# Goodness-of-fit tests for Ordinal Logistic Regression

Find definitions and interpretation guidance for every statistic in the Goodness-of-fit tests table.

## Pearson goodness-of-fit test

The Pearson goodness-of-fit test assesses the discrepancy between the current model and the full model.

### Interpretation

Use the goodness-of-fit tests to determine whether the predicted probabilities deviate from the observed probabilities in a way that the multinomial distribution does not predict. The test is not useful when the number of distinct values is approximately equal to the number of observations, but the test is useful when you have multiple observations at the same values of the predictors. If the p-value for the goodness-of-fit test is lower than your chosen significance level, the predicted probabilities deviate from the observed probabilities in a way that the multinomial distribution does not predict. This list provides common reasons for the deviation:
• Omitted higher-order term for variables in the model
• Omitted predictor that is not in the model

If the deviation is statistically significant, you can try a different link function or change the terms in the model.

## Deviance goodness-of-fit test

The deviance goodness-of-fit test assesses the discrepancy between the current model and the full model.

### Interpretation

Use the goodness-of-fit tests to determine whether the predicted probabilities deviate from the observed probabilities in a way that the multinomial distribution does not predict. The test is not useful when the number of distinct values is approximately equal to the number of observations, but the test is useful when you have multiple observations at the same values of the predictors. If the p-value for the goodness-of-fit test is lower than your chosen significance level, the predicted probabilities deviate from the observed probabilities in a way that the multinomial distribution does not predict. This list provides common reasons for the deviation: