OVERVIEW

Despite what we have learned in the preceding chapters on hypothesis testing and statistical significance, we have not yet crossed all four of our hurdles for establishing causal relationships. Recall that all of the techniques we have learned in Chapters 8 and 9 are simply bivariate, X-and-Y-type analyses. But, to fully assess whether X causes Y, we need to control for other possible causes of Y, which we have not yet done. In this chapter, we show how multiple regression – which is an extension of the two-variable model we covered in Chapter 9 – does exactly that. We explicitly connect the formulae that we include to the key issues of research design that tie the entire book together. We also discuss some of the problems in multiple regression models when key causes of the dependent variable are omitted, which ties this chapter to the fundamental principles presented in Chapters 3 and 4.

MODELING MULTIVARIATE REALITY

From the very outset of this book, we have emphasized that almost all interesting phenomena in social reality have more than one cause. And yet most of our theories are simply bivariate in nature.

We have shown you (in Chapter 4) that there are distinct methods for dealing with the nature of reality in our designs for social research. If we are fortunate enough to be able to conduct an experiment, then the process of randomly assigning our participants to treatment groups will automatically “control for” those other possible causes that are not a part of our theory.