Exercise 3. Attitude on Social Security Spending and the Presidential Vote

Step A. Create and interpret Table 3A

Studies of voting behavior often attempt to explain voting behavior based on certain policy issues. The hypothesis is that voters choose between candidates based on their issue positions. One public policy issue that has received attention in recent years concerns federal entitlement programs for senior citizens, such as Social Security or Medicare. Specifically, these programs fall under the category of social insurance programs, where recipients get a benefit under a program they themselves help fund. Democrats have long been viewed as more supportive of these programs than Republicans.

Social Security was not a major issue in the 2020 campaign, but there did appear to be some differences between the candidates on this issue. Biden opposed any benefit cuts to Social Security, and the Democratic Party platform took similar positions. Trump had not cut benefits as president, but he also opposed increased funding through tax increases.

Did attitudes on Social Security influence voters in 2020? Perhaps the lack of attention given to this issue resulted in little effect. Yet, it also seems plausible to hypothesize that voters who wanted more spending on Social Security would have been more likely to vote for Biden than would voters who did not favor increasing spending on Social Security. To examine whether attitudes on this issue affected how people voted, we can create and look at a table (Create Table 3A) that relates attitude on Social Security spending (J01) 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 you examine only the major-party vote (i.e., only the Biden and Trump voters).

If you ran Table 3A as suggested, you should have a table with five columns and two rows. Attitude toward Social Security spending (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 percent 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 favored increased spending on Social Security cast a ballot for Biden? What percentage of the voters who favored less spending cast a ballot for Trump? How did those who wanted to maintain the same level of spending vote?Because we are using the two-party vote, it makes no difference whether we look at the percentage voting for Biden or for Trump. If those who favored more spending were more likely to vote for Biden, then those who opposed more spending were more likely to vote for Trump.
  2. Overall, is there a clear relationship between these two variables? How strong is the relationship?

After examining Table 3A, you should conclude that those who favored increasing Social Security spending had a greater propensity to vote for Biden than did those who wanted to hold spending constant or even reduce it. Does this prove that voters cast their ballots in part based on this issue, or is there something else that might cause this relationship? In social science research, we must be careful about attributing causality. Often, two variables are associated not because one affects the other but because both are influenced by a confounding variable.

Step B. Create a three-variable table

One possible confounding variable is party identification. The relationship in Table 3A could be due to both variables being affected by party identification. To examine this possibility, you need to create Table 3B, a three-variable table that shows the relationship between attitude on Social Security spending, presidential vote, and party identification. To keep the table simple and to have a sufficient N (i.e., number of cases) for each column, you should recode the Social Security support variable (J01) so that it has just two categories (i.e., increased spending versus same or decreased spending), as there are too few voters who favored decreased spending to keep that as a separate category. Also, use the recoded version of party identification that you created for Exercise 1 (i.e., Democrats, independents, and Republicans) to keep the table simple and have a sufficient N in each column.

The Social Security spending variable (J01) has five response categories, running from Social Security spending should be increased a lot to Social Security spending should be decreased a lot. Keeping all five categories of J01 will create some problems when you generate a five-variable table in which party identification is a control variable.

There are not that many voters who favored decreased spending (by a lot or by a little), so the result would be a five-variable table in which there are too few respondents in some table columns, making the percentages in those columns unreliable estimates of population patterns. Recoding J01 so that it has only two categories—more spending (by a lot or a little) versus same or less spending (by a lot or a little)—will solve that problem.

Thus, you should recode J01 so that those who favor more spending (by a lot or a little) are in one category and those who favor either the same level of spending or less spending (by a lot or a little) are in a second category.

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 3B

If you ran Table 3B as suggested, you should have a table that consists of three sub-tables, one for each category of party identification. Each sub-table should have two columns and two rows. Each sub-table should have the recoded version of attitude toward Social Security spending (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 Social Security spending and presidential vote among Democrats? How about among independents? Among Republicans? Overall, what relationship exists between this attitude and voting?
  2. How does the relationship between attitude toward Social Security spending and presidential vote in each sub-table compare to the original relationship in Table 3A? Is it stronger, weaker, or about the same? What conclusion do you draw from this?

Your interpretation of Table 3B should have concluded that there was very little association between attitude toward Social Security spending and presidential vote once party identification is controlled for in the analysis. This is clear when we examine each sub-table:

  • Among Democrats, the percentage voting for Biden is just as high among those who do not favor more spending on Social Security as it is among those who want more.
  • Among independents, the percentage voting for Biden who favor more spending on Social Security is similar to those who do not.
  • Among Republicans, there is virtually no difference in the vote between those who want more spending on Social Security and those who do not.

This is an excellent example of a spurious association because the original relationship in Table 3A disappears in all three sub-tables.

In this example, the relationship between one’s attitude toward Social Security spending and presidential vote is extremely weak once we control for party identification. This tells us that the original relationship between the attitude and the vote is spurious.