Task: It is your third day of your internship at the campaign of a candidate for Congress. Your supervisor wants a new analysis of public opinion on government spending. She asks you to look at the 2016 GSS again, but this time she wants you to focus on one of the government spending variables. She wants you to use contingency tables and/or bar charts to examine how three independent variables (personal characteristics) affect attitudes toward whether we should be spending more or less on that item.
· Choose a government spending item (any of the variables that start with nat).
· Create a new version of the variable and recode it in a way that you like.
o Turn any “Don’t Know” or “No Answer” responses into missing values (remember that Stata uses a period “.” to represent a missing value).
o If you recode this variable into a dummy variable to create a bar chart, code it 0 for No and 100 for Yes. (You can make either “too much” or “too little” your Yes.)
· Choose three independent variables that you think might affect attitudes toward spending on this item. Obvious choices include sex, race, age, education (educ), religion (use relig), religious attendance (attend), party identification (partyid), and liberalism-conservatism (polviews). Remember to make sure that you understand how each variable is coded; you may need to recode it.
· First, describe public opinion for the data set as a whole.
· Second, create at least two contingency tables. Be sure you know whether your independent variable is the column variable or the row variable.
o If your independent variable is the column variable, have Stata print only the column percentages. (Use the nofreq option.) When writing up your description of the table, compare column percentages in the top or bottom row.
If your independent variable is the row variable, have Stata print only the row percentages. (Use the nofreq option.) When writing up your
