Understanding Multivariate Research A Primer For Beginning Social Scientists [Paperback]

Although nearly all major social science departments offer graduate students training in quantitative methods, the typical sequencing of topics generally delays training in regression analysis and other multivariate techniques until a student's second year. William Berry and Mitchell Sanders's Understanding Multivariate Research fills this gap with a concise introduction to regression analysis and other multivariate techniques. Their book is designed to give new graduate students a grasp of multivariate analysis sufficient to understand the basic elements of research relying on such analysis that they must read prior to their formal training in quantitative methods. Berry and Sanders effectively cover the techniques seen most commonly in social science journals--regression (including nonlinear and interactive models), logit, probit, and causal models/path analysis. The authors draw on illustrations from across the social sciences, including political science, sociology, marketing and higher education. All topics are developed without relying on the mathematical language of probability theory and statistical inference. Readers are assumed to have no background in descriptive or inferential statistics, and this makes the book highly accessible to students with no prior graduate course work.* 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 VariableslÓ)