# What is self-explicated conjoint analysis and why you should not use it

Posted on 18 April 2017

First of all, “self-explicated conjoint analysis” is not conjoint analysis. It is an inferior technique that attempts to present similar outputs as conjoint, but does that badly and should not be used. Conjoint.ly does not offer self-explicated conjoint and we recommend you steer clear from suppliers advocating this technique.

### What is the similarity with conjoint analysis?

Like normal conjoint, this technique assumes that people’s preferences for products are a sum of preferences for the different features (levels of attributes). Mathematically speaking, this model can be expressed as

where:

• $I_j$ is the importance of attribute $j$
• $D_{jk}$ is the desirability of the applicable level $k$ of attribute $j$,
• $P_c$ is the preference for product concept $c$.

### What is different?

Unlike in normal conjoint, where respondents see sets of carefully constructed questions about which product they would choose or buy, in self-explicated conjoint, respondents typically go through two questions:

1. All levels of all attributes are presented to the respondent and each level is evaluated for desirability (using a Likert scale or a 0 to 100 scale). Levels are often grouped by attribute.
2. To each respondent, the survey will show the most desirable level of every attribute (as reported by the respondent in the previous question). These levels are evaluated in a constant sum question to assign relative attribute importance scores.

### What is wrong with it?

This approach is proven to be bad. Theoretically, it is not supported by the random utility theory. Practically speaking, studies that involve self-explicated conjoint are producing wildly unreliable numbers (you may be better off using the RAND function in Excel). The reason is that they do not utilise three important properties of choice-based conjoint analysis:

• Conjoint is about asking people to “consider jointly”. In self-explicated conjoint, respondents are not given a chance to make trade-offs between products, they are only asked to view levels and attributes separately.
• In self-explicated conjoint, respondents are not making choices like they would in real life. Respondents are asked to self-assess the factors that drive their decisions, yet in reality, people are often unable to articulate what drives their behaviour (they might not know themselves or might be afraid to say).
• Self-explicated conjoint is not based on regression analysis, which allows to calculate confidence intervals and make sound market share simulations.

In conclusion, we recommend that self-explicated conjoint never be used. But if you are looking for quality analytics, you are already in the right place.