INTRODUCTION

The first part of this chapter introduces regression analysis, one of the most widely used multivariate statistical techniques. Although a comprehensive treatment of this topic is beyond the scope of a first course in research methods, a brief introduction to regression analysis is essential because it appears so frequently in quantitative social research literature. The aim of the present discussion is to give the reader a basic overview and some suggestions for further reading. One reason that regression analysis is so widely used is that it lends itself to causal modeling. Although there are many types of causal modeling, we discuss path analysis here because it is one of the most commonly used types.

The second section of this chapter covers statistical inference. Social researchers use tests of significance to make inferences about a population (or universe) based on the evidence obtained from a sample selected from that population. Many social researchers put a great deal of confidence in these tests of significance, and it is important that both researchers and consumers of social research understand their uses and misuses. Although they can help researchers to answer legitimately a wide range of questions, tests of significance simply cannot answer some questions.

MULTIVARIATE ANALYSIS

Regression analysis

linear regression is a statistical procedure used to estimate the amount of change in a dependent variable that can be expected for a given change in an independent variable.