1. Introduction to Marketing Research
CHAPTERS 9/10: MEASUREMENT AND SCALING
Idil Yaveroglu Lecture Notes

2. Measurement and Scaling
Measurement means assigning numbers or other symbols to characteristics of objects according to certain pre-specified rules.
One-to-one correspondence between the numbers and the characteristics being measured.
The rules for assigning numbers should be standardized and applied uniformly.
Rules must not change over objects or time.
We assign numbers to the object in such a way as to represent quantities of attributes associated with that object.
Height: 1 metre 80 cms, Weight: 85 kg.
Number of credit hours earned: 96

3. General issues in measurement
Two essential steps in measurmement process:
Construct development
Operationalization (The process of assigning descriptors to represent the range of possible responses to a question about a particular object or construct)

4. General issues in measurement
Choosing question-response format:
The nature of the property being measured
Gender=dichotomous;
liking for chocolate=scale
Previous research studies
Use format in previous study if you’d like to compare
The data collection mode
Cannot use some scales on the phone
The ability of the respondent
Kids can’t relate to scaled response
The scale level desired for analysis

5. Scaling: Scale characteristics
Scaling involves creating a continuum upon which measured objects are located
1. Description
The unique labels or descriptors that are used to designate each value of the scale. All scales possess description.
Male/female; yes/no
2. Order
The relative sizes or positions of the descriptors are known. Order is denoted by descriptors such as greater than, less than, and equal to.

6. Scale Characteristics
Description
By description we mean the unique labels or descriptors that are used to designate each value of the scale. All scales possess description.
Male/female; yes/no
Order
By order we mean the relative sizes or positions of the descriptors. Order is denoted by descriptors such as greater than, less than, and equal to.

7. Scale Characteristics (Cont.)
Distance The characteristic of distance means that absolute differences between the scale descriptors are known and may be expressed in units. 1 YTL difference between 25 YTL and 26 YTL 10 degrees difference between 25 C and 35 C Origin The origin characteristic means that the scale has a unique or fixed beginning or true zero point. Zero market share, zero YTL, zero purchases

8. An Illustration of Scale Characteristics
Age in Years
Distance
120
Very Old
Description
Golden Years 80
Seniors 60
Order
Middle Aged 40
Young Adults 20
Youth
Origin
New Born

9. Primary Scales of Measurement
Lowest level of measurement
Primary Scales
Nominal Scale
Ordinal Scale
Interval Scale
Ratio Scale
Highest level of measurement

10. Why important?
The scale affects what may or may not be said about the property being measured.
Examples:
If you wish to calculate an average, you must use an interval or ratio scale.
If you have a nominal or ordinal scale, you must summarize the results with a percentage or frequency distribution.
Nominal scales - Nominal scales are defined as those that use only labels; that is, they possess only the characteristic of description.
Ordinal scales - Ordinal scales permit the researcher to rank-order the respondents or their responses.
Interval scales - Interval scales are those in which the distance between each descriptor is known.
Ratio scales - Ratio scales are ones in which a true zero origin exists—such as an actual number of purchases in a certain time period, dollars spent, miles traveled, number of children, or years of college education.
There is a hierarchy of scales; ratio scales are the “highest” and nominal scales are the “lowest.” Nominal scales simply label objects. Ordinal scales indicate only relative size differences between objects. Interval scales use descriptors that are equal distances apart. Ratio scales have a true zero point.

11. Primary Scales of Measurement: Nominal Scale
The numbers serve only as labels or tags for identifying and classifying objects.
The numbers do not reflect the amount of the characteristic possessed by the objects.
The only permissible operation on the numbers in a nominal scale is counting.
Only a limited number of statistics, all of which are based on frequency counts, are permissible, e.g., percentages, and mode.
MEAL PREFERENCE: Breakfast, Lunch, Dinner
    RELIGIOUS PREFERENCE: 1 = Buddhist, 2 = Muslim, 3 = Christian, 4 = Jewish, 5 = Other
    POLITICAL ORIENTATION: Republican, Democratic, Libertarian, Green

13. Primary Scales of Measurement: Ordinal Scale
A ranking scale in which numbers are assigned to objects to indicate the relative extent to which the objects possess some characteristic. An ordinal scale indicates direction, in addition to providing nominal information.
Can determine whether an object has more or less of a characteristic than some other object, but not how much more or less.
Examples:
RANK: 1st place, 2nd place, ... last place   LEVEL OF AGREEMENT: No, Maybe, Yes
POLITICAL ORIENTATION: Left, Center, Right
In addition to the counting operation allowable for nominal scale data, ordinal scales permit the use of statistics based on centiles, e.g., percentile, quartile, median.

