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.

Survey writing is a science in market research. With any given objective there may be 10, 20, or 30+ ways to ask the question. Part of the expertise in survey writing comes from using the right question format to best address an objective. It doesn't always involve picking the easiest yes or no option, or scale. There are more complex ways to address survey topics.

This is the main reason why it is so important to lean on someone or a firm in the industry who has a deep background in survey writing and market research. The outcomes or results from a question are often unknown. Unfortunately, when you are reviewing the analysis it is too late to come to the determination you should have asked the question differently.

This is not a spot you want to be in as a brand commissioning marketing research in-house. The expertise of a market research company includes foresight into how results will pan out for analysis including how the results can be interpreted and action can be taken. It is an invaluable service and although anyone thinks they can write a survey question, there are much deeper levels of understanding that help formulate the best possible question and outcome for your project.

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 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.

But there is always a risk that each option comes back with an equal share within the margin of error. The results may appear as price (20%), location (20%), school district (20%), inside features (20%), etc. It is near impossible to for anyone to understand which of these choices may be more or less important than the other.


Example of a Multiple Response Option

Next, let's try to ask the question as a checklist or multiple response options. 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.

Similar to the single response question, this also runs the risk of diluting the analysis by respondents seemingly selecting everything as important.


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 the 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 is 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.

This constant sum scale adds another layer of analytical thinking for the respondent rather than just selecting one, running through a checklist of choices, or selecting from a grid or scaling question. It forces respondents to slow down and understand the relative value of each factor and compare the importance of one over another. It maximizes the chances of creating differentiation between your choices.

Always consult with a market research or survey design expert when undertaking new projects. These analysts and firms can point you in the right direction or even design the project from end-to-end for you.


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