In a traditional choice-based conjoint analysis it is common to give people a "None of these" option. The more this option is chosen, the less information is collected about preferences for the attributes.

In practical terms, the sample size is reduced in proportion to the frequency with which the "None of these" option is chosen. A way to prevent the sample size from being reduced is to show the "None of these" option in the choice questions, and then follow up each question with a Yes/No question asking something like "Given what you know about this market, would you really have purchased this option?"

In this post, I describe how to analyze such data in Displayr.

 

Data files

Typically, you will have two files. One file will contain the experimental design (this is identical to a typical choice-based conjoint study). The other file contains the raw data. This raw data file will contain:

  • The choices that the respondents made from all the alternatives excluding the "None of these" questions (tasks).
  • A set of binary variables (Structure of Binary - Multi) indicating whether or not the respondent said they would purchase the chosen alternative in each of the choice questions. This is the only difference between the setup for a dual-response 'none of these' and a traditional choice-based conjoint study.
  • A single variable indicating the Version of the questionnaire seen by each respondent.

 

Setting up the experimental design

There are lots of different ways of setting up the experimental design. In this post, I assume you've got a spreadsheet or CSV file with a standard experimental design. The actual experimental design I've used is here if you wish to inspect it.

  • Insert the Hierarchical Bayes Choice Modeling analysis: Anything > Advanced Analysis > Choice Modeling > Hierarchical Bayes.
  • Ensure that the Design source is set correctly; in this example, to Data set
  • Set the Version and Task variables
  • Set the Attributes

In the example that I'm using, it looks like this:

 

Selecting the respondent data

The respondent data I've used in this post is here. In the RESPONDENT DATA section, select:

  • The variables that contain the respondents' choices as Choices
  • The variables that indicate the tasks shown in each choice question in Tasks; these correspond to the Task in the experimental design
  • The "None of these" variables in Dual-response 'none' choice

In the example used in this post, once selected, it should look like this:

 

Running and interpreting the model

To run the model, check the Automatic option at the top.

The resulting model has the same interpretation as the traditional conjoint model, which is to say:

  • The interpretation of the utilities for all the attributes other than the "None of these" alternative remains unchanged.
  • The interpretation of the "None of these" alternative remains problematic. With all conjoint models, regardless of whether dual-response or not, the utility of "None of these" is unlikely to be correct, which is why typically when choice models are analyzed this option is either left out of the simulations or calibrated in some way.

To see a Displayr document with this all set up, click here.

 

More information about this topic

Jeff D. Brazell, Christopher G. Diener, Ekaterina V. Karniouchina, and William L. Moore (2006), "The no-choice option and dual response choice designs" in Marketing Letters,  17(4):255-268 · February 2006.

Sawtooth software: https://www.sawtoothsoftware.com/help/lighthouse-studio/manual/hid_web_cbc_none.html