Marketing Research Module 3 Scaling

8/14/2019 Marketing Research Module 3 Scaling

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2007 Prentice Hall

8-1

Ch apter E ight

Measurement and Scaling:

Fundamentals andComparative Scaling


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2007 Prentice Hall 8-3

Cha pter Ou tl ine5) Comparative Scaling Techniques

i. Paired Comparison

ii. Rank Order Scaling

iii. Constant Sum Scaling

iv. Q-Sort and Other Procedures

6) Verbal Protocols

7) International Marketing Research

4) Ethics in Marketing Research

5) Summary


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Mea su remen t a nd Sc al ingMea su reme nt means assigning numbers or othersymbols 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 bestandardized and applied uniformly.

Rules must not change over objects or time.


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Mea sur emen t a nd S ca l ingSc ali ng in vo lve s cr eat ing a co nt inuum uponwh ich mea su red object s are lo cated .Consider an attitude scale from 1 to 100. Eachrespondent is assigned a number from 1 to 100, with 1= Extremely Unfavorable, and 100 = ExtremelyFavorable. Measurement is the actual assignment of a

number from 1 to 100 to each respondent. Scaling isthe process of placing the respondents on a continuumwith respect to their attitude toward department stores.


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Pr imary Sc ales of Mea sur emen t

7 3

ScaleNominal Numbers

Assigned

to Runners

Ordinal Rank Orderof Winners

Interval PerformanceRating on a

0 to 10 Scale

Rat io Time toFinish, in

Fig. 8.1

Thirdplace Secon dpla ce Fir stplace

Finish

Finish

8.2 9.1 9.6

15.2 14.1 13.4


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8/25 2007 Prentice Hall 8-8

Ill ustra tio n o f Pri mar y Scales ofMeasur ementTable 8.2

Nominal Ordinal Ratio

Scale Scale Scale Preference $ spent last

No. Store Rankings

3 months

1. Parisian

2. Macys

3. Kmart

4. Kohls

5. J.C. Penney

6. Neiman Marcus

7. Marshalls

8. Saks Fifth Avenue

9. Sears

10.Wal-Mart

IntervalScale

PreferenceRatings

1-7 11-177 79 5 15 0

2 25 7 17 200

8 82 4 14 0

3 30 6 16 100

1 10 7 17 250

5 53 5 15 35

9 95 4 14 0

6 61 5 15 100

4 45 6 16 0

10 115 2 12 10



Pr imary Sc ales of Mea sur emen tOrdi nal Sc ale A ranking scale in which numbers are assigned to objects to

indicate the relative extent to which the objects possess somecharacteristic.

Can determine whether an object has more or less of acharacteristic than some other object, but not how muchmore or less.

Any series of numbers can be assigned that preserves the

ordered relationships between the objects. In addition to the counting operation allowable for nominal

scale data, ordinal scales permit the use of statistics based oncentiles, e.g., percentile, quartile, median.



Pr imary Sc al es of Measur emen tInte rva l S ca le Numerically equal distances on the scale represent equal

values in the characteristic being measured.

It permits comparison of the differences between objects.

The location of the zero point is not fixed. Both the zero pointand the units of measurement are arbitrary.

Any positive linear transformation of the form y = a + bx willpreserve the properties of the scale.

It is not meaningful to take ratios of scale values. Statistical techniques that may be used include all of those

that can be applied to nominal and ordinal data, and inaddition the arithmetic mean, standard deviation, and otherstatistics commonly used in marketing research.



Pr imary Sc ales of Mea sur emen tRatio S ca le Possesses all the properties of the nominal, ordinal, and

interval scales.

It has an absolute zero point.

It is meaningful to compute ratios of scale values.

Only proportionate transformations of the form y = bx,where b is a positive constant, are allowed.

All statistical techniques can be applied to ratio data.



