Rating scales

marketing research  Rating scales

Subjective properties e.g. attitude, beliefs, intentions, preferences etc. are measured with the help of rating scales usually called scales. In a scale numbers are assigned to the amount of characteristic or “construct” being measured. The number varies according to the amount of characteristic available in the object. There are four basic scales.

1. Nominal scale

A nominal scale is one in which number serve as labels to identify or categorize objects or events. All numbers are equal with respect to characteristics of objects. Each number is assigned to only one object and each object has only one number. The number in a nominal scale does not have any relationship with the amount of characteristic. For example a unique number is given to each player in a football team but player having a number 8 does not play better twice than the player having number 4.

Although nominal scales are used for the lowest form of measurement, yet nominal scales are frequently used in marketing research. Nominal level identification are needed in marketing research to identify brands, store types, sales territories customers, gender, geographic location, race, religion, buyer/non buyer heavy and light users etc. The numbers assigned to such categories are mutually exclusive. Alphabets, even symbols, could be used instead of numbers in nominal scales. Nominal scales simply label objects and do not provide information on greater than or less than. Usually counting is permissible operation in nominal scale. Thus statistics like frequency distribution, percentages, mode, chi-square etc. are used while analyzing data gathered by nominal scale. Average in these scales is meaningless.

2. Ordinal Scale

An ordinal scale defines ordered relationship among the objects measured. It indicates relative size difference between objects. An ordinal scale shows whether an object has more or less of the attribute but not as to how much less or more. It shows relative position of the objects under measurement but not what the magnitude of difference is. World ranking of cricket teams, finishing order of horse race, positions of the students in the class and social class are examples of ordinal scales. In marketing research opinions, measurement of preferences, relative attitudes, evaluation of quality of different brands of the same product etc. are through ordinal scales. In ordinal scale difference of numbers indicate difference in rank and nothing else. The statistics commonly used in analyzing data gathered by ordinal scale is percentile, median, rank order correlation etc.

3. Interval scale

One problem with the ordinal scale is that it defines the order of the objects but it does not tell about what is difference (or distance) between the objects. Interval scale shows that as the interval between the numbers on the scale represent equal increments of the attribute being measured, the differences can be compared. A difference between 25 and 26 is the same as between 26 and 27 which is same as between 27 and 28. The most common example of interval scale in life is that of thermometer. But as you know two types of thermometer, Celsius and Fahrenheit do not have a fixed or true zero or freezing point. Both the zeros and units of measurements are different although the amount of heat in various intervals on each thermometer will be same. Let us illustrate it with figures. Amount of heat between 88o and 89o on Fahrenheit is same as between 91o-92o but amount of heat between 88o and 89o on Fahrenheit is different from amount of heat between 88o and 89o on Celsius.

Statistical techniques that are used in nominal and ordinal scales can also be used in interval scale. In addition to that, statistics like mean, standard deviation, product moment correlation etc. can also be used in interval scale.

4. Ratio scale

Ratio scale is one in which a true zero exist. True zero or absolute zero means that number zero is assigned to the absence of the characteristic being measured. Thus we can compute ratio of scale values. For example, it is possible to say how many times greater or smaller one object is than another. This is the only type of scale that allows making comparison of absolute quantities. We can say that market share of company A is twice as much as of company B. In market research, data on number of customers, costs, sales, market share and some other marketing variables are measured on ratio scales. All statistical techniques can be applied to analyze ratio scale data. Summary of four basic rating scales is given below.

Types of Rating Scale

Scale Nature Application in marketing research
Nominal Identification, labeling of objects Classification by gender, location, social class. Identification of stores, brands etc
Ordinal Ordered relations according to more or less of the attribute Ranking, preferences, merit list, positions in the tournament
Interval Interval between adjacent ranks are equal Attitude measurement, index numbers, temperature
Ratio Absolute zero exists. Comparisons possible Sales, income, age, units produced costs, market share

The scales are used in marketing research can be divided into two types:

Comparative and non-comparative scales. In the first type, direct comparison of objects can be made with one another. Data in comparative scales have ordinal or rank order properties. Each object in non-comparative scaling is scaled independently of others in the set. The data in such scales is usually interval or ratio scaled. Likert scale, semantic differential or staple scales are the classification of itemized non comparative scales.

