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Statistical methods for social scientists /
The aspects of this text which we believe are novel, at least in degree, include: an effort to motivate different sections with practical examples and an empirical orientation; an effort to intersperse several easily motivated examples throughout the book and to maintain some continuity in these exa...
| Clasificación: | Libro Electrónico |
|---|---|
| Autores principales: | , |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
New York :
Academic Press,
�1977.
|
| Colección: | Quantitative studies in social relations.
|
| Temas: | |
| Acceso en línea: | Texto completo |
Tabla de Contenidos:
- Front Cover; Statistical Methods for Social Scientists; Copyright Page; Table of Contents; Preface; Acknowledgments; Chapter 1. Empirical Analyses in the Social Sciences; 1.1 Introduction; 1.2 Social Science Theory and Statistical Models; 1.3 Fitting Models to Data; 1.4 The Development of Stochastic Models; 1.5 The Analysis of Nonexperimental Data and the Selection of a Statistical Procedure; 1.6 Simple Methods; REVIEW QUESTIONS; Chapter 2. Estimation with Simple Linear Models; 2.1 Introduction; 2.2 The Basic Model; 2.3 Least Squares Estimators; 2.4 Two Examples; 2.5 Conclusion; APPENDIX 2.1.
- APPENDIX 2.2REVIEW QUESTIONS; Chapter 3. Least Squares Estimators: Statistical Properties and Hypothesis Testing; 3.1 Introduction; 3.2 Properties of Least Squares Estimators; 3.3 Distribution of b-A Monte Carlo Experiment; 3.4 Statistical Inference; 3.5 Hypothesis Tests for Schooling/Earnings Model; 3.6 Conclusion; APPENDIX 3.1; REVIEW QUESTIONS; Chapter 4. Ordinary Least Squares in Practice; 4.1 Introduction; 4.2 Interpretation of Regression Coefficients; 4.3 Model Specification; 4.4 Multicollinearity; 4.5 Model Specification and Multicollinearity in Practice; 4.6 Functional Forms.
- 4.7 Dummy Explanatory VariablesREVIEW QUESTIONS; Chapter 5. Multivariate Estimation in Matrix Form; 5.1 Introduction; 5.2 The Least Squares Estimators; 5.3 Least Squares in Matrix Notation; 5.4 Properties of Least Squares; 5.5 Distributional Aspects of the Error Term; 5.6 Statistical Inference; 5.7 Multivariate Education Example; 5.8 Multicollinearity; 5.9 Conclusion; APPENDIX 5.1; APPENDIX 5.2; REVIEW QUESTIONS; Cahpter 6. Generalized Least Squares; 6.1 Introduction; 6.2 Heteroskedasticity and Autocorrelation; 6.3 Formal Statement of the Problem; 6.4 Generalized Least Squares.
- 6.5 Generalized Least Squares and Examples of Heteroskedasticity and Autocorrelation6.6 Generalized Least Squares and Weighted Regression; 6.7 Monte Carlo Simulation of Generalized Least Squares; 6.8 Generalized Least Squares in Practice; 6.9 Visual Diagnostics; 6.10 Dynamic Models; 6.11 Conclusion; APPENDIX 6.1; APPENDIX 6.2; REVIEW QUESTIONS; Chapter 7. Models with Discrete Dependent Variables; 7.1 Introduction; 7.2 The Problem of Estimating Models with Discrete Dependent Variables; 7.3 Alternative Models-Dichotomous Dependent Variables; 7.4 Logit Analysis-Grouped Data.
- 7.5 Logit Analysis-Microdata7.6 Probit Analysis; 7.7 An Example; 7.8 Monte Carlo Simulation of Dichotomous Dependent Variables; 7.9 Polytomous Variables/Joint Distributions; 7.10 Conclusions; Chapter 8. Introduction to Multiequation Models; 8.1 Introduction; 8.2 Two Examples of Structural Systems; 8.3 Path Analysis; 8.4 The General Multiequation Model; 8.5 Estimating Hierarchical Models; 8.6 Hierarchical, Nonrecursive Systems; 8.7 Underidentification in Hierarchical Models; 8.8 Nonrecursive Hierarchical Models: Two Examples; 8.9 Conclusion; APPENDIX 8.1; REVIEW QUESTIONS.