DSS 012Y Data Visualization for the Social Sciences California Bar Examination

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DSS 012Y Data Visualization for the Social Sciences

Optional Take Home Final

California Bar Examination

Background:

The State Bar of California licenses and regulates attorneys. In order to practice law, one must pass the California Bar Examination. In October 2020 8,723 people took the general bar exam.

Law schools prepare people to take the bar. Some law schools are approved by the American Bar Association (ABA). Some schools are accredited even though they are not ABA approved. Some schools are unaccredited.

The exam is scored out of 2000 points. A score of 1390 or higher is passing. A score below 1390 is a failing score. If a person fails on their first attempt, they can repeat the exam.

This is a random sample of 700 of the 8,723 records.

Data

Variables:

Name                                        Notes

Participant

Age

School Type

Attempt

Score

Randomly assigned participant number

In years

ABA Approved, Accredited, Unaccredited First time exam taker or repeated              Out of 2000 points

Assignment:

Identify a question and answer it. Find an interesting story to tell from this data.

•    This is an individual assignment. Please do your own work.

•     No late work accepted

•      Elements of a Write Up

•     Format Details - limit to 3 pages, embed images of your graphs/tables, double-spaced.

•    Visualizations - include exactly 3 graphs or tables in your write up

Potential Questions:

What variables predict score on the bar exam?

What variables predict passing or failing the bar exam?

Do the different school types have different pass rates?

Are students more likely to pass the exam on the first attempt or on a repeated attempt? Does age predict score on the bar exam?

Lab Notes (Hints) :

•   You do not need to include all of the records.

•   You should not consider using all of the variables. Choose which variables help you to   tell a coherent story. Narrow down the data set in order to address a specific question.

•   You might consider transforming the data (e.g., transform the raw scores into “pass” and “fail”)

•    Be sure that you are grouping the data by predictor variables, not outcome variables.

•   Be sure to include overall descriptions of the variables that you choose (percentages or averages).


2023-03-22