What is a Constant Sum Scale in Market Research?

A constant sum scale is a type of question used in a market research survey in which respondents are required to divide a specific number of points or percents as part of a total sum. The allocation of points are divided to detail the variance and weight of each category.

Constant sum scales are a less frequently used question in surveys when compared to basic likert scales, single radio responses, or checklists (i.e. multiple response options). They are an excellent way to create variance among a data set and truly understand which factors are key and which are not for customers or respondents.

They are especially helpful if you need to ask a question to a customer or respondent where you believe several factors are critical or of high importance. You are more likely to create differentiation in the data with a constant sum when compared to other question types.

Let's dive into this a little deeper.

What is a Constant Sum Scale in Market Research?

Our Market Research 101 series discusses the basics of surveys and other common methods used in the industry. Try reading our Market Research 101 category for more information.


Example of a Single Response Question

The best way to explain how this works is to walk you through an example of a constant sum scale question in a survey. This is a theoretical question. Let's say you want to understand what factor(s) matter most to a consumer when purchasing a home. There are several ways you can ask this.

One option is a single response question:

Q: What one factor is most important to you when buying a home? Select one.

A: Price, Location, School District, Inside Features, etc.

You are a likely to get a mix of different responses here and your results may look like: price (40%), location (25%), school district (10%), inside features, (5%), etc. However you do lack some context as to exactly how much more important price is compared to others. For some price may be very important but for others it is not. However, a single response is likely to create more differentiation than a multiple response or likert scale option.


Example of a Multiple Response Option

Next, let's try to ask the question as a checklist or multiple response option. This would be asked as follows.

Q: What factor(s) are most important to you when buying a home? Select all that apply.

A: Price, Location, School District, Inside Features, etc.

Here, if all of the categories are deemed very important by the respondent you may end up with response breakdowns like price (96%), location (93%), school district (85%), inside features (83%), etc. This creates even less variance among responses making it more difficult to differentiate.


Example of a Likert Scale

As a third option, you may want to ask this question using a 1 to 5 or 1 to 10 scale to ask respondents to rate importance of each factor.

Q: Using a scale of 1 to 5 where "1" is not at all important and "5" is very important, how important are each of the following when buying a home? Select a rating for each.

A: Price, Location, School District, Inside Features, etc. with 1 to 5 scale.

For the analysis you can run a top-2 box breakdown which tallies up those who rated each a "4" or "5" to provide percentages for each category.

The other option is creating a mean score for each which may look like: price (4.9), location (4.8), school district (4.4), etc. Again, the variance is not strong here so it is difficult to interpret. The key takeaway may be all of these factors are important.


Example of a Constant Sum Scale Option

Finally, let's give an example of the constant sum scale question. This is asked in the following manner in your survey. It forces the respondent to slow down a bit and think about how important each factor is as they allot points.

Q: Using 100 points, please apply a number of points to each factor based on how important each are to you when buying a home. You must total 100 points divided among the factors.

A: Price, Location, School District, Inside Features, etc.

The respondent is given 100 points. They may choose to apply 80 to price, 15 to location, and spread out the remaining 5 points among other factors. When you analyze this data set, the differentiation between factors becomes evident. Most survey software will automatically tally and sum the point values to ensure they add to a constant sum of 100.


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