SmithWagnerCV 0.1.0

Produces critical values for value-added learning scores proposed in Smith and Wagner (2018) through Monte Carlo simulations.

SmithWagnerCV

This module produces critical values for the disaggregated learning types as described in Smith and Wagner (2018) and Smith and White (2021).

Examples

Run a Monte Carlo Simulation of mu value of 0.1 and 25 students.

from SmithWagnerCV import RunSimulation

d = RunSimulation(25, 0.1)

Simulate all combinations of [10,20] students and [0.1,0.5] mu values and return them as a dictionary

from SmithWagnerCV import SimulationTable

d = SimulationTable([10,20], [0.1,0.5])

Simulate all combinations of [10,20] students and [0.1,0.5] mu values and save them to CSV files

from SmithWagnerCV import SaveSimulationTable 

d = SaveSimulationTable([10,20], [0.1,0.5])

Installation

Using the pip tool, you can install this module with the following command:

pip install SmithWagnerCV

Using the conda command you can type the following:

conda install -c tazzben smithwagnercv