Dataset: GSS2018_Lab 3
Codebook/variable list: GSS variable search
1. Remember in Lab 1 when we looked at voting participation, news attention, and trust in institutions? Let’s run the analyses to see if the general patterns we identified are significant or not! Using voting participation in the 2016 (VOTE16) elections, compare the frequency of reading newspaper (NEWS), trust in private companies (TRBIGBUS), trust in the court system (TRCOURTS), and views about placing too much trust in science (TRUSTSCI) across voters and non-voters.
a. Run the appropriate t-tests, pasting the relevant syntax below.
b. Report the results of the t-tests along with the relevant means in a single table.
c. Report your findings, addressing any assumption checks as well. Do voters and non-voters in the 2016 election differ in terms of their news attention and trust in institutions? Don’t forget to include the t-statistic, degrees of freedom, and p-value.
2. We want to know if people with different incomes have different topics interests. Specifically, we want to look at whether high and low family income respondents (CONINC) have different levels of interest in economic issues (INTECON), international issues (INTINTL), space exploration (INTSPACE), and technologies (INTTECH).
a. First, check the family income variable. Is it appropriately structured to perform a t-test? Why or why not?
b. If no, implement the necessary fix and paste the syntax below. Why did you decide on that particular fix?
c. Run the appropriate t-tests, pasting the relevant syntax below.
d. Pick two of the issues most interesting to you and report your findings (addressing any assumption checks). Does interest in the issues vary by income level?
3. We want to know about visiting patterns for natural history museums (VISNHIST), science museums (VISSCI), and zoos (VISZOO). Specifically, we want to know if people visit natural history museums more or less than they visit 1) science museums or 2) zoos.
a. Run the appropriate t-tests, pasting the relevant syntax below.
b. Report the results of the t-tests along with the relevant means in a table(s).
c. Report your findings, including the relevant statistics and any assumption checks.
Dataset: Experiment_Lab 3
Codebook/variable list: see variable viewer
Description: experiment with control group and warning message; issue topic: vaccines
Scenario
We conducted a messaging experiment testing warning messages about potential side effects of vaccines. We included a control group who only received basic information about what vaccines are and a warning message group who received the basic information plus a warning that side effects can be severe in a small portion of the population. We measured support for vaccines before (SupportVaccine) and after (PostSupportVaccine) exposure to the experimental treatments (ExpCondition).
4. Now that we have our data, we want to analyze whether the warning message impacted people’s support for vaccines.
a. Identify the appropriate t-test to run and explain your choice.
b. Check any necessary assumptions and report the findings here.
c. Run the appropriate t-tests, pasting the relevant syntax below.
d. Report the results of the t-tests along with the relevant means in a single table.
e. Summarize your findings. Did the warning message impact support for vaccines?
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