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Figure 5.1;

Classification of scaling techniques

Figure 5.1;

Classification of scaling techniques

While comparative scaling is used for comparison between stimuli, on the other hand, non-comparative scaling involves each stimulus object being scaled independently of the other objects in the stimulus set. The resulting data in non-comparative scale are assumed to be interval or ratio scaled. For example, instead of direct comparison between brands researcher may ask the respondent to rate each brand separately on a scale of 1 - 10 and can evaluate each brand as well as compare the brands also. Non-comparative scaling techniques involve continuous rating scales as well as itemised rating scales. The itemised rating scales are further sub-divided into likert scale, semantic differential scale and stapel scale. As one can easily infer, non-comparative scaling is highly used in marketing research. In the following section we will focus on each of the scaling techniques in details.

5.6 Comparative scaling techniques

As discussed above, comparative scaling techniques provide a direct comparison between stimulus objects. Because the respondents are forced to choose one out of two (or many) stimulus objects, researchers can identify small differences between stimulus objects. One of the other advantages of comparative scaling is the easy application by researcher and easy understanding by the respondent. Comparative scaling involves fewer theoretical assumptions however as the data gathered using this technique is mostly ordinal it lacks distance and origin properties and therefore, does not provide possibility of carrying out various advance statistical techniques.

5.6.1 Paired comparison scaling

In paired comparison scaling, respondents are asked to choose one among two alternatives on a selected criterion. For example, a respondent may be asked to choose between two well-known toothpaste brands on the criterion of quality. The data obtained from paired comparison scaling is ordinal in nature. When there are more than two stimuli involved paired comparison scaling can still be useful technique to compare various stimuli. Lets say, a researcher is interested in knowing consumers preference among three different toothpaste brands, A, B and C. Using the paired comparison scaling researcher will create three questions for respondents namely:

1. Preference between toothpaste brand A and brand B

2. Preference between toothpaste brand B and brand C

3. Preference between toothpaste brand A and brand C.

If for the first question respondent choose brand A over brand B and in the second question chose brand B over brand C, using simple logic researcher can derive that brand A will be more preferred in comparison to brand C. In simple terms, using paired comparison scaling researcher can generate a rank order among stimuli. Paired comparison scaling is used in pricing decisions frequently. It is quite helpful when the number of stimuli is limited. In such circumstances, paired comparison can reveal direct comparisons and overt choice. However, when large number of stimuli is involved, paired comparison scaling becomes a tedious technique. Paired comparison scaling is highly used in product testing. Many food companies and other Fast Moving Consumer Goods (FMCG) companies use this technique to compare their existing product with an upcoming variant or with their competitor's products. CocaCola is reported to have conducted more than 190,000 paired comparisons before introducing new Coke in 1985.56

5.6.2 Rank order scaling

Rank order scaling as the name suggests is about ranking a specific set of stimuli on a predefined criterion. It's also quite popular among researchers when trying to understand a specific rank order among various stimuli. The respondents are provided with various stimuli objects and asked to rank the most preferred object, the second most preferred object and so on. The earlier example of newspaper selection was kind of rank order scaling where respondents were asked to choose most preferred to least preferred newspapers. This scaling technique also uses comparison between stimuli objects using a pre-determined criterion (in the case of newspapers it may be content quality, use of relevant images and so on). In absence of such criterion this technique may deliver biased results. Furthermore, looking at the ranking in isolation also can create bias. For example, newspaper X may be the most preferred in terms of content quality however may be ranked lower in overall readability. Rank order scaling generates ordinal data and therefore lacks distance and origin properties. Due to the absence of distance and origin properties rank order scaling cannot provide an objective difference between various stimuli objects. For example, in the newspaper example, the researcher using rank order scaling cannot confidently state that the difference between preference of newspaper X, Y and Z (ranked as most to least preferred) is constant. In other way, we cannot determine if the preference difference between newspaper X and newspaper Y; and newspaper Y and newspaper Z is the same. While there are disadvantages of using rank order scaling, the ease of understanding is the greatest advantage associated with rank order scaling. When asked, most respondents can easily understand the instructions for ranking as the ranking process reflects our real life shopping environment and choice process.

5.6.3 Constant sum scaling

In constant sum scaling, respondents are asked to assign a constant sum of units (could include points, currency, and so on) to a specific set of stimulus objects with respect to some pre-defined criterion. For example, researcher may ask the respondents to assign a number according to their perceptions of a specific stimuli object on the criteria chosen so as the total becomes 100. The attributes are scaled by counting the points assigned to each criterion by all the respondents and divided by the number of respondents. Table 5.1 below provides detailed explanation of how constant sum scaling is used in real life. The table explains respondents' preferences regarding various pre-defined criteria namely: content quality, supplements, writers (columnists) involved, images used, breadth of coverage (local, regional, local and global) and advertisements. Respondents were asked to rate each criteria in such a way that the total of their responses becomes 100. Two hundred responses were collected. From the table, it can be observed that respondents put content quality as the most preferred factor and advertisement in the newspaper to be least preferred factor. Furthermore, it can also be stated that supplements provided with the newspapers as well as images used within the newspaper are twice as important in comparison to writers or columnist involved with the newspaper. Using the numbers assigned researcher can easily convert constant sum scale into rank order scale.

Table 5.1:

Example of constant sum scaling

Table 5.1:

Example of constant sum scaling

Continue reading here: Data preparation and preliminary data analysis

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