Exercise 6. Gender and the Presidential Vote

Step A. Create and interpret Table 6A

One of the interesting features of American politics over the past 30 years is the existence of a gender gap in voting behavior. Specifically, women have been more likely than men to vote for Democratic candidates. To examine this relationship for 2016, you should create Table 6A that shows how an individual’s gender (R01) was related to the individual’s presidential vote. For the reasons suggested in exercise 1, you should use the recoded version of A02 that you created for that exercise, so that you examine only the major-party vote for president.

If you ran Table 6A as suggested, you should have a table with three columns and two rows. Gender (the independent variable) should be on the top of the table (the column variable), and the major-party presidential vote (the dependent variable) should be on the side of the table (the row variable). Percentages should be calculated by column (i.e., they should sum to 100% for each column). In reading your table, take care to properly interpret the percentages, remembering that they are column percentages, not row percentages.

You should attempt to answer these questions to see if you are able to correctly read the table and interpret the data:

  • What percentage of men cast a ballot for Clinton? What percentage of women did so? The difference between these two percentages is the gender gap for the presidential election—the gender difference in voting for this office. Overall, how strong of a relationship is there between these two variables?
  • Note that we could also calculate the gender gap by using the percentage of men who cast a ballot for Trump and the percentage of women who voted for Trump. The difference between these two percentages also is the gender gap in voting. Since we are using the two-party presidential vote, we will obtain the same number regardless of whether we focus on the Democratic or the Republican percentage.
  • There were a few respondents who did not select male or female as a gender identity. However, there were so few people in this category that we cannot meaningfully make any conclusions about this group of people. Given the small number of people in this category, we probably should drop them from further analysis.

Table 6A raises as many questions as it answers. Why should gender be related to the vote? What explains the greater tendency for women to vote Democratic (or men to vote Republican)? We can attempt to explain this relationship by examining possible intervening variables (i.e., those that are influenced by the independent variable and in turn affect the dependent variable). In this case, we would might want to identify the attitudes that create a gender gap in voting. Some people might speculate that attitudes toward abortion produce the relationship. Democrats have been more supportive of abortion rights than have Republicans. Perhaps women are more likely to vote for Democratic candidates because they are more supportive of abortion and cast their ballots on the basis of that issue.

Step B. Create Table 6B

As we saw in exercise 5, in order to examine a potential intervening variable, you should run the original two-variable relationship with the potential intervening variable added as a control variable. In this exercise, you should use K01 (attitude toward abortion) as your control variable, and you should recode K01 so that there are just two categories (favor and oppose) as outlined in Exercise 4, which will make the three-variable table easier to interpret. Also, recode R01 so that there are just two categories, male and female (drop the “other” category). For information on how to create a three-variable table using SDA, see exercises 3 or 4. Create Table 6B.

Variable K01 has four response categories. Respondents could have said that:

  1. Abortion should never be allowed;
  2. Abortion should be allowed only in limited circumstances, such as rape, incest, or when the life of the mother is in danger;
  3. Abortion should be allowed in more circumstances, but the need for abortion should be established;
  4. Abortion should always be allowed.

The first two categories are best classified as anti-abortion or pro-life positions. Even allowing abortion in very limited circumstances is generally accepted as being opposed to abortion. For example, President George W. Bush favored allowing abortion in very limited circumstances, and he was always considered to be opposed to legalized abortion. The last category is clearly a pro-abortion or pro-choice position. The third category is somewhat ambiguous. Permitting abortion in a wider set of circumstances does not fit with the perspective of those opposed to abortion, but stating that a need must be established does not fit with the perspective of those who think that it should be the decision of the mother and her doctor. If we want to recode this variable into just two categories (pro and anti-abortion), then we must decide where this third category fits. We suggest that it makes more sense to combine the third and fourth categories, which will divide those who would allow abortion either not at all or only in very limited circumstances from those who would allow abortion in a broader set of circumstances.

An alternative would be to recode K01 into three categories, putting the first two categories into a clearly anti-abortion group, the fourth category into a clearly pro-abortion group, and the third category into a middle group. That is a legitimate option, and you could choose to do that. As we have discussed in earlier exercises, there often is more that one reasonable way to recode a variable. While we could keep the third group as a separate category, in order to make the tables for this exercise as simple as possible, we suggest combining the third and fourth categories into one that is more favorable to abortion.

Step C. Interpret Table 6B

If you ran Table 6B as suggested, you should have a table that consists of two subtables. Each subtable should have two columns and two rows. Both subtables should have gender (the independent variable) on the top of the table (the column variable), and the two-party presidential vote (the dependent variable) on the side of the table (the row variable). Percentages should be calculated by column (i.e., they should sum to 100% for each column). There should be one subtable for those who basically favored legalized abortion and one subtable for those who basically opposed it. In reading your subtables, take care to properly interpret the percentages, remembering that they are column percentages, not row percentages.

You should attempt to answer these questions to see if you are able to correctly read the table and interpret the data:

  1. What is the gender gap in presidential voting among those who favored legalized abortion?
  2. What is the gender gap in presidential voting among those who opposed legalized abortion?
  3. How does the relationship between gender and presidential vote in each subtable compare to the original relationship in Table 6A? Is it stronger, weaker, or about the same? What conclusion do you draw from this?

You should have concluded that the relationship between gender and vote is just as strong in Table 6B as it was in Table 6A. In Table 6A, the gender gap was about 6 percentage points (i.e., women were six points more likely to vote for Clinton than were men). In Table 6B, the gender gap is 5.6 points in one subtable and 7.5 points in the other subtable—an overall gender gap that is just as large as in Table 6A. This indicates that we have not identified an intervening variable between gender and the vote. This does not mean that attitudes toward abortion are unrelated to the vote. In fact, they are, as you can see from comparing the two subtables. It just means that these attitudes do not explain the gender gap in voting.

In this example, the relationship between gender and presidential vote is just as strong when we control for abortion attitude as it is when we do not. This tells us that the effect that gender has on presidential vote is not due to attitudes on abortion. If the original two-variable relationship had greatly weakened in the three-variable table (which is what happened in exercise 5), then we would have concluded that the control variable was a key intervening variable in this relationship, but that is not what happened in this case.

Suggested additional analysis

Generate a table that shows the relationship between gender and attitude on abortion (think about which should be the independent variable and which should be the dependent variable). Does this table help you to better understand how gender, abortion attitude, and the presidential vote are related?

You also could consider other variables that might be related to women’s political positions as potential control variables. In this dataset, the L series of variables (L01 through L08) are all questions on gender issues. You could try one or more of these as control variables to examine potential reasons for the gender gap in 2016 presidential voting.