## Introduction

The *Ordered Logit* is a form of regression analysis that models a discrete and ordinal dependent variable with more than two outcomes (Net promoter Score, Customer Satisfaction rating, etc.).

This article describes how to create an *Ordered Logit* Regression output as shown below. The example below is a model that predicts an overall satisfaction score based on satisfaction with specific features.

## Requirements

- An Ordinal
**Outcome**variable with at least three outcomes to be predicted. Ideally, an Ordinal-Categorical variable. When using stacked data the**Outcome**variable should be a single question in a Ordinal Categorical . - Continuous, categorical, or binary
**Predictors**variables will be considered as predictors of the outcome variable.

## Method

- Go to
**Create > Regression > Ordered Logit.** - In the
**object inspector**go to the**Inputs**tab. - In the
**Output**menu select the binary variable to be predicted by the*predictor variables.* - Select the predictor variable(s) from the
**Predictor(s)**list. - OPTIONAL: Select the desired
**Output**type:**Summary**: The default; as shown in the example above.**Detail**: Typical R output, some additional information compared to**Summary**, but without the pretty formatting.**ANOVA**: Analysis of variance table containing the results of Chi-squared likelihood ratio tests for each predictor.**Relative Importance Analysis**: The results of a relative importance analysis.**Effects Plot**Plots the relationship between each of the*Predictors*and the*Outcome*.

- OPTIONAL: Select the desired
**Missing Data**treatment. (See Missing Data Options). - OPTIONAL: Select
**Variable names**to display variable names in the output instead of labels. - OPTIONAL: Select
**Correction**. Choose between**None**(the default),**False Discovery Rate**,**Bonferroni**. - OPTIONAL: Specify the
**Automated outlier removal**percentage to remove possible outliers. - OPTIONAL: Select
**Stack data**to stack the input data prior to analysis. Stacking can be desirable when each individual in the data set has multiple cases and an aggregate model is desired.

## Next

How to do Latent Class Regression

How to Do Mixture Models for Regression