## Introduction

This article describes how to go from a Latent Class Analysis, Hierarchical Bayes or Ensemble Choice Model output created in Q:

To a numeric variables containing respondent-level utilities derived from the Choice Model for each attribute level:

## Requirements

- A Q project containing a Latent Class Analysis, Hierarchical Bayes or Ensemble Choice Model output created in Q.

## Method

1. Select the Choice Model output in your document.

2. From the toolbar menu, select Automate** > Browse Online Library > Choice Modeling > Save Variable(s) **and select one of the following utilities options based on your preferred scaling:

- Utilities (Mean 0) - individual-level coefficients shifted so that for each individual and attribute the mean utility across the levels is zero.
- Utilities (Mean 0, Max Range 100) - individual-level coefficients scaled and shifted so that, for each individual and attribute, the lowest utility of any level is zero and the greatest utility of any level is 100.
- Utilities (Mean 0, Mean Range 100) - individual-level coefficients shifted so that, for each individual and attribute, the mean utility across the levels is zero. Utilities are then all multiplied by a scaling factor per individual, so that the average range of utilities per individual across the levels of each attribute is 100.
- Utilities (Min 0) - individual-level coefficients shifted so that, for each individual and attribute, the lowest utility of any level is zero.
- Utilities (Min 0, Max Range 100) - individual-level coefficients shifted so that, for each individual and attribute, the mean utility across the levels is zero. Utilities are then all multiplied by a scaling factor per individual, so that the maximum range of utilities per individual across the levels of any attribute is 100.
- Utilities (Min 0, Mean Range 100) - individual-level coefficients shifted so that, for each individual and attribute, the lowest utility of any level is zero. Utilities are then all multiplied by a scaling factor per individual, so that the average range of utilities per individual across the levels of each attribute is 100.

Individual-level coefficients are not able to be saved to variables for models with simulated data and models created using a CHO data file where respondent IDs were not specified.

A new variable is added containing the utilities for each each attribute level as shown in the Raw Data table below:

## Next

How to Do Choice Modeling in Q

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