This article describes how to use the root likelihood (RLH) to identify respondents that appear to have randomly chosen options when answering a choice-based conjoint questionnaire. It then describes how to remove these respondents from further analyses.

## Requirements

A document containing a choice model.

## Method

- Fit a Hierarchical Bayes model. See How to do the Statistical Analysis of Choice-Based Conjoint Data. Preferably, leave out the
*Alternative*attribute by unchecking**MODEL > Alternative-specific constants**. - Save the RLH as a variable
**SAVE VARIABLES > RLH (Root Likelihood)**. Note that if the Hierarchical Bayes model has**Questions left out for cross-validation**, this step will create multiple RLH variables. Both of these variables may be used in this exercise, but it is recommended that you turn off cross-validation in this process. - Examine the distribution of the RLH statistic
- Select the variable created in the previous step.
- Create a histogram (
**Visualization > Distributions > Histogram**) and click and drag to draw the visualization.

- Duplicate the Hierarchical Bayes model in step 1. It is usually best to select the entire page in the
**Pages**tree and press**Duplicate**. - Set
**RESPONDENT DATA > Data Source**to**Simulate choices from priors**and**Simulated sample size**to the*sample size*. Provided that no priors have been set (as may have occurred if using an*efficient*or*partial profiles*design), this will simulate data under the assumption that people are randomly choosing alternatives. - Plot the distribution of the RLH for the simulated data:
- Insert a calculation
- Enter the code of

hist(choice.model.2$rlh)

- Replace
*choice.model.2*with the name of the choice model that contains the simulated choices.

- Create a calculation with code of

random.rlh.cutoff = quantile(choice.model.2$rlh, .95)

replacing*choice.model.2*with the name of the choice model and*.95*with some other percentile if you desire (0.95 will set the cutoff to the value that 95% of simulated respondents are below). - Create a filter variable as follows:
- Hover over a variable in the
**Data**tree, and press**+ > Custom Code > R Numeric** - Activate the cursor in the
**R CODE**block, click on the RLH variable and type in > random.rlh.cutoff (e.g., if your model is called*choice.model,*then the code will appear as:

`RLH from choice.model` > random.rlh.cutoff

**Label**:*Non-Random Choosers***Name**:*nonRandom*- Check
**Usable as a filter**

- Hover over a variable in the
- Duplicate the initial model, and apply the filter.

## See Also

How to Read Displayr's Choice Model Output

How to Remove Irrational Respondents from a Choice-Based Conjoint Model

How to Create an Experimental Design for Conjoint Analysis

How to Set Up a Choice-Based Conjoint Analysis in Qualtrics

How to Preview a Choice Model Questionnaire

How to Compare Discrete Choice Models

How to Create a Choice Model Utilities Plot

How to Save Utilities from a Choice Model

How to Save Class Membership from a Choice Model

How to Create a Choice Model Simulator

How to Create a Choice Model Optimizer