Exercise 4. Attitude on abortion and the presidential vote

Step A. Create and interpret Table 4A

Another issue that might have influenced how people voted in the 2020 presidential election is the issue of abortion. To examine whether attitudes on this issue affected voting, we can look at a table that relates attitudes toward abortion to the presidential vote. Respondents in the 2020 ANES were asked about the conditions under which abortion should be legal. One could hypothesize that being more willing to allow abortions would make one more likely to vote for Biden. To examine that possibility, we can create Table 4A and look at how it relates attitude toward abortion (K03) to the 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 (i.e., only the Biden and Trump voters).

If you ran Table 4A as suggested, you should have a table with four columns and two rows. Attitude toward abortion (the independent variable) should be on the top of the table (the column variable), and the two-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 interpret the percentages properly, remembering that they are column percentages, not row percentages.

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

  1. What percentage of the voters who thought that abortion should never be permitted voted for Trump? What percentage of the voters who thought that abortion should always be permitted cast a ballot for Trump? Looking at all four columns of this table, is the relationship between the two variables clear and consistent?As discussed previously, it does not matter whether you focus on the vote for Trump or for Biden. If voters who were anti-abortion were more likely to vote for Trump, then those who were pro-abortion must have been more likely to vote for Biden.
  2. Overall, how strong of a relationship is there between these two variables? How does the relationship of this issue to voting compare with the relationship that you found in Table 3A?

After examining Table 4A, you should conclude that those who were more supportive of abortion had a much greater propensity to vote for Biden than those who were more opposed. However, as we learned from Exercise 3, this does not necessarily mean that attitudes on this issue really had a significant effect on the vote. The relationship in Table 4A could be the result of the actions of a confounding variable.

Step B. Create a three-variable table

As in Exercise 3, party identification could be a confounding variable. That possibility is worth examining to see if there is a relationship between the independent and dependent variables. To examine this, you need to create Table 4B, a three-variable table that shows the relationship between attitude toward abortion, presidential vote, and party identification. To ensure that you have a sufficient N (i.e., number of cases) for each column, you should recode K03 so that it has just two categories (favor and oppose) and use the recoded version of party identification you created for Exercise 1 (i.e., Democrats, independents, and Republicans).

To create a three-variable table, you must specify a row variable, a column variable, and a control variable. You normally would set up the table by putting your independent variable as the column variable (this will put it on the top of each sub-table), your dependent variable as the row variable (this will put it on the side of each sub-table), and your control variable so that it specifies each sub-table (there will be one sub-table for each category of the control variable). When you are in the SDA crosstabulation program, enter your variables in the row, column, and control dialog boxes. You also will have to enter your recoding instructions for each variable in the appropriate dialog box.

In this example, your control variable should be party identification. You want to recreate Table 3A for each category of party identificationI—Democrats, independents, and Republicans. You want to use your recoded version of party identification, which has only three groups, because if you use the full seven-category version of party identification, you will have too many sub-tables, and the result will be many columns with small Ns and a fairly complex table that will be difficult to interpret.

If the table is set up in the above fashion, you normally would want percentages by columns. You should check this option under “table options.”

You should be sure to have the weight on and that you have selected the weighted Ns to appear in the table. The unweighted data are not a representative sample of the electorate, so be careful not to use them mistakenly.

Because the data are weighted, which means that individual respondents may count as more or less than one person (e.g., as .75 or 1.35 persons), the number of respondents in each cell (the Ns) probably will not be whole numbers. If you prefer to have Ns that are whole numbers, you can revise the output to do that by using the “revise the display” option that appears to the left of the table that you generated.

If statistics are desired, that option should be checked under “table options.” For a discussion of the statistics that are commonly used for contingency tables, see the section on data analysis. In these exercises, we have not asked you to generate statistics, but your instructor may suggest doing so.

The SDA crosstabulation program will produce both a table and a chart, but the chart is not necessary, as all the information you need will be contained in the table that you generate. You can revise the output to drop the chart if you like by using the “revise the display” option.

Step C. Interpret Table 4B

If you ran Table 4B as suggested, you should have a table that consists of three sub-tables. Each sub-table should have two columns and two rows, with the recoded version of attitude toward abortion (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 sub-table for Democrats, one for independents, and one for Republicans. In reading your sub-tables, take care to interpret the percentages properly, remembering that they are column percentages, not row percentages.

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

  1. What is the relationship between attitude toward abortion and presidential vote among Democrats? How about among independents? Among Republicans? Is there still a clear relationship between this attitude and voting?
  2. How does the relationship between attitude toward abortion and the presidential vote in each sub-table compare to the original relationship in Table 4A? Is it stronger, weaker, or about the same? What conclusion do you draw from this?

In interpreting Table 4B, focus on the relationship within each sub-table. For example, among Democrats, there is about a 10 percentage point difference in voting for Biden (or for Trump) between those who approve of abortion (97 percent voted for Biden) and those who disapprove (87 percent voted for Biden). Both groups were heavily for Biden, but the first group was more so, which means that these two variables are related among Democrats. The same is true for Republicans: attitudes toward abortion and the presidential vote for Donald Trump are related in this sub-table as well, and the difference between the favorable and unfavorable groups is 16 percentage points (93% of Republicans who are anti-abortion voted for Trump compared to 77% of Republicans who favor legal abortion). Among independents, the relationship is even stronger on the vote for Trump, with a difference of 27 percentage points separating the favorable and unfavorable groups.

In interpreting the sub-tables, pay attention to the N in each column. A small N is cause for caution, as the column percentages could be unrepresentative due to random sampling error. In this case, the Ns in each column are large. In only one case is the N less than 200.

In this example, the relationship between attitude toward abortion and the presidential vote persists even after we control for party identification. This indicates that attitudes on this issue did influence how people voted. The association between the independent and dependent variables is not as strong in Table 4B as it was in Table 4A, so we would conclude that party identification did inflate the bivariate association between the two variables. Nonetheless, the critical point is that the two variables still are clearly and substantially associated even when controlling for party identification.