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JTRAIN

JTRAIN

JTRAIN installed course data for Module 7 program indicators and training-policy 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 JTRAIN from the dataset link first.
data = pd.read_stata("JTRAIN.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

lscrapnumeric

lscrap used for Module 7 program indicators and training-policy comparisons.

Missing values: Review source documentation before recoding missing values.

grantnumeric

grant used for Module 7 program indicators and training-policy comparisons.

Missing values: Review source documentation before recoding missing values.

lemploynumeric

lemploy used for Module 7 program indicators and training-policy comparisons.

Missing values: Review source documentation before recoding missing values.

hrsempnumeric

hrsemp used for Module 7 program indicators and training-policy comparisons.

Missing values: Review source documentation before recoding missing values.

salesnumeric

sales used for Module 7 program indicators and training-policy comparisons.

Missing values: Review source documentation before recoding missing values.

employnumeric

employ used for Module 7 program indicators and training-policy comparisons.

Missing values: Review source documentation before recoding missing values.

Practice tasks

  1. 1.JTRAIN is catalogued for Module 7 program indicators and training-policy 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.