- Among those who both oppose free trade agreements and a path to citizenship for illegal immigrants, what percentage of respondents voted for Trump? Among those who both favor free trade agreements and favor a path to citizenship, what percentage voted for Trump?
- Among those who favor free trade agreements but oppose a path to citizenship, what percentage of respondents voted for Trump? How about among those oppose free trade agreements but favor a path to citizenship?
- In examining the joint impact of these two variables on the vote, does the combination of the two variables provide a better explanation that either variable alone? Does one variable seem to have a greater effect, or do both have a similar effect?
Exercise 10: Attitudes on Trade, Illegal Immigration, and the Presidential Vote
Another example of the joint influence of two independent variables involves the impact of two different issue orientations on the vote: attitudes on trade and on illegal immigration. Two variables that measure attitudes on these issues are: F21, favor/oppose free trade agreements; and K13, favor/oppose providing a path to citizenship for unauthorized immigrants who obey the law, pay a fine, and pass security check?
Attitudes on Trade and Immigration
Donald Trump’s position on trade stood out from other past Republican presidential candidates. He frequently criticized trade policy that previous U.S. presidents had negotiated as harmful to manufacturing workers in the United States. As president, Trump revised trade deals such as the North American Free Trade Agreement (NAFTA). Second, Trump emphasized stricter immigration policies, focusing especially on border control with Mexico.
Step A. Create and interpret Tables 10A and 10B
To examine the effects of these variables on the presidential vote, create Tables 10A and 10B, two basic two-variable tables: one table for the relationship between trade policy attitudes (F21) and the presidential vote (Table 10A) and one table for the relationship between support for creating a path to citizenship (K13) and the presidential vote (Table 10B). For the reasons suggested in exercise one, 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 Tables 10A and 10B as suggested, you should have two tables. Table 10A (vote by free trade policy) should have seven columns and two rows. Table 10B (vote by favor/oppose a path to citizenship for unauthorized illegals) should have seven columns and two rows. In each case, the attitude (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:
- In Table 10A, how much difference in their presidential voting is there between those who favor free trade agreements and those who do not?
- In Table 10B, how much difference in their presidential voting is there between those who oppose a path to citizenship for illegal immigrants and those who do not? Looking at all seven categories of K13, what is the overall relationship between these two variables?
- Is the relationship in Table 10A stronger, weaker, or about the same as the relationship in Table 10B?
Step B. Create Table 10C
These tables show that both attitudes are related to the vote. Trump did better among both those who oppose free trade agreements and those who oppose a path to citizenship for illegal immigrants. This raises an interesting question: what if a voter agreed with Trump on one of these two issues, but disagreed with him on the other? Did both of these variables equally affect the vote, or was one a more importance influence? To examine these questions, create Table 10C that uses attitudes on both issues as independent variables and presidential vote as the dependent variable.
To simplify the table, recode F21 and K13 so that each has three categories (favor/oppose/neither favor nor oppose). In this case, it does not matter whether F21 is treated as the independent variable and the recoded version of K13 is the control variable, or the reverse, since we are considering both to be independent variables that jointly affect the vote.
F21 and K13 both have seven categories, ranging from “favor a great deal” to “oppose a great deal.” To simplify the three-variable table that we want to create, it would be helpful to collapse these seven categories down to just three. One way to recode F21 and K13 would be to combine the favor and oppose categories, thus dividing respondents into those who: (a) favor; (b) oppose; and (c) neither favor nor oppose.