14. Ordinal Scale

15. Primary Scales of Measurement: Interval Scale
Numerically equal distances on the scale represent equal values in the characteristic being measured. Interval scales provide information about order, and also possess equal intervals.
It permits comparison of the differences between objects.
Examples:
TIME OF DAY on a 12-hour clock  
POLITICAL ORIENTATION: Score on standardized scale of political orientation
Statistical techniques that may be used include all of those that can be applied to nominal and ordinal data, and the arithmetic mean, standard deviation, and other statistics commonly used in marketing research.

16. Interval Scale

17. Primary Scales of Measurement: Ratio Scale
Possesses all the properties of the nominal, ordinal, and interval scales.
It has an absolute zero point.
Using a ratio scale permits comparisons such as being twice as high, or one-half as much.
Examples:
RULER: inches or centimeters YEARS of work experience
  INCOME: money earned last year NUMBER of children
  GPA: grade point average
All statistical techniques can be applied to ratio data.

18. Ration Scale

19. What type of measurement scale?
Q1. The following is a question on a survey:
Please check the appropriate price range that indicates the amount you spend each week on gasoline for your car:
_____ 1. $ $10.00
_____ 2. $ $20.00
_____ 3. $ $30.00
_____ 4. $ $40.00
Ordinal Scale

20. What type of measurement scale? (cont’d)
Q2. The number of children in a family is an example of what kind of data?
Q3. In a survey of luxury car owners, respondents were chosen from 4 states; California, New York, Illinois, and Ohio. What is the level of measurement that is reflected by the states the owners were selected from?
RatioScale
Nominal Scale

21. A Classification of Scaling Techniques
Comparative
Scales
Itemized
Rating Scales
Continuous
Noncomparative
Paired
Comparison
Rank
Order
Constant
Sum
Stapel
Likert
Semantic
Differential

22. A Comparison of Scaling Techniques
Comparative scales involve the direct comparison of stimulus objects. Comparative scale data must be interpreted in relative terms and have only ordinal or rank order properties.
In noncomparative scales, each object is scaled independently of the others in the stimulus set. The resulting data are generally assumed to be interval or ratio scaled.

23. Comparative Scaling Techniques: Paired Comparison Scaling
A respondent is presented with two objects and asked to select one according to some criterion.
The data obtained are ordinal in nature.
Paired comparison scaling is the most widely used comparative scaling technique.
With n brands, [n(n - 1) /2] paired comparisons are required.
Under the assumption of transitivity, it is possible to convert paired comparison data to a rank order.

24. Paired Comparison Scaling
Instructions
We are going to present you with ten pairs of shampoo brands. For each pair, please
indicate which one of the two brands of shampoo in the pair you would prefer for
personal use.
Recording Form
Jhirmack
Finesse
Vidal Sassoon
Head & Shoulders
Pert 1 1A
Number of times preferred 3B 2 4
A A 1 in a particular box means that the brand in that column was preferred over the
brand in the corresponding row. A 0 means that the row brand was preferred over the column brand.
B The number of times a brand was preferred is obtained by summing the 1s in each column.

25. Comparative Scaling Techniques: Rank Order Scaling
Respondents are presented with several objects simultaneously and asked to order or rank them according to some criterion.
Possible that the respondent may dislike the brand ranked 1 in an absolute sense.
Furthermore, rank order scaling also results in ordinal data.
Only (n - 1) scaling decisions need be made in rank order scaling.

