**Four Steps for Conducting Bivariate Analysis
**By Daniel Palazzolo, Ph.D.

The statistics we use for bivariate analysis are determined by levels of measurement for the two variables. We normally will want to take four steps in conducting a bivariate analysis. Keep in mind, we use statistics to test a bivariate hypothesis. In commonsense terms, we are using statistics to explain the relationship between the two variables and to determine the strength and significance of the relationship. I will use the relationship between gender and party identification to illustrate a bivariate analysis.

Here are the four steps:

**Step 1:** Define the nature of the relationship in terms
of how the values of the independent variables relate to the values of
the dependent variable.

For example, if I am testing the relationship between gender and party identification, then I will ultimately say something to the effect of:

“The data show a relationship between gender and party identification; women are more likely than men to call themselves Democrats; and men are more likely than women to call themselves Republicans. About 39% of women call themselves Democrats compared with just 30% of men; and about 29% of men call themselves Republicans compared with about 23% of women. The crosstab shows that men are slightly more likely to be independents, but the difference is so small (about 2% points), that a difference among the population is unlikely.”

**Step 2:** Identify the type and direction (if applicable)
of the relationship.

Ex: In the example above, gender is nominal and party identification is ordinal, so it is a correlative relationship.

**Step 3:** Determine if the relationship is statistically
significant, i.e. different from the null hypothesis (meaning there is
no expected relationship), and generalizable to the population.

Ex: We use Chi-square to determine the statistical significance of a relationship with at least one nominal variable; and looking at the chi-square table, we see that the relationship is significant at beyond the .05 level.

**Step 4:** Identify the strength of the relationship, i.e.
the degree to which the values of the independent variable explain the
variation in the dependent variable.

Ex: We can use Lambda or Cramer’s V to measure strength, and we see that on a scale from 0 to 1, a symmetric value of .01 for Lamba and .098 for Cramer’s V places strength on the low end of the scale. Thus, this is not a strong relationship, although it is statistically significant.

Back to "Four Steps for Conducting Bivariate Analysis" or the Political Science main page.

Copyright 2010