Exercise 7: Attitude on Gun Control and the Presidential Vote

Step A. Create and interpret Table 7A

Another reason for introducing a control variable into the analysis is to see if the relationship between the independent and dependent variable is stronger for some groups of people than it is for others. If we find that such is the case, we commonly refer to that as a conditional relationship. An example of a conditional relationship involves the association between attitudes toward gun control and the presidential vote. Biden and Trump differed in their positions on gun control, with Biden favoring stricter gun control laws and Trump opposing them. These differences reflected typical differences between the parties on this issue. If voters cast their ballots based on this issue, at least in part, then we would expect voters who favored tougher gun control laws to be more likely to vote for Biden. To see if this is so, create Table 7A, a two-variable table that examines the relationship between attitudes on banning “assault-style” rifles (K07) and presidential vote (A02). To simplify the table, use the recoded version of A02 that excludes the minor party voters, as you have in previous exercises.

If you ran Table 7A as suggested, you should have a table with seven columns and two rows. Favoring/opposing the banning of “assault-style” rifles (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 voters who favored an assault rifles ban voted for Biden? What percentage of those opposed did so? The difference between these percentages is one indicator of the strength of the relationship between these two variables.
  2. Note that we could also use the difference between the percentage of those favoring an assault rifles ban and those opposing such a ban who cast a ballot for Trump. The difference between these two percentages is the same as the difference obtained by using the percentages for Biden, given that we are using the two-party presidential vote.

Step B. Create Table 7B

Table 7A shows that there is a strong relationship between support for an assault rifles ban and the presidential vote. This does not mean that all groups of voters were equally influenced by this issue. We can hypothesize about the types of voters for whom this relationship would be stronger and the types for whom it would be weaker. One possible hypothesis is that the relationship will be stronger for those who support background checks for all gun purchases. Those who favor background checks would be more likely to favor banning assault rifles. You can test this hypothesis by introducing favorability toward background checks as a control variable (K06) into the analysis when you create Table 7B.

Variable K06 has seven response categories to reflect how favorable a respondent is toward background checks for gun purchases. Respondents could have said that:

  1. Favor a great deal;
  2. Favor a moderate amount;
  3. Favor a little;
  4. Neither favor nor oppose;
  5. Oppose a little;
  6. Oppose a moderate amount;
  7. Oppose a great deal

It is quite easy to recode K06 into three categories, putting the first three categories into a clearly pro background check group, the fourth into the middle group (neither favor nor oppose), and the last three categories into anti background check group.

Step C. Interpret Table 7B

If you ran Table 7B as suggested, you should have a table that consists of three sub-tables. Each sub-table should have seven columns and two rows. Each sub-table should have favor/oppose an assault rifles ban (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 each category of favoring background checks (favor, neither favor nor oppose, oppose). 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 opposition to a ban on assault rifles and presidential vote among those who favor background checks for all gun purchases? What is the relationship among those who oppose background checks? Be sure to focus on the strength of the relationship in each sub-table.
  2. How does the relationship between the attitude and presidential vote in each sub-table compare to the original relationship in Table 7A? What conclusion do you draw from this?

In this example, there is a relationship between the attitude toward gun control and presidential vote in each sub-table, but the second sub-table (neither favor nor oppose) does not have enough individuals to draw any substantive conclusions. In both the first sub-table and the last sub-table, there are large differences in voting for Biden between those who favor the assault weapons ban a great deal and those oppose it a great deal. The intervening variable had no major impact on the strength of the relationship.

In this example, our original two-variable relationship remains the same regardless of whether someone favors or opposes background checks for all gun purchases. In both major sub-tables, those who oppose an assault rifles ban are more likely to vote for Trump than are those who favor them.