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LAWSCH85

LAWSCH85

LAWSCH85 installed course data for Module 7 institutional categories and school outcome comparisons.

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Structure

cross-sectional

File type

DTA

Module

Course catalog

Visibility

public

Preview table

Preview rows are shown when safely installed in the app. External or licensed files may need to be opened from the source link.

No local preview table is installed for this dataset. Use the download/source link, then load it with the Python example below.

Python loading code

import pandas as pd

# Download LAWSCH85 from the dataset link first.
data = pd.read_stata("LAWSCH85.DTA")
print(data.head())

Check the variable dictionary and source documentation before dropping or recoding missing values.

Dataset AI assistant

Get help with variables, graphs, regression questions, limitations, and Python loading code.

Variables dictionary

salarynumeric

salary used for Module 7 institutional categories and school outcome comparisons.

Missing values: Review source documentation before recoding missing values.

LSATnumeric

LSAT used for Module 7 institutional categories and school outcome comparisons.

Missing values: Review source documentation before recoding missing values.

GPAnumeric

GPA used for Module 7 institutional categories and school outcome comparisons.

Missing values: Review source documentation before recoding missing values.

libvolnumeric

libvol used for Module 7 institutional categories and school outcome comparisons.

Missing values: Review source documentation before recoding missing values.

costnumeric

cost used for Module 7 institutional categories and school outcome comparisons.

Missing values: Review source documentation before recoding missing values.

ranknumeric

rank used for Module 7 institutional categories and school outcome comparisons.

Missing values: Review source documentation before recoding missing values.

Practice tasks

  1. 1.LAWSCH85 is catalogued for Module 7 institutional categories and school outcome comparisons. Installed original synthetic CSV; estimates are practice results, not real empirical findings.
  2. 2.Create one summary table and one graph that support an econometric question.
  3. 3.Write one limitation about measurement, missing data, or omitted variables.