Introduction to Multiple Regression

  • Scott M. Lynch
Chapter

Abstract

In the previous chapters, we have focused on relationships between only two variables at a time. Most relationships that we are interested in social science research are more complicated, however, than can be understood with only bivariate analyses. In some cases, the relationship between two variables depends entirely on a third variable, and so a bivariate analysis can be misleading. In some cases, the relationship between two variables depends on, or operates through, additional variables. In this chapter, we will discuss multiple regression. In multiple regression analysis, a single outcome variable is modeled as a linear combination of as many additional variables as desired. Multiple regression is sometimes used simply to understand factors that are relevant to predicting an outcome, but it is also used in science to help establish that the relationship between two variables is a causal one and to understand complex relationships that simply cannot be understood with bivariate analyses. In this chapter, before introducing the details of multiple regression, we will discuss causal thinking in order to lay the foundation for seeing why multivariate analyses are necessary.

Keywords

Placebo Cholesterol Obesity Income 

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Copyright information

© Springer Science+Business Media New York 2013

Authors and Affiliations

  • Scott M. Lynch
    • 1
  1. 1.Department of SociologyPrinceton UniversityPrincetonUSA

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