- How much difference in their presidential voting is there between those who thought that Biden was honest and those who did not think so? Between which categories does the greatest difference in voting exist?
- How much difference in their presidential voting is there between those who thought that Trump was honest and those who did not think so? Between which categories does the greatest difference in voting exist?
- Is the relationship in Table 9C stronger, weaker, or about the same as the relationship in Table 9D?
Exercise 9: Candidate Character Traits and the Presidential Vote
Another reason for using three-variable tables in our data analysis is to examine the joint influence of two independent variables on a dependent variable. To illustrate this situation, we can consider the influence of how perceptions of candidate character traits influenced the presidential vote in 2020. Character traits were emphasized in the 2020 election campaign, partly because both candidates were defined by their opponent as dishonest. Trump was viewed as weak in integrity, as well as being too rash and impulsive; Biden received low marks on knowledge and integrity. Since integrity was a particular problem for Trump, it should be interesting to analyze how voters perceived the honesty of Biden and Trump.
Perceptions of Honesty
This dataset contains several items of information about the perceptions of the character traits of the two presidential candidates, including questions that asked the respondents whether or not they thought that Biden and Trump were honest (D04 and D08). These items asked respondents whether the phrase “he/she is honest” characterized each candidate extremely well, very well, moderately well, slightly well, or not well at all.
Step A. Create and interpret Tables 9A and 9B
To begin, you should create Frequency Tables 9A and 9B that show how voters perceived the honesty of the two candidates (D04 and D08). (Note: to create a frequency table for a single variable, enter the variable name in the box for the row variable and leave the column variable blank. Be sure to use the weighted data.)
These two tables show the frequency distribution of these two variables. Examine the tables and answer the following questions:
- What percentage of the respondents had a favorable view of Biden’s integrity (i.e., honesty)? What percentage had an unfavorable view? In answering this question, which categories of variable D04 would you consider to be a favorable assessment?
- What percentage of the respondents had a favorable view of Trump’s integrity? What percentage had an unfavorable view?
- How do the perceptions of Biden’s honesty compare with perceptions of Trump’s honesty? Was one candidate seen as more honest than the other?
You should have concluded that while neither candidate received particularly high marks for integrity, Biden had a clear advantage over Trump on this character trait.
Step B. Create and interpret Tables 9C and 9D
The next question is whether these perceptions of honesty influenced the vote. You should create Table 9C that shows how perceptions of Biden’s honesty (D04) were related to the vote. 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). After creating this table (Table 9C), create create Table 9D using the same question about Trump’s honesty (D08).
Step C. Create Table 9E
Tables 9C and 9D each indicate that perceptions of both Biden’s and Trump’s honesty were related to the vote. However, looking at each perception separately does not answer the most interesting question, which is what effect perceptions of differences between the candidates have on the vote. That is, some voters who thought that Biden was not honest may also have felt the same about Trump, in which case they did not see a difference between the candidates on this attribute. Other voters who thought that Biden was not honest may have felt that Trump was, and these voters clearly saw a difference between the candidates on this attribute. To examine the joint influence of D04 and D08 on the presidential vote, you should create Table 9E, a three-variable table. In this case, it does not matter whether D04 is treated as the independent variable and D08 as the control variable, or the reverse. In order to simplify the table, first recode D04 and D08 so that they have fewer categories.
If variables D04 and D08 are not recoded, using both variables together to predict the vote will produce a three-variable table containing five sub-tables, each with five columns, for a total of 25 columns. This would be a complex table that might be difficult to interpret, and some of the columns might have too few respondents in them, which would make the percentages unreliable estimates of the true figures. By recoding D04 and D08, you can produce a table that is simpler. To make your three-variable table as simple as possible, you could recode these two variables so that they each have just two categories: favorable and unfavorable.
In recoding variables D04 and D08, the responses “extremely well” and “very well” clearly could be considered to be favorable assessments of a candidate’s honesty. Similarly, “slightly well” and “not well at all” would seem to be unfavorable assessments. The response “moderately well” is somewhat ambiguous—it is clearly less positive than the first two responses, but more positive than the last two responses.
For some guidance on how this middle category is best recoded, we can look at the result of the previous tables that show how these variables are related to the presidential vote. Tables 9C and 9D show that voters who thought that the term honest characterized the candidate as moderately well were highly likely to vote for that candidate. In both tables, the difference in the vote between the “very well” column and the “moderately well” column is not that great. This suggests that we could recode the top three categories into one group, which is basically favorable in its assessment of the candidate.
An alternative would be to keep the “moderately well” category as a separate group, and we could do this, thereby recoding D04 and D08 into variables with three categories. However, to make this exercise as simple as possible, we recommend combining the top three categories together so that we have a simple dichotomous variable for the perception of each candidate’s honesty.
Step D. Interpret Table 9E
If you ran Table 9E as suggested, you should have a table that consists of two sub-tables. Each sub-table should have two columns and two rows. Both sub-tables should have the perception of Biden’s (or Trump’s) honesty (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 two sub-tables: one for each of the two categories of responses regarding Biden’s (or Trump’s) honesty. It does not make any difference whether you treated D04 as the independent variable and D08as the control variable or the reverse, given that both are really independent variables in this analysis. 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:
- Among those who thought that Biden was honest and Trump was not, what percentage of respondents voted for Biden? Among those who thought that Trump was honest and Biden was not, what percentage of respondents voted for Biden? These two groups are the voters who saw big difference between the candidates on this attribute.
- Among those who thought that both Biden and Trump were basically honest, what percentage of respondents voted for Biden? How about among those who thought that both were not honest? These groups are the voters who did not see much difference between the candidates on this attribute.
- How many voters were in each of the four possible combinations of views of Biden and Trump: (1) Biden honest and Trump not; (2) Trump honest and Biden not; (3) both not honest; (4) both honest?
- Is Table 9E superior to Tables 9C and 9D for examining how perceptions of honesty is related to the vote? What did you learn from Table 9E that you did not from Tables 9C and 9D?