Pr imary Sc al es of Measur emen tTable 8.1

Scale Basic

Characteristics

Common

Examples

Marketing

Examples

Nominal Numbers identify

& classify objects

Social Security

nos., numbering

of football players

Brand nos., store

types

Percentages,

mode

Chi-square,

binomial test

Ordinal Nos. indicate the

relative positions

of objects but not

the magnitude of

differences

between them

Quality rankings,

rankings of teams

in a tournament

Preference

rankings, market

position, social

class

Percentile,

median

Rank-order

correlation,

Friedman

ANOVA

Ratio Zero point is fixed,

ratios of scale

values can be

compared

Length, weight Age, sales,

income, costs

Geometric

mean, harmonic

mean

Coefficient of

variation

Permissible Statistics

Descriptive Inferential

Interval Differencesbetween objects

Temperature(Fahrenheit)

Attitudes,opinions, index

Range, mean,standard

Product-moment



A C lassi f ication o f S cal ingTec hni que sFigure 8.2

LikertSemantic

Differential

Stapel

Scaling Techniques

Noncomparative

Scales

Comparative

Scales

Paired

Comparison

Rank

Order

Constant

Sum

Q-Sort and

OtherProcedures

ContinuousRating Scales

ItemizedRating Scales



A Com pa ri son of S ca lingTechniques Co mpa ra tiv e scal es involve the direct comparison of

stimulus objects. Comparative scale data must beinterpreted in relative terms and have only ordinal orrank order properties.

In noncompa rat iv e sca le s, each object is scaledindependently of the others in the stimulus set. Theresulting data are generally assumed to be interval orratio scaled.



Re lative Adv anta ges of Co mpara tiveSca les 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 onejudgment to another.



Rel ati ve Di sadva nta ges ofCo mpar ative S ca les

Ordinal nature of the data

Inability to generalize beyond the stimulusobjects scaled.



o mpa ra ve ca ngTechniquesPa ire d Co mp ariso n S ca l ing 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 arerequired.

Under the assumption of transitivity, it is possible toconvert paired comparison data to a rank order.



Usi ng Pai red C ompar is onsFig. 8.3

Instructions: We are going to present you with ten pairs ofshampoo brands. For each pair, please indicate which one of thetwo brands of shampoo you would prefer for personal use.

Reco rding Fo rm: Jhirmack Finesse VidalSassoon Head &Shoulders PertJhirmack 0 0 1 0

Finesse 1a 0 1 0

Vidal Sassoon 1 1 11

Head & Shoulders 0 0 0 0

Pert 1 1 0 1

Number of Times

Preferredb

3 2 0 4

1

aA 1 in a particular box means that the brand in that column was preferredover the brand in the corresponding row. A 0 means that the row brand waspreferred over the column brand. bThe number of times a brand was preferredis obtained by summing the 1s in each column.



Pa ire d Co mp ariso n Sel l ingThe most common method of taste testing is paired comparison.

The consumer is asked to sample two different products and selectthe one with the most appealing taste. The test is done in privateand a minimum of 1,000 responses is considered an adequatesample. A blind taste test for a soft drink, where imagery, self-perception and brand reputation are very important factors in the

consumers purchasing decision, may not be a good indicator ofperformance in the marketplace. The introduction of New Cokeillustrates this point. New Coke was heavily favored in blind pairedcomparison taste tests, but its introduction was less than successful,because image plays a major role in the purchase of Coke.

A paired comparisontaste test


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Co mpar ative S ca lingTec hn iqu esRan k O rde r S ca l ing Respondents are presented with several objects

simultaneously and asked to order or rank themaccording to some criterion.

It is possible that the respondent may dislike the brandranked 1 in an absolute sense.

Furthermore, rank order scaling also results in ordinal

data.

Only (n - 1) scaling decisions need be made in rankorder scaling.


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re ere nc e o r o o pa s e ra n sUsi ng Rank Orde r S calingFig. 8.4

In stru ctions: Rank the various brands of toothpaste inorder of preference. Begin by picking out the one brand that

you like most and assign it a number 1. Then find thesecond most preferred brand and assign it a number 2.Continue this procedure until you have ranked all thebrands of toothpaste in order of preference. The leastpreferred brand should be assigned a rank of 10.

No two brands should receive the same rank number.

The criterion of preference is entirely up to you. There is noright or wrong answer. Just try to be consistent.


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Usi ng Rank Orde r S caling

Brand Rank Ord er1. Crest ____ _____2. Col gate _________3. Ai m _________4. Gl eem _________5. Sensod yne _________6. Ult ra Bri te _____ ____7. Cl ose Up _____ ____