CHAPTER 8

Lecture 8

MEASUREMENT AND SCALING:

FUNDAMENTALS AND COMPARATIVE SCALING

CHAPTER OBJECTIVES1.Introduce the concepts of measurement and scaling and show how scaling may be considered an extension of measurement.

2.Discuss the primary scales of measurement and differentiate nominal, ordinal, interval, and ratio scales.

3.Classify and discuss scaling techniques as comparative and noncomparative, and describe the comparative techniques of paired comparison, rank order, constant sum, and Q-sort scaling.

4.Discuss the considerations involved in implementing the primary scales of measurement in an international setting.

5.Understand the ethical issues involved in selecting scales of measurement.

TEACHING SUGGESTIONS

Chapter Objective 1

Explain the differences between measurement and scaling.

Distinguish the two concepts by noting that measurement precedes scaling in test construction. Measurement is the assignment of numbers or other symbols to characteristics of objects according to certain prespecified rules. Scaling is an extension of measurement where it involves the generation of a continuum upon which measured objects are located.

Chapter Objective 2

Discuss and illustrate the primary scales of measurement.

1)Nominal scale: This is used only as a labeling scheme where numbers serve only as labels or tags for identifying and classifying objects. The numbers in a nominal scale do not reflect the amount of a characteristic possessed by the objects, rather they are used only for identification. For example, numbers on baseball players uniforms, street names, or social security numbers.

2)Ordinal scale: This is a ranking scale in which numbers are assigned to objects to indicate the relative extent to which some characteristic is possessed. It is then possible to determine whether an object has more or less of a characteristic than some other object. For example, rankings of teams for the NCAA Basketball tournament, socioeconomic status, and quality rankings.

3)Interval scale: Numbers are used to rank objects such that numerically equal distances on the scale represent equal distances in the characteristic being measured. Examples include time and temperature.

4)Ratio scale: This is used to identify or classify objects, rank order the objects, and compare intervals, differences. For example, height, age, and income.

Figures 8.1 and 8.2, Tables 8.1 and 8.2 provide a framework and examples for explaining scaling. (See text)

Chapter Objective 3

Distinguish the two broad scaling measures.

Begin by stating the two types of scales: comparative and noncomparative.

Comparative scalesa direct comparison of stimulus objects is elicited. Thus, two brands may be compared along a dimension such as quality.

Noncomparative scalesthe respondent provides whatever standard seems appropriate to him/her, thus, only one object is evaluated at a time. In this case, one brand is rated on a scale independent of other brands.

See Figure 8.2 (See text) for the hierarchy of scaling procedures.

Describe the different comparative scaling techniques. If available, bring examples of different scales to class to show to students.

Begin by recalling that all comparative scaling techniques involve a direct comparison of stimulus objects with one another. This should be highlighted as each of the scales are discussed in turn.

1) Paired comparison scaling: Here a respondent is presented with two objects at a time and asked to select one object in the pair according to some criterion. The data obtained is ordinal in nature. This is frequently used in marketing when comparisons of products or brands are being made.

See Figure 8.3 (See text) for an example of paired comparison scaling.

2) Rank order scaling: Respondents are presented with several objects simultaneously and asked to order or rank them according to some criterion. This is commonly used to measure preferences for brands as well as the importance of attributes.

See Figure 8.4 (See text) for an example of rank order scaling.

3) Constant sum scaling: Respondents are required to allocate a constant sum of units such as points, dollars, chits, stickers, or chips among a set of stimulus objects with respect to some criterion. Specific instructions are provided that if an attribute is not at all important, it is possible to assign zero points. If an attribute is twice as important as some other attribute it should receive twice as many points.

See Figure 8.5 (See text) for an example.

4) Q-sort scaling: This technique uses a rank order procedure in which objects are sorted into piles based on similarity with respect to some criterion.

5) Magnitude estimation: Here numbers are assigned to objects such that ratios between the assigned numbers reflect ratios among the objects on the specified criterion.

6)Guttman scaling or scalogram analysis: A procedure for determining whether a set of objects can be ordered into an internally consistent, unidimensional scale.

Chapter Objective 4

Identify the measurement and scaling issues in International research.

From the viewpoint of the respondents, nominal scales are the simplest to use, whereas ratio scales are the most complex. Respondents in many developed countries, due to higher education and consumer sophistication levels, are quite used to providing responses on interval and ratio scales. However, such is not the case in less developed countries. Preferences can, therefore, be best measured by using ordinal scales in less developed countries. In particular, the use of binary scales (e.g., preferred/not preferred) is recommended.

Example: While measuring preferences for jeans in the United States, Levi Strauss & Co. could ask consumers to rate their preferences for wearing jeans on specified occasions using a seven point interval scale. However, consumers in Papua, New Guinea could be shown a pair of jeans and simply asked whether or not they would prefer to wear it for a specific occasion (e.g., when shopping, working, relaxing on a holiday, etc.).

Chapter Objective 5 Discuss the ethical concerns of scaling.

The researcher has the responsibility to use the appropriate type of scales to get the data needed to answer the research questions and test the hypotheses. For example, if personality characteristics are measured using ordinal scales, these data cannot be easily used in multivariate analysis. To examine differences in the personality characteristics and relate them to other consumer behavior variables, interval scale data are needed.

After the data have been collected, they should be analyzed correctly. If ordinal scaled data are collected, statistical procedures developed for use with interval or ratio data should not be used. Conclusions based on the misuse of statistics are misleading. Using the personality example above, if after data collection the client wishes to know how the users and nonusers differed, the researcher should treat these data correctly and use nonmetric techniques for analysis (discussed in Chapter 15). When the researcher lacks the expertise or the computer software to compute these statistics, ethical dilemmas arise. Either an outside statistician should be hired or the relevant software should be obtained.

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