Comparative scales First type of comparative scaling is paired comparison scale. Paired Comparison Scale A paired comparative scale is a technique in which respondent is presented with two objects at a time in the pair and asked to select one according to some criterion. It is like an ordinal scale in which two objects are ranked. Let us see the example. We are presenting you with ten pairs of shampoo brands. Please indicate which one you like for your use.

A B C D E
A - 1 1 0 0
B 0 - 0 0 0
C 0 1 - 0 0
D 1 1 1 - 1
E 1 1 1 0 -
2 4 3 0 1

In the above table, we see that figure 1 in the box means that column brand is preferred to corresponding brand in row and a zero in the box means that brand in row is preferred to the corresponding column. For example in the first row B and C are preferred to A and A is preferred to D and E which is also depicted in first column. Likewise in the second row, B is preferred to all other brands i.e. A, B, C, D, E which is also reflected in second column. Data in this matrix can be analyzed by finding out percentages of preferences. In this case brand A is preferred e.g. A is preferred by 2/10*1000=20%, B=40%, C=30% , D=0% and E=10%. Thus you can find out rank order of the five brands. Paired comparison should be used if the number of brand is limited. In case the number of brands is large, then the exercise becomes unmanageable. Due to this difficulty, respondents cannot usually meaningfully rank more than five or six brands. Another problem with this technique is that the comparison of two objects at a time is seldom the way choices are really made in the marketplace. Thus a brand can be a first choice in paired comparison situation but performs poorly in actual marketplace. Paired comparison, however is most common method of testing the taste.

Rank Order Scale This scale is also comparative in nature. It involves asking the respondents to rank various brands/objects with regard to some criterion. For example a respondent may be asked to rank five print ads on the basis of awareness, it provides, liking of the respondent or intention to buy. Look at the following data.

An Example of Rank-Order Scale

Please rank the following brands of fruit juices in order of preference from 1 to 7.rate your most preferred brand as 1 and least preferred brand 7; all others are in between No. 2 brands will receive the same rating. There is no right or wrong answer: it is just matter of preference.

Brand Preference order
A 2
B 6
C 4
D 1
E 7
F 5
G 3

This technique is frequently used in marketing research. Advantages of rank-order scaling include that it is simple concept, easy to administer and less time consuming to administer than other comparative scale such as paired comparison. The instructions for ranking objects are easy to comprehend. It is also said that the ranking made by the respondent is closer to his/her real purchase situation. The major disadvantage of rank order scale is that it produces only ordinal data. It does not mean that first preference in the set is the most liked. It may be “least disliked” in the set. Constant-Sum ScaleIn constant-sum scale respondents are required to allocate a fixed number of rating point (usually 100) among several objects. It is widely used to measure the relative importance of various attributes of the object. See the following example. Please divide 100 points among the following characteristics of a tooth paste that reflects the relative importance of each characteristic to you in the selection of toothpaste. It an attribute is unimportant to you, assign zero.

Taste _____________8_________ Fragrance _____________7_________ Tube _____________5_________ Cleanliness of teeth _____________35________ Prevention of tooth decay ____________25________ Price _____________2_________ Quality _____________8_________ Shining of the teeth _____________10________ Total 100 _______

The relative importance of the attributes is determined by the counting the points assigned by all respondents and dividing by the number of respondents. The main merit of the constant sum scale is that it permits fine distinction of attributes of an object without much time. However it may be difficult to allocate points to several categories. The main disadvantage of this scale is that respondents may allocate points that exceeds or are short of the required total say 103o or 97o instead of 100.

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