Gives new social science graduate students sufficient background in quantitative multivariate analysis (e.g., regression analysis) to allow them to read research relying on such analysis prior to formal training in statistical methods.
Gives new social science graduate students sufficient background in quantitative multivariate analysis (e.g., regression analysis) to allow them to read research relying on such analysis prior to formal training in statistical methods.
List of Tables and Figures Preface for Teachers and Students Acknowledgments Introduction The Concept of Causation Experimental Research The Logic Underlying Regression Analysis Some Necessary Math Background The Bivariate Regression Model The Equation The Intercept The Slope Coefficient The Error or Disturbance Term Some Necessary Assumptions Estimating Coefficients with Data from a Sample The Multivariate Regression Model The Value of Multivariate Analysis Interpreting the Coefficients of a Multivariate Regression Model Dichotomous and Categorical Independent Variables The Assumptions of Multivariate Regression Choosing the Independent Variables for a Regression Model Evaluating Regression Results Standardized Coefficients Strong Relationships Among the Independent Variables: The Problem of Multicollinearity Measuring the Fit of a Regression Model Statistical Significance Cross-Sectional vs. Time-Series Data Some Illustrations of Multiple Regression Lobbying in Congress Population Dynamics and Economic Development Advanced Topics Interaction vs. Nonlinearity Interactive Models Nonlinear Models Dichotomous Dependent Variables: Probit and Logit Multi-equation Models: Simultaneous Equation Models and Recursive Causal Models Conclusion Glossary References Index
List of Tables and Figures Preface for Teachers and Students Acknowledgments Introduction The Concept of Causation Experimental Research The Logic Underlying Regression Analysis Some Necessary Math Background The Bivariate Regression Model The Equation The Intercept The Slope Coefficient The Error or Disturbance Term Some Necessary Assumptions Estimating Coefficients with Data from a Sample The Multivariate Regression Model The Value of Multivariate Analysis Interpreting the Coefficients of a Multivariate Regression Model Dichotomous and Categorical Independent Variables The Assumptions of Multivariate Regression Choosing the Independent Variables for a Regression Model Evaluating Regression Results Standardized Coefficients Strong Relationships Among the Independent Variables: The Problem of Multicollinearity Measuring the Fit of a Regression Model Statistical Significance Cross-Sectional vs. Time-Series Data Some Illustrations of Multiple Regression Lobbying in Congress Population Dynamics and Economic Development Advanced Topics Interaction vs. Nonlinearity Interactive Models Nonlinear Models Dichotomous Dependent Variables: Probit and Logit Multi-equation Models: Simultaneous Equation Models and Recursive Causal Models Conclusion Glossary References Index
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