Chapter 18. Tabular Bivariate and Multivariate Analyses

Introduction

Bivariate analysis is the simultaneous analysis of two variables. It is usually undertaken to see if one variable, such as gender, is related to another variable, perhaps attitudes toward male/female equality. Multivariate analysis is the simultaneous analysis of three or more variables. It is frequently done to refine a bivariate analysis, taking into account the possible influence of a third variable on the original bivariate relationship. Multivariate analysis is also used to test the joint effects of two or more variables upon a dependent variable.

This chapter describes how to carry out bivariate and multivariate analyses by means of constructing tables. Tables can be used to show bivarate and multivariate relations with any variables, whether the variables are measured at the nominal, ordinal, interval or ratio levels . Also, variables measured at each of these levels can be analyzed by means of statistical tests of significance. Chapter 19 includes descriptions of a few of the many tests of statistical significance you can use. Any introductory statistics textbook can provide descriptions of additional tests of statistical significance. So can most of the Web sites we mention in Chapter 19. We begin this chapter with the simplest form of bivariate analysis. This is the analysis of two variables measured at the nominal level.

Bivariate analysis: nominal variables

Suppose we wanted to find out if gender was related to attitudes toward equality between men and women and had measured each variable at the nominal level. The variables are:

Gender - measured as male or female; and

Attitude - measured simply as a "in favor of" or as "opposed to" gender equality.

To test the relationship between gender and attitude, let's develop a hypothesis based on the following theoretical base:

• Formation of attitudes is based on socialization, which begins in infancy and extends over many years;
• In all societies, males and females are socialized according to different norms;
• The female subculture generally favors imparting positive attitudes toward gender equality, whereas the male subculture stresses maintenance of male prerogatives.

This theory of self interest suggests that females are socialized in ways to acquire more favorable views toward gender equality. We therefore designate gender as the independent variable and attitudes toward gender equality as the dependent variable for this analysis. On this basis, we can establish the following hypothesis:

Adult females have more favorable views toward gender equality than adult males.

We will test this hypothesis by constructing a two-way or bivariate frequency distribution. Most computer-based programs can do this quickly, once all the data are properly coded and entered into the computer memory. However, we will use hand analysis to show the logic of bivariate analysis.

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