POLI380 Term 2 Assignment 2 (2022 Spring)
Question 1: [4 points]
- Use your census dataset sample to estimate the NUMBER (not the percentage) of people who live in British Columbia at the time of the census. So first find the proportion of British Columbia residents then turn it into a number of people. Assume the total population of Canada is exactly 35 million.
- Indicate how far away from the true number of residents of British Columbia who are (of whatever categorical variable you choose) you would expect to be, 19 times out of 20. Mention this: “Say: ± ______ number of people. (NOT ± %) You calculate this using the formula for the standard error of the mean (or the easier one for a proportion) and then use that percentage to calculate the number, as you will have done in question 1a.”
Question 2: [5 points]
Find the average total income of people who live in British Columbia, excluding all missing values on income. Do not recode values that are $0 or negative. Do the same for people who live in in Ontario. Report your comparison of these two means in a smoothly worded paragraph that summarizes the findings for a reader. Be sure to report your uncertainty about these sample estimates in the standard way, with a report of the margin of error 19 times out of 20.
Question 3: [5 points]
Using the variables immstat and kol, report the results from a crosstabulation to smoothly answer the question of whether the knowledge of official languages is related to immigration status. (Recode the immstat variable so that “Not Available” and “Non-permanent resident” are set to missing if you have those categories in your data.)
For this question you do not need to mention margins of error or p-values or anything.
Now switch to the American National Election Study. The file is “anes_timeseries_2020_for 380.dta”
DO THIS FIRST: You need to draw a random sample of 1500 cases from the dataset. That way you’ll all get different samples that I can have my computer replicate.
First, set the random number seed by typing: set seed studentnumber (where you replace studentnumber with your numeric student number).
Use the command sample: sample 1500, count. (If you do not include “count” in your command, Stata thinks you want 1500% of your sample and won’t be able to do anything.)
Now use the separate command count to double check that you now have 1500 cases to work with:
type count in the command window
Stata should simply report 1500. (If it’s close, it’s ok).
Question 4: [6 points]
In this question you will answer the question of whether attitudes towards federal budget spending is associated with party identification.
So first create an index of attitudes on federal budget spending using only three variables that focus on 1) social security, 2) welfare programs, and 3) aid to the poor. Use only the pre-election variables and use the summary variables (the ones ending with x).
If you have difficulty finding them, you can always use the lookfor command, e.g.: lookfor budget
4a. Paste in the Stata commands from this part of your do-file so we can see how you created the federal budget spending index. Then paste in the tabulation of your new variable. (e.g. tabulate my_var).
4b. Using the party id variable (V201228) summarize the distribution of your spending index for people who identify as Democrats compared to those who identify as Republicans. Your answer should explain what the spending variable measures, the range and meaning of the numeric values, the mean and standard deviation of the variable and anything interesting about the shape of the distributions. Do not paste any Stata results in this answer. Write it up as it would appear in a newspaper or political analysis blog.
Question 5. [5 points]
Use the Quality of Government dataset. Do the same thing as you did at the start of question 4.
First, set the random number seed by typing: set seed studentnumber (where you replace studentnumber with your numeric student number). Use the command sample: sample 170, count. Note that you are selecting 170 cases, not 1,500 as you did in Question 4.
Use the variable “bmr_dem” and “wdi_afp”. Run t-test of the difference in mean wdi_afp among countries that have a 1 or 0 for bmr_dem. Do this only for countries in sub-Saharan Africa (use ht_region and an if statement).
Report the two means, their difference, and the p-value from the t-test. Do all that in a nice smooth, informative paragraph where you start by explaining in a simple way what the research question is, then how the variable is measured/calculated, what the results are, and what they tell us. You will need to consult the codebook to understand how the variables are measured and what concepts they are intended to measure.
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