Dual negative-positive scale


Posted on 30 October 2018


Respondents in different countries use survey scales differently. Academics have identified several country-specific peculiarities. Key among them is the degree of “acquiescence bias”, i.e., the extent to which respondents are biased to agree with the presented statement. For example, for a very comparable product, audience, and real market performance of a product, in China a Likert scale result may be 4.5 (on a scale of 1 to 5), while in Argentina it will be 3.5.

To make results more comparable across the countries, Conjoint.ly Claims Test employs a well-known but underutilised technique: a dual positive-negative scale. Specifically, we ask the question in two ways (each respondent will see only one of these questions). For example:

  • Give it this statement 👍👍👍👍👍 if it is good value for money.
  • Give it this statement 👎👎👎👎👎 if it is not a good value for money.

When respondents are presented with the positive question, they can rate the claim from 👍 (lowest) if they feel that the claim is not a good value for money to them and 👍👍👍👍👍 (highest) if they feel that the claim is a good value for money to them. These responses will be recorded as 1 to 5.

Conversely, when respondents are presented with the negative question, they can rate the claim from 👎 (lowest) to 👎👎👎👎👎 (highest).

How do we calculate results

To obtain the diagnostic results that you see in the report, we simply use the following formula:

Overall Score = Mean of positive questions - Mean of negative questions


As such, the Overall Score for a dual positive-negative scale could go from -4 to 4, where an Overall Score of 4 could be obtained when the Mean of positive questions is the absolute highest (coded as 5) and the Mean of negative questions is the absolute lowest (coded as 1), vice versa.

However, very few values would be outside the range of -3 to +3. Hence the colour coding stops outside this range:

-4.0 -3.5 -3.0 -2.5 -2.0 -1.5 -1.0 -0.5 0.0 0.5 1.0 1.5 2.0 2.5 3.0 3.5 4.0

The above legend for diagnostics was obtained and refined from our past studies and pilot projects with our FMCG clients and, from our experience, can be applied to most types of consumer or B2B products.