Editing Statistics and Statistical Programming (Fall 2020)/pset8
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== Part II: Analyze and interpret a simulated study of education and income == | == Part II: Analyze and interpret a simulated study of education and income == | ||
The second part of this problem set | The second part of this problem set pose an open-ended set of questions about a simulated dataset from an observational study of high school graduates' academic achievement and subsequent income. Here is some information about the "study design" ('''note: this is not data from an actual study'''): | ||
:: | :: Data from twelve cohorts of public high school students was collected from across the Chicago suburbs. Each cohort incorporates a random sample of 142 students from a single suburban school district. For each student, researchers gathered a standardized measure of the students' aggregate GPA as a proxy for their academic achievement. The researchers then matched the students' names against IRS records five years later and collected each student's reported pre-tax earnings for that year. | ||
I have provided you with a version of the dataset from this hypothetical study in which each row corresponds to one student. For each student, the dataset contains the following variables: | I have provided you with a version of the dataset from this hypothetical study in which each row corresponds to one student. For each student, the dataset contains the following variables: | ||
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For the rest of this programming challenge, you should use this dataset to answer the following research questions: | For the rest of this programming challenge, you should use this dataset to answer the following research questions: | ||
* How does high school academic achievement relate to earnings? | * How does high school academic achievement relate to earnings? | ||
* | * Does this relationship vary by school district? | ||
You may use any analytical procedures you deem appropriate given the study design | You may use any analytical procedures you deem appropriate given the structure of the dataset and study design. Some things you may want to keep in mind: | ||
* | * ANOVAs, T-tests, and linear regression might help you test different kinds of hypotheses. | ||
* Adjusting for multiple comparisons is important. | |||
== Part III: Trick-or-treating all over again == | == Part III: Trick-or-treating all over again == |