26. Rank Order Scaling Instructions
Rank the various brands of toothpaste in order of preference. Begin by picking out the one brand that you like most and assign it a number 1. Then find the second most preferred-brand and assign it a number 2. Continue this procedure until you have ranked all the brands of toothpaste in order of preference. The least preferred brand should be assigned a rank of 10. No two brands should receive the same rank number. The criteria of preference is entirely up to you. There is no right or wrong answer—
Just try to be consistent.
Brand
Rank Order
1. Crest
2. Colgate
3. Aim
4. Mentadent
5. Macleans
6. Ultra Brite
7. Close Up
8. Pepsodent
9. Plus White
10. Stripe

27. Comparative Scaling Techniques: Constant Sum Scaling
Respondents allocate a constant sum of units, such as 100 points, to attributes of a product to reflect their importance.
If an attribute is unimportant, the respondent assigns it zero points.
If an attribute is twice as important as some other attribute, it receives twice as many points.
The sum of all the points is Hence, the name of the scale.

28. AVERAGE RESPONSE OF 3 SEGMENTS
Constant Sum Scaling
Instructions
Below are eight attributes of bathing soaps. Please allocate 100 points among the attributes so that your allocation reflects the relative importance you attach to each attribute. The more points an attribute receives, the more important the attribute is. If an attribute is not at all important, assign it zero points. If an attribute is twice as important as some other attribute, it should receive twice as many points.
Form
AVERAGE RESPONSE OF 3 SEGMENTS
Attributes
Segment I
Segment II
Segment III
1. Mildness 8 2 4
2. Lather 17
3. Shrinkage 3 9 7
4. Price 53
5. Fragrance 19
6. Packaging 5
7. Moisturising 20
8. Cleaning Power 13 60 15
SUM
100

29. Relationship of Measurement and Scaling to the Marketing Research Process
Step 1 : Marketing Research Problem Definition
Step 2 : Approach to the Problem
• Specify the information needed
Step 3 : Research Design
•Use appropriate level of measurement and appropriate scales to measure each item of information
•Questionnaire Design: Translate the information needed to
appropriate questions using the identified scales
Step 4: Fieldwork
Administer questions using the identified scales
Step 5 : Data Preparation and Analysis:
•Use appropriate statistical techniques compatible with the level of measurement of the data
Step 6: Report Preparation and Presentation
Discuss the statistical results and findings in light of the scales used .

30. Primary Scales of Measurement
Basic Description
Scale Characteristics
Common Examples
Marketing Examples
Permissible Statistics
Nominal
Numbers
Identify and classify objects
Description
Social Security numbers; numbering of football players
Brand numbers; store types; sex classification
Percentages; mode
Ordinal
Numbers indicate the relative positions of the objects but not the magnitude of differences between them
Order
Quality rankings; rankings of teams in a tournament
Preference rankings; market position; social class
Percentile; median
(continued on next slide)

31. Primary Scales of Measurement (Cont.)
Basic Description
Scale Characteristics
Common Examples
Marketing Examples
Permissible Statistics
Interval
Differences between objects can be compared; zero point is arbitrary
Description
Order
Distance
Temperature (Fahrenheit, Celsius)
Attitudes; opinions; index numbers
Range; mean; standard deviation
Ratio
Zero point is fixed; ratios of scale values can be computed
Origin
Length; weight
Age; income; costs; sales; market shares
Geometric mean (All)

32. Illustration of Primary Scales of Measurement
NOMINAL SCALE
ORDINAL SCALE
INTERVAL SCALE
RATIO SCALE
No.
Jean Brand
Preference Ranking
Preference Ratings
Price ($)
1 - 7 1.
Bugle Boy 7 79 5 15 30 2.
Calvin Klein 2 25 17 48 3.
Diesel 8 82 27 4.
Gap 3 6 16 32 5.
Guess? 1 10 34 6.
Jordache 53 35
Lee 9 95 4 14 8.
Levi 61 33 9.
Old Navy 45 29
10.
Wrangler
115 12 24

33. Relative Advantages of Comparative Scales
Small differences between stimulus objects can be detected.
Same known reference points for all respondents.
Easily understood and can be applied.
Involve fewer theoretical assumptions.
Tend to reduce halo or carryover effects from one judgment to another.

34. Relative Disadvantages of Comparative Scales
Ordinal nature of the data.
Inability to generalize beyond the stimulus objects scaled.

35. Noncomparative Scaling Techniques
Respondents evaluate only one object at a time
Noncomparative techniques consist of continuous and itemized rating scales.

