Understanding Multivariate Research: A Primer for Beginning Social Scientists / Edition 1

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Overview

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.

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Product Details

  • ISBN-13: 9780813399713
  • Publisher: Westview Press
  • Publication date: 4/28/2000
  • Series: Essentials of Political Science Series
  • Edition description: New Edition
  • Edition number: 1
  • Pages: 104
  • Sales rank: 450,978
  • Lexile: 1550L (what's this?)
  • Product dimensions: 5.26 (w) x 8.09 (h) x 0.26 (d)

Meet the Author

William D. Berry is Professor of Political Science at Florida State University. He is a contributor to American Political Science Review, Public Administration Review, American Journal of Political Science, and Journal of Politics, and has served on the editorial board of the latter two journals. He has published several other books on related subjects, and has received the Policy Studies Organization’s Harold Laswell Award for outstanding career contributions to the study of the policy making process. Berry has also served as president of the State Politics and Policy section of the American Political Science Association. Mitchell Sanders is Assistant Professor of Political Science at Florida State University. His research can be found in American Journal of Political Science, Political Research Quarterly, Public Choice, and Political Analysis. William D. Berry is Professor of Political Science at Florida State University. He is a contributor to American Political Science Review, Public Administration Review, American Journal of Political Science, and Journal of Politics, and has served on the editorial board of the latter two journals. He has published several other books on related subjects, and has received the Policy Studies Organization’s Harold Laswell Award for outstanding career contributions to the study of the policy making process. Berry has also served as president of the State Politics and Policy section of the American Political Science Association. Mitchell Sanders is Assistant Professor of Political Science at Florida State University. His research can be found in American Journal of Political Science, Political Research Quarterly, Public Choice, and Political Analysis.

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Table of Contents

List of Tables and Figures vii
Preface for Teachers and Students ix
Acknowledgments xiii
1 Introduction 1
The Concept of Causation 1
Experimental Research 2
The Logic Underlying Regression Analysis 5
Some Necessary Math Background 7
2 The Bivariate Regression Model 15
The Equation 15
The Intercept 17
The Slope Coefficient 18
The Error or Disturbance Term 19
Some Necessary Assumptions 20
Estimating Coefficients with Data from a Sample 24
3 The Multivariate Regression Model 29
The Value of Multivariate Analysis 29
Interpreting the Coefficients of a Multivariate Regression Model 31
Dichotomous and Categorical Independent Variables 33
The Assumptions of Multivariate Regression 37
Choosing the Independent Variables for a Regression Model 38
4 Evaluating Regression Results 41
Standardized Coefficients 41
Strong Relationships Among the Independent Variables: The Problem of Multicollinearity 43
Measuring the Fit of a Regression Model 44
Statistical Significance 45
Cross-Sectional vs. Time-Series Data 49
5 Some Illustrations of Multiple Regression 51
Lobbying in Congress 51
Population Dynamics and Economic Development 57
6 Advanced Topics 63
Interaction vs. Nonlinearity 63
Interactive Models 64
Nonlinear Models 68
Dichotomous Dependent Variables: Probit and Logit 72
Multi-equation Models: Simultaneous Equation Models and Recursive Causal Models 76
7 Conclusion 79
Glossary 81
References 83
Index 85
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