The Count per Capita (CpC) is used in MR-sets where the number of respondents and the number of replies are different.

For example, it allows us to calculate the average number of mentioned brands in the awareness questions, and then separately calculate the averages for women and men. As we can see from our example, men named 4.2 brands per capita, and women named 4.3 brands per capita.

These calculations are performed on the entire set of responses from the multi-response group, without isolating specific brands. However, we can further complicate the analysis by adding a filter to limit our pool of respondents to those who mentioned a specific brand with the interviewer’s prompt.

In our example below it is the Fresh Story brand. As you can see, there is no point in calculating the CpC for a single brand, as the number of responses for that brand exactly matches the number of respondents, resulting in a CpC of 1.0.

Another example of CpC usage is with a scale response. For example, we have a survey with 15 scale items, each representing a statement rated from 1 to 5. Thus, we get 15 responses from each respondent. Although we can easily count the total number of 4 and 5 scores, we need a CpC metric to determine the average number of 4 and 5 scores given by one person.