ordinalcorr 0.7.1

correlation coefficients for ordinal-scaled variables

ordinalcorr: correlation coefficients for ordinal variables

ordinalcorr is a Python package designed to compute correlation coefficients tailored for ordinal-scale data (e.g., Likert items). It supports polychoric correlation coefficients and other coefficients for ordinal data.

✨ Features

1️⃣ Correlation Coefficients

This package provides several correlation coefficients (e.g. Polyserial and Polychoric)

Variable X	Variable Y	Method	Function
continuous	ordinal (discretized)	Polyserial correlation	polyserial
ordinal (discretized)	ordinal (discretized)	Polychoric correlation	polychoric

Here is an example:

>>> from ordinalcorr import polychoric
>>> x = [1, 1, 2, 2, 3, 3]
>>> y = [0, 0, 0, 1, 1, 1]
>>> polychoric(x, y)
0.9986287922233864

2️⃣ Heterogeneous Correlation Matrix

A function for computing the heterogeneous correlation matrix—a correlation matrix that includes both continuous and ordinal variables—is also provided.

>>> from ordinalcorr import hetcor
>>> import pandas as pd
>>> data = pd.DataFrame({
...     "continuous": [0.1, 0.1, 0.2, 0.2, 0.3, 0.3],
...     "dichotomous": [0, 0, 0, 1, 1, 1],
...     "polytomous": [1, 1, 3, 3, 2, 2],
... })
>>> hetcor(data)
             continuous  dichotomous  polytomous
continuous     1.000000     0.989335    0.514870
dichotomous    0.989335     1.000000    0.549231
polytomous     0.514870     0.549231    1.000000

📦 Installation

ordinalcorr is available at the PyPI

pip install ordinalcorr

Requirements

• Python 3.10 or later
• Dependencies:
  • numpy >= 1.23.0
  • scipy >= 1.8.0

📒 Documentation

ordinalcorr documentation

⚖️ License

This project is licensed under the MIT License. See the LICENSE file for details.