Exercise 7: Abortion attitude, presidential vote, and issue importance
Another reason for introducing a control variable into the analysis is to see if the relationship between the independent and dependent variable is much 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. A good example of a conditional relationship involves the association between abortion and presidential vote. Obama and McCain differed in their positions on abortion, each taking the position generally associated with his party: Obama was pro-abortion and McCain anti-abortion, although neither was the staunchest and most ardent proponent of his position. If voters cast their ballots on the basis of this issue, at least in part, then we would expect voters with more pro-abortion attitudes to be more likely to vote for Obama. To see if this is so, generate a two-variable table that examines the relationship between attitude on abortion (V104) and presidential vote (V002). To simplify the table, recode V104 into two categories, as you did in exercise 6, and use the recoded version of V002 that excludes the minor party voters, as you have in previous exercises.
Table 7A shows that there is a fairly strong relationship between attitude on abortion and presidential vote. 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 think that it is an issue of high importance. You can test this hypothesis by introducing a control variable (V105) into the analysis. To keep your table simple and to ensure that there are enough respondents in each column of each subtable, recode V105 so that it has just two categories (high importance and low importance).
In this example, our original two-variable relationship becomes much weaker in one category of the control variable and becomes much stronger in the other category. Therefore V105 is a conditional variable in this situation, as we hypothesized.