexi
  • CHAPTER 1. INTRODUCTION1
  • CHAPTER 2. SIMPLE LINEAR REGRESSION7
  • 2.1 Linear Functions and Estimation7
  • 2.2 Parameter Estimation12
  • 2.3 Interpreting Regression Parameters26
  • 2.4 Interpolation and Extrapolation28
  • 2.5 Testing Regression Hypotheses29
  • CHAPTER 3. MULTIPLE LINEAR REGRESSION43
  • 3.1 Ordinary Least Squares Estimation44
  • 3.2 Data Example50
  • 3.3 Multiple Correlation and Determination53
  • 3.4 Significance Testing58
  • CHAPTER 4. CATEGORICAL PREDICTORS63
  • 4.1 Dummy and Effect Coding65
  • 4.2 More Than Two Categories70
  • 4.3 Multiple Categorical Predictors77
  • CHAPTER 5. OUTLIER ANALYSIS81
  • 5.1 Leverage Outliers81
  • 5.2 Remedial Measures89
  • CHAPTER 6. RESIDUAL ANALYSIS99
  • 6.1 Illustrations of Residual Analysis100
  • 6.2 Residuals and Variable Relationships106
  • CHAPTER 7. POLYNOMIAL REGRESSION117
  • 7.1 Basics117
  • 7.2 Orthogonal Polynomials124
  • 7.3 Example of Non-Equidistant Predictors128
  • CHAPTER 8. MULTICOLLINEARITY133
  • 8.1 Diagnosing Multicollinearity136
  • 8.2 Countermeasures to Multicollinearity138
  • CHAPTER 9. MULTIPLE CURVILINEAR REGRESSION143
  • CHAPTER 10. INTERACTION TERMS IN REGRESSION151
  • 10.1 Definition and Illustrations151
  • 10.2 Multiplicative Terms154
  • 10.3 Variable Characteristics162
  • CHAPTER 11. ROBUST REGRESSION175
  • 11.1 The Concept of Robustness175
  • 11.2 Models of Robust Regression178
  • 11.3 Computational Issues191
  • CHAPTER 12. SYMMETRIC REGRESSION209
  • 12.1 Pearson’s Orthogonal Regression210
  • 12.2 Other Solutions219
  • 12.3 A General Model for OLS Regression225
  • 12.4 Robust Symmetrical Regression230
  • 12.5 Computational Issues230
  • CHAPTER 13. VARIABLE SELECTION TECHNIQUES237
  • 13.1 A Data Example240
  • 13.2 Best Subset Regression244
  • 13.3 Stepwise Regression251
  • 13.4 Discussion257
  • CHAPTER 14. REGRESSION FOR LONGITUDINAL DATA259
  • 14.1 Within Subject Correlation260
  • 14.2 Robust Modeling of Longitudinal Data266
  • 14.3 A Data Example270
  • CHAPTER 15. PIECEWISE REGRESSION277
  • 15.1 Continuous Piecewise Regression278
  • 15.2 Discontinuous Piecewise Regression281
  • CHAPTER 16. DICHOTOMOUS CRITERION VARIABLES287
  • CHAPTER 17. COMPUTATIONAL ISSUES291
  • 17.1 Creating a SYSTAT System File291
  • 17.2 Simple Regression295
  • 17.3 Curvilinear Regression298
  • 17.4 Multiple Regression304
  • 17.5 Regression Interaction308
  • 17.6 Regression with Categorical Predictors310
  • 17.7 The Partial Interaction Strategy315
  • 17.8 Residual Analysis319
  • 17.9 Missing Data Estimation323
  • 17.10 Piecewise Regression328
  • APPENDIX A. ELEMENTS OF MATRIX ALGEBRA333
  • A.1 Definition of a Matrix333
  • A.2 Types of Matrices335
  • A.3 Transposing Matrices337
  • A.4 Adding Matrices337
  • A.5 Multiplying Matrices338
  • A.6 The Rank of a Matrix342
  • A.7 The Inverse of a Matrix344
  • A.8 The Determinant of a Matrix346
  • A.9 Rules for Operations with Matrices347
  • A.10 Exercises349
  • APPENDIX B. BASICS OF DIFFERENTIATION351
  • APPENDIX C. BASICS OF VECTOR DIFFERENTIATION355
  • APPENDIX D. POLYNOMIALS359
  • D.1 Systems of Orthogonal Polynomials361
  • D.2 Smoothing Series of Measures363
  • APPENDIX E. DATA SETS365
  • E.1 Recall Performance Data365
  • E.2 Examination and State Anxiety Data370
  • References373
  • Index381
Book details
  • Vendor Elsevier S & T
  • SKU 9780127249551
  • ISBN-13 9780080550824
  • Author von Eye, Alexander; Schuster, Christof
  • Category Social Science
  • Subject Statistics

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Description eBook delivery & access Reviews
Regression Analysis for Social Sciences presents methods of regression analysis in an accessible way, with each method having illustrations and examples. A broad spectrum of methods are included: multiple categorical predictors, methods for curvilinear regression, and methods for symmetric regression. This book can be used for courses in regression analysis at the advanced undergraduate and beginning graduate level in the social and behavioral sciences. Most of the techniques are explained step-by-step enabling students and researchers to analyze their own data. Examples include data from the social and behavioral sciences as well as biology, making the book useful for readers with biological and biometrical backgrounds. Sample command and result files for SYSTAT are included in the text.

Key Features
* Presents accessible methods of regression analysis
* Includes a broad spectrum of methods
* Techniques are explained step-by-step
* Provides sample command and result files for SYSTAT