Understanding multivariate research : a primer for beginning social scientists / William D. Berry, Mitchell S. Sanders.

Includes bibliographical references (page 83) and index.

List of Tables and Figures -- Preface for Teachers and Students -- Acknowledgments -- 1. Introduction -- The Concept of Causation -- Experimental Research -- The Logic Underlying Regression Analysis -- Some Necessary Math Background -- 2. 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 -- 3. 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 -- 4. 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 -- 5. Some Illustrations of Multiple Regression -- Lobbying in Congress -- Population Dynamics and Economic Development -- 6. 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 -- 7. Conclusion -- Glossary -- References -- Index.

Machine converted from AACR2 source record.