Comparative scales involve the direct comparison of stimulus objects. Most often, the respondent is asked to compare one brand, product, or feature against another. Comparative scale data must be interpreted in relative terms and have only ordinal or rank order properties.
This guide will teach you different types of Comparative scales:
1. Paired comparison
Sometimes marketing researchers wish to find out which are the most important factors in determining the demand for a product or which are the most important factors acting to prevent the widespread adoption of a product.
For example, an ice cream company discovers after some initial research and observation that there are five main reasons why people don't buy their product:
- Too much sugar
- Not enough flavor
- Too expensive
- Doesn't look tasty
- Ugly packaging
What if the company wants to know which factors are most important in the consumer's mind?
It may well be the case that improving those factors will boost widespread sales! This is where the paired comparison comes in very handy and is a way better alternative than to abandon the product's development due to lacking sales or to completely re-design it which, is time-consuming and expensive.
With paired comparison, the objections are ordered from most to least important by pairing each objection by the researcher so that with 5 factors, as in this example, there are 10 pairs. Since every factor has to be paired with every other factor in turn. However, only one pair is ever put to the consumer at any one time.
The question might be put as follows:
Which of the following was the more important in making you decide not to buy the ice cream?
"The ice cream contains too much sugar" or "It's too expensive."
He/she then indicates which of the two was the more important in the questionnaire.
The question is repeated with the second set of factors, and the appropriate box ticked again. This process continues until all possible combinations are exhausted, in this case, 10 pairs.
It is good practice to make sure that any particular factor is sometimes the first of the pair and sometimes the second. The researcher would never, for example, take the first factor ("Too much sugar") and systematically compare it to each of the others in succession. That is likely to cause systematic bias.
Below, labels have been given to the factors so that the example will be easier to understand. The letters A - E have been allocated as follows:
Too much sugar
Not enough flavor
Doesn't look tasty
The data is then arranged into a matrix. Assume that they interviewed 200 ice cream lovers and have their responses arranged in the grid below. The matrix is read from top to side. For example, 154 out of 200 respondents said that the price of the ice cream was a greater deterrent than the flavor.
If the grid is carefully read, it can be seen that the rank order of the factors is ranked from most important to least important:
- Ugly packaging (E)
- Doesn't look tasty (D)
- Too expensive (C)
- Not enough flavor (B)
- Too much sugar (A)
This means that it is more important for designers to change the packaging and look (D and E) of the ice cream rather than the ingredients or price (A, B, and C).
One of the advantages of paired questioning is that it is possible to measure the order of importance from the respondent, while he or she is never asked to think about more than two factors at the same time.
2. Graded paired Comparisons
Graded paired Comparison is an extension of the paired comparison method. It requires respondents to indicate not only their preference but also how much they prefer it over the other factor. This scaling technique gives the marketing researcher an interval-scaled measurement. The procedure is, to begin with, a list of features that might be offered as "options" on the product, and alongside each option, you list its cost. A third column is constructed, and this forms an index of the relative prices of each of the items.
3. The Unity-sum-gain technique
A common problem with launching new products is one of reaching a decision as to what options and how many options one offers. A company often wants to meet the needs of as many market segments as possible, but it also has to make sure that the segment is large enough to enable him to make a profit. One way for evaluating the options which are likely to prove successful is the unity-sum-gain approach.
The unity-sum-gain scaling technique is used to determine which product options are likely to be the most successful in the market. In this technique, researchers offer respondents a list of features that could be offered as product options. Each option is listed with its additional retail cost. The researcher observes how the respondent chooses among the product options based on being given a limited amount of resources to allocate to the optional item listings. This scaling technique is used to determine consumer preferences among a given set of options.
Check out the entire glossary list in a printable list.
- Discrete scale like counts, are numeric data that have a finite number of possible values and can only be whole numbers. Discrete data arise from observations that can only take certain numerical values. Fractions are meaningless. In some situations, mathematical functions or calculations are not possible either.
- Interval scale has intervals which each have the same interpretation and do not have a "true" zero point, therefore it is not possible to make statements about how many times higher one score is than another. One unit represents the same magnitude on the trait or characteristic being measured across the whole range of the scale.
- Likert scale questions present the respondent with a statement and asks for his/her level of agreement with the statement by selecting a rating point. These points have often verbal statements or numbers attached to them. The values should be balanced between positive and negative agreement options.