Political Analysis Homework

Description

Make sure you look and see if you are able to do it! Adjust accordingly if needed. IBM SPSS STATISTICS REQUIRED. Chapters 3-4

(Dataset: GSS. Variables: polviews, wtss.) The GSS dataset contains polviews, which measures political ideology—the extent to which individuals “think of themselves as liberal or conservative.” Here is how polviews is coded (the labels are fully written out):

NUMERIC CODE

VALUE LABEL

1

Extremely liberal

2

Liberal

3

Slightly liberal

4

Moderate

5

Slightly conservative

6

Conservative

7

Extremely conservative

Apply the Analyze ▶ Descriptive Statistics ▶ Frequencies procedure to polviews, making sure to weight the data using the wtss variable. Eyeball the percent column and make some rough-and-ready estimates.
The percentage of respondents who are either “extremely liberal,” “liberal,” or “slightly liberal” is (circle one)

about 18 percent.  about 28 percent.  about 38 percent.

The percentage of respondents who are either “slightly conservative,” “conservative,” or “extremely conservative” is (circle one)

about 10 percent.  about 20 percent.  about 30 percent.

Use polviews and the Transform ▶ Recode into Different Variables procedure to create a new variable named polview3. Give polview3 this label: “Ideology: 3 categories.” Collapse the three liberal codes into one category (coded 1 on polview3), put the moderates into their own category (coded 2 on polview3), and collapse the three conservative codes into one category (coded 3 on polview3). Don’t forget to recode missing values on polviews into missing values on polview3. Run Frequencies on polview3.
The percentage of respondents who are coded 1 on polview3 is (fill in the blank) _______________ percent.
The percentage of respondents who are coded 2 on polview3 is (fill in the blank) _______________ percent.
Make sure that the two percentages you wrote down in part B match the percentages you recorded in part A. The numbers may be slightly different and may still be considered a match. If the two sets of numbers match, proceed to part C. If they do not match, you performed the recode incorrectly. Review this chapter’s discussion of the Recode procedure and try the recode again.
In the Variable View of the Data Editor, change Decimals to 0, and then click in the Values cell and supply the appropriate labels for the numeric codes of the new polview3 variable: “Liberal” for code 1, “Moderate” for code 2, and “Conservative” for code 3. Run the Analyze ▶ Descriptive Statistics ▶ Frequencies procedure on polview3. Use your results to fill in the table below.
X
X
NO 2 QUESTION
(Dataset: NES. Variables: Who_2016, better_worse_past_econ, nesw.) What factors determine how people vote in presidential elections? Political scientists have investigated and debated this question for many years. A particularly powerful and elegant perspective emphasizes voters’ retrospective evaluations. According to this view, for example, voters who think the country’s economy has gotten better during the year preceding the election are likely to reward the candidate of the incumbent party. Voters who believe the economy has worsened, by contrast, are likely to punish the incumbent party by voting for the candidate of the party not currently in power. As political scientist V. O. Key once famously put it, the electorate plays the role of “rational god of vengeance and reward.”10 Does Key’s idea help explain how people voted in the 2016 election?
Test this hypothesis: In a comparison of individuals, those who thought the economy had improved during the year preceding the 2016 election were more likely to vote for the candidate of the incumbent party, Hillary Clinton, than were individuals who thought the economy had not improved. Apply the Analyze ▶ Descriptive Statistics ▶ Crosstabs procedure to obtain a cross-tabulation of the relationship between these two variables from the NES dataset: Who_2016 (dependent variable) and better_worse_past_econ (independent variable). Be sure to weight observations using nesw (weight variable) and request column percentages in the cross-tab cells. Record the percentages voting for Clinton and Trump in the table that follows.

DID THE ECONOMY GET BETTER/WORSE IN THE LAST YEAR?

Respondent’s vote, 2016

Much Better

Somewhat Better

About Same

Somewhat Worse

Much Worse

Total

Hillary Clinton

?

?

?

?

?

?

Donald Trump

?

?

?

?

?

?

Total

100.00%

100.00%

100.00%

100.00%

100.00%

100.00%

What do you think? Are the data consistent with V. O. Key’s retrospective evaluation hypothesis? Write a paragraph explaining your reasoning.
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Loss aversion is an interesting psychological phenomenon that can shape the choices people make.11 One idea behind loss aversion is that losses loom larger than commensurate gains. According to this theory, for example, the psychological pain felt from losing $100 is greater than the pleasure felt from gaining $100. Applied to retrospective voting, loss aversion might suggest that the “vengeance” impulse is stronger than the “reward” impulse—that the anti-incumbent party motivation among those who say the economy has worsened will be stronger than the pro-incumbent party motivation among those who think it has improved.
With this idea in mind, examine the percentages in the table in part A. What do you think? Do the data suggest that Key’s rational god of vengeance is stronger than his rational god of reward? Answer yes or no, and write a few sentences explaining your reasoning.