36. A Classification of Non-comparative Rating Scales
Continuous
Itemized
Semantic
Differential
Stapel
Likert

37. Continuous Rating Scale
Respondents rate the objects by placing a mark at the appropriate position on a line that runs from one extreme of the criterion variable to the other. The form of the continuous scale may vary considerably. Leads to interval scale

38. Itemized Rating Scales
The respondents are provided with a scale that has a number or brief description associated with each category.
The categories are ordered in terms of scale position, and the respondents are required to select the specified category that best describes the object being rated.
The commonly used itemized rating scales are the Likert, semantic differential, and Stapel scales.

39. Likert Scale
The Likert scale requires the respondents to indicate a degree of agreement or disagreement with each of a series of statements about the stimulus objects.
Strongly Disagree Neither Agree Strongly
disagree agree nor agree
disagree
1. Sears sells high quality merchandise X
2. Sears has poor in-store service X
3. I like to shop at Sears X 4 5
The analysis can be conducted on an item-by-item basis (profile analysis), or a total (summated) score can be calculated.
When arriving at a total score, the categories assigned to the negative statements by the respondents should be scored by reversing the scale.

40. Semantic Differential Scale
The semantic differential is a seven-point rating scale with end
points associated with bipolar labels that have semantic meaning.
SEARS is:
Powerful --:--:--:--:-X-:--:--: Weak
Unreliable --:--:--:--:--:-X-:--: Reliable
Modern --:--:--:--:--:--:-X-: Old-fashioned
The negative adjective or phrase sometimes appears at the left side of the scale and sometimes at the right.
This controls the tendency of some respondents, particularly those with very positive or very negative attitudes, to mark the right- or left-hand sides without reading the labels.
Individual items on a semantic differential scale may be scored on either a -3 to +3 or a 1 to 7 scale.

41. Stapel Scale
The Stapel scale is a unipolar rating scale with ten categories numbered from -5 to +5, without a neutral point (zero). This scale is usually presented vertically.
SEARS +5 +4 +3 +2
+2x +1
High Quality
Poor Service -1 -2 -3
-4x -4 -5
The data obtained by using a Stapel scale can be analyzed in the same way as semantic differential data.

42. Table 10.1 Basic Noncomparative Scales
Basic Characteristics
Examples
Advantages
Disadvantages
Continuous Rating Scale
Place a mark on a
continuous line
Reaction to TV commercials
Easy to
construct
Scoring can be cumbersome
Unless
computerized
Itemized
Rating Scales
Likert Scale
Degree of agreement on a 1 (strongly
disagree) to 5
(strongly agree) scale
Measurement of attitudes
construct,
administer, and understand
More time
consuming
Semantic Differential
Seven-point scale with
bipolar labels
Brand, product, and company
images
Versatile
Difficult to cons-truct bipolar
adjectives
Stapel Scale
Unipolar ten-point
scale, -5 to +5, without a neutral point (zero)
Measurement of attitudes and
construct and administer over
telephone
Confusing and
difficult to apply

43. Summary of Itemized Rating Scale Decisions
1. Number of categories
While there is no single, optimal number,
traditional guidelines suggest that there
should be between five and nine categories.
2. Balanced vs. unbalanced
In general, the scale should be balanced to
obtain objective data.
3. Odd or even number of
Categories
If a neutral or indifferent scale response is
possible for at least some of the respondents, an odd number of categories should be used.

44. Summary of Itemized Rating Scale Decisions (Cont.)
4. Forced versus nonforced
In situations where the respondents are
expected to have no opinion, the accuracy of data may be improved by a nonforced scale.
5. Verbal description
An argument can be made for labeling all or many scale categories. The category
descriptions should be located as close to the response categories as possible.
6. Physical form
A number of options should be tried and the
best one selected.

