I'm trying decide on which discrete choice method to use? Do you have a table that provides a simple summary of the different modules available?
This page will summarize the different discrete choice methods used within Sawtooth Software tools. In addition, you may want to review the following resource:
Summary of Available Methods
- Conjoint Value Analysis (CVA) is the original (1970’s) traditional conjoint analysis (full profile, rating or ranking data on single or pairwise concepts)
- Adaptive Conjoint Analysis (ACA) is a computerized, adaptive conjoint analysis technique; very popular in the 80s and 90s
- Choice-based Conjoint (CBC) is currently the most popular technique which asks respondents to choose one of several product concepts (no ratings/rankings)
- Adaptive Choice-based Conjoint (ACBC) uses a build-your-own screening section together with a choice task tournament to identify products that are most likely to be first considered by respondent and then chosen
- Maximum Difference Item Scaling (MaxDiff) is roughly comparable to a one-attribute, multi-level CBC where respondents typically shown 2-6 items at a time, asked to indicate which is best and which is worst, and then the task is repeated many times showing a different set of items in each task
- Menu-based Choice (MBC) is an analysis tool that allows researchers to model complex decisions, such as restaurant menus, multi-drug prescriptions, telecom bundling, etc.
Which Conjoint Method Grid
| Method | Minimum Sample Size | Attributes | Levels | Pricing? | Complexity* | Fielding | Typical Use |
|---|---|---|---|---|---|---|---|
| CVA | Small | Typical: 6-7 Max: 30 |
Typical: 4-5 Max: 15 |
Yes, but limited | No | Paper, computer | Small attribute studies, situations where objective is to measure purchase likelihood or other direct scale elicitation, small sample size studies; may be used to generate generalized designs; situations where small, fixed design is required. |
| ACA | Small | Typical: 7-8 Max: 10 |
Typical: 5 Max: 15 |
Not recommended | No | Computer only | Large attribute studies; situations where objective is to measure purchase likelihood. |
| ACA 30 | Small | Typical: 12 Max: 30 |
Typical: 5 Max: 15 |
Not recommended | No | Computer only | Large attribute studies; situations where objective is to measure purchase likelihood. |
| CBC | Large | Typical: 6-7 Max: 10 |
Typical: 5 Max: 15 |
Yes | Yes | Paper, computer |
Competitive scenarios where choice is among multiple alternatives; pricing studies; chip allocation studies; fixed alternatives/competitors. |
| CBC ADM | Large | Typical: 6-7 Max: 250 |
Typical: 5 Max: 250 |
Yes | Yes | Paper, computer |
In addition to the CBC usage above, the Advanced Design Module allows you to conduct alternative-specific studies, partial-profile designs, and shelf-facing studies with up to 100 product concepts per screen. |
| ACBC | Small | Typical: Any Max: 100 |
Typical: Any Max: 250 |
Yes | Some | Computer only | Pricing studies; large number of attributes; focus is on finding best product; allow respondents or situation to determine which attributes/levels are shown. |
| MaxDiff 30 | Medium | No Attributes | Typical: 20-25 Max: 30 |
N/A | No | Paper, computer |
Lists of brands, positioning statements, specific product concepts, flavors, etc. |
| MaxDiff 2000 | Medium | No Attributes | Typical: 30-40 Max: 2000 |
N/A | No | Paper, computer |
Lists of brands, positioning statements, specific product concepts, flavors, etc. |
| MBC | Very Large | Typical: Any Max: 1000 |
Typical: Any Max: 1000 |
Yes | Yes | Paper, computer |
Multi-part decisions; complex models; bundling; mixed designs (CVA & CBC together). |
*Complexity, in this case, means whether or not the attributes can freely combine with other attributes.
Strengths and Weaknesses of the Different Methods
Conjoint Value Analysis (CVA)
- [Strength] Good for both product design and pricing issues
- [Strength] Can be administered on paper, computer/internet
- [Strength] Shows products in full-profile, which many argue mimics real-world
- [Weakness] Limited ability to study many attributes (more than about six results in respondent fatigue, long questionnaires)
- [Weakness] Limited ability to measure interactions and other higher-order effects (cross-effects)
Adaptive Conjoint Analysis (ACA)
- [Strength] Ability to measure many attributes, without wearing out respondent
- [Strength] Respondents find interview more interesting and engaging
- [Strength] Efficient interview: high ratio of information gained per respondent effort
- [Weakness] Partial-profile presentation less realistic than real world (respondents may not be able to assume attributes not shown are “held constant”)
- [Weakness] Often not good at pricing research (tends to understate importance of price, and within each respondent assumes all brands have equal price elasticities)
- [Weakness] Must be computer-administered (PC or Web)
Choice-Based Conjoint (CBC)
- [Strength] Easy for respondents to answer
- [Strength] Flexible designs (full or partial profile designs, alternative-specific designs including multiple fixed alternatives, chip allocation, shelf-facing designs, paper & pencil or computer administered)
- [Strength] Measure “None” alternative
- [Strength] Works well for measuring price
- [Weakness] Best to have large sample sizes (n=>200). If your sample size is smaller, you’ll need to keep your attribute list small and ask each respondent to answer more CBC tasks.
- [Weakness] Limited number of attributes – survey can become too cumbersome for respondents (but you can use partial profile designs)
- [Weakness] Not adaptive
Adaptive Choice-Based Conjoint (ACBC)
- [Strength] Many of benefits of CBC, but can be done with smaller sample size
- [Strength] Good choice if about 5 or more attributes
- [Strength] Works well for measuring price
- [Strength] Accommodates non-compensatory behavior
- [Strength] More attributes
- [Strength] More interaction leads to greater respondent involvement in survey
- [Weakness] Survey is often 2-3 times longer than a comparable CBC
- [Weakness] Currently no support for some CBC “goodies” (chip allocation, traditional none, etc.)
- [Weakness] More complex to program, analyze
- [Weakness] Must be administered on computer
- [Weakness] May be overkill for small-attribute studies (4 or fewer attributes)
Maximum Difference Item Scaling (MaxDiff)
- [Strength] Easy for respondents to answer
- [Strength] Fixed designs possible (supports paper-and-pencil interviewing)
- [Strength] Often better than standard rating or ranking exercises (ratings often end up in ties; rankings are difficult to manage with more than about 7 items)
- [Weakness] Best to have large sample sizes (n=>200)
- [Weakness] Resulting model is not additive (can’t add the score for one item to the score for another item to find the value of offering both)