How to Create an Experimental Design for Choice-Based Conjoint

This article describes how to design experiments for choice-based conjoint analysis (also known as choice modeling).

Requirements

A list of brand attributes and levels for each attribute.

Method

1. Select Anything > Advanced Analysis > Choice Modeling > Experimental Design.

2. From the inputs on the right. select an Algorithm for creating the design. The default is Balanced overlap which generates a design where there is a high level of balance for each respondent. Algorithms options include:

Random - Randomly chooses levels, only ensuring alternatives are not identical within a question.
Shortcut - The design is built with each alternative consisting of the least frequently used Level for each attribute. If levels are equally frequent, the least used level within the question is selected, or else a random choice is made.
Complete enumeration - For each alternative, every possible alternative is evaluated and the one with the lowest cost selected. The cost of an alternative depends on its incremental impact upon the design in terms of a combination of single level balance, pairwise level balance and level overlap within questions.
Balanced overlap - As per Complete enumeration except level overlaps are less strongly penalized.
Efficient - The design is chosen using a recent algorithm to optimize the D-error, so that the variance of the model parameter estimates is minimized.
Partial profiles - Uses the same algorithm as Efficient except that designs generated with this algorithm can have a specified number of attributes set constant.
Alternative specific - Random - Creates a design with attributes that are specific to each alternative. Random levels are chosen for each attribute.
Alternative specific - Federov - Creates a design with attributes that are specific to each alternative. The design is optimized to maximize the information about the model parameters, given the responses.

3. Enter the number of Questions per respondent.

4. Enter the number of different design Versions that you want to generate.

5. Only check the Alternative are labeled by first attribute checkbox if your the first attribute in your list is an alternative label.

6. Enter the number of Alternatives per question (excluding None(s)) that respondents will be shown.

7. Enter the number of None alternatives.

8. For Attributes and levels, select one of the following options

Enter in spreadsheet - enter the attributes and levels through a spreadsheet-style data editor with one attribute per column followed by each attribute level by clicking the Add attributes and levels button.
Enter attributes individually - enter each attribute name followed by a list of levels, delimited by commas.
Enter number of levels per attribute - A comma delimited list of levels per attribute.

9. For Efficient and Partial profiles designs that use the Attributes and levels spreadsheet option, you can additionally add mean and/or standard deviation priors to optimize your design by placing a column called mean or sd adjacent to each attribute you wish to apply a prior to. Best practice is to do this with all attributes. Priors are designed to reflect the utilities of the survey respondents whereby a high utility for an attribute level indicates a stronger preference and vice versa. The first utility should be 0. The range should be between -3 and 3 relative to the first utility based on your industry knowledge and judgment.

10. Tick the Enter prohibited alternatives checkbox if there are combinations of attribute levels that you want to prohibit from appearing in the same choice option. Note that the alternatives that you want to prohibit from appearing together must be on the same row with the attribute levels in the same column as entered in the original attributes and levels spreadsheet.

11. Enter the expected Sample size for the experiment. The default is 300, however a warning may be generated, similar to the one below, recommending you increase the sample size if the standard error for any of the attribute levels is greater than 0.05.