45. Balanced and Unbalanced Scales
Surfing the Internet is
____ Extremely Good
____ Very Good
____ Good
____ Bad
____ Very Bad
____ Extremely Bad
____ Somewhat Good
Balanced Scale
Unbalanced Scale

46. Rating Scale Configurations
A variety of scale configurations may be employed to measure the comfort of Nike shoes. Some examples include:
Nike shoes are:
1) Place an “X” on one of the blank spaces…
Very Very
Uncomfortable Comfortable
2) Circle the number…
Very Very
Uncomfortable Comfortable
3) Place an “X” on one of the blank spaces…
Very Uncomfortable
Uncomfortable
Neither Uncomfortable nor Comfortable
Comfortable
Very Comfortable

47. Rating Scale Configurations (Cont.)
Very
Uncomfortable
Somewhat
Neither Comfortable nor Uncomfortable
Somewhat Comfortable
Comfortable
Very Comfortable 4)
Neither Comfortable nor Uncomfortable 5)

48. Some Unique Rating Scale Configurations
Thermometer Scale
Instructions: Please indicate how much you like McDonald’s hamburgers by coloring in the thermometer. Start at the bottom and color up to the temperature level that best indicates how strong your preference is.
Form:
Smiling Face Scale Instructions: Please point to the face that shows how much you like the Barbie Doll. If you do not like the Barbie Doll at all, you would point to Face 1. If you liked it very much, you would point to Face 5.
Like very much
Dislike very much

49. Some Commonly Used Scales in Marketing
Construct
Scale Descriptors
Attitude
Very Bad, Bad, Neither Bad nor Good, Good, Very Good
Importance
Not at All Important, Not Important, Neutral, Important, Very Important
Satisfaction
Very Dissatisfied, Dissatisfied, Neither Dissatisfied nor Satisfied, Satisfied, Very Satisfied
Purchase Frequency
Never, Rarely, Sometimes, Often, Very Often
Copyright © 2012 Pearson Education, Inc.
Copyright © 2012 Pearson Education, Inc.
Chapter

50. Developing a Multi-Item Scale
Develop the Constraint
Develop a Theoretical Definition
Develop an Operational Definition
Develop a Multi-Item Scale
Generate a Pool of Scale Items
Reduce the Pool of Items Based on Judgment
Collect Data
Purify the Scale Based on Statistical Analysis
Evaluate Scale Reliability and Validity
Apply the Scale and Accumulate Research Findings

51. Scale Evaluation Scale Evaluation Validity Reliability Content
Chapter
Scale
Evaluation
Reliability
Validity
Test-Retest
Internal
Consistency
Alternative
Forms
Construct
Criterion
Content
Convergent
Discriminant
Nomological

52. Total Measurement Error
Total measurement error is the sum of systematic error and random error.
Total measurement error = Systematic error + Random error
Systematic error affects the measurement in a constant way, that is, in the same way each time the measurement is made.
Random error, in contrast, arises from random changes and has a different effect each time the measurement is made.

53. Reliability and Validity
Recall class on reliability versus validity!
Reliability:
Test-retest reliability
Inter-rater reliability
Internal consistency
reliability
Split-half reliability
Cronbach’s alpha
Recall: a reliable measure may
not be valid! II

54. Reliability
Reliability can be defined as the extent to which measures are free from random error.
In test-retest reliability, respondents are administered identical sets of scale items at two different times and the degree of similarity between the two measurements is determined.
In alternative-forms reliability, two equivalent forms of the scale are constructed and the same respondents are measured at two different times, with a different form being used each time.

55. Reliability (Cont.)
Internal consistency reliability determines the extent to which different parts of a summated scale are consistent in what they indicate about the characteristic being measured.
In split-half reliability, the items on the scale are divided into two halves and the resulting half scores are correlated.
The coefficient alpha, or Cronbach's alpha, is the average of all possible split-half coefficients resulting from different ways of splitting the scale items. This coefficient varies from 0 to 1, and a value of 0.6 or less generally indicates unsatisfactory internal consistency reliability.

56. Validity
The validity of a scale may be defined as the extent to which differences in observed scale scores reflect true differences among objects on the characteristic being measured, rather than systematic or random error. Perfect validity requires that there be no measurement error.
Content validity is a subjective but systematic evaluation of how well the content of a scale represents the measurement task at hand.

57. Relationship Between Reliability and Validity
If a measure is perfectly valid, it is also perfectly reliable. In this case, there is no random or systematic error.
If a measure is unreliable, it cannot be perfectly valid, since at a minimum random error is present. Thus, unreliability implies invalidity.
If a measure is perfectly reliable, it may or may not be perfectly valid, because systematic error may still be present.
Reliability is a necessary, but not sufficient, condition for validity.