```{toctree}
:maxdepth: 2
:caption: Contents:

```

# Method Selection Guide

The following table shows which correlation method to use based on your variable types:

| Variable X                 | Variable Y                 | Method                                  |
| --------------------------- | --------------------------- | ---------------------------------------- |
| dichotomous (discretized)   | dichotomous (discretized)   | {py:func}`ordinalcorr.tetrachoric`        |
| polytomous (discretized)    | polytomous (discretized)    | {py:func}`ordinalcorr.polychoric`         |
| continuous                  | polytomous (discretized)     | {py:func}`ordinalcorr.polyserial`         |
| continuous                  | dichotomous (discretized)    | {py:func}`ordinalcorr.biserial`           |
| continuous                  | dichotomous                  | {py:func}`ordinalcorr.point_biserial`     |

Where:

- **dichotomous** variable: An ordinal variable with exactly two categories (e.g., Yes/No, 0/1).
- **polytomous** variable: An ordinal variable with more than two categories (e.g., Likert scale with 5 options).
- **discretized**: Indicates that the variable is assumed to originate from an underlying continuous latent distribution, and that observed categories result from applying thresholds to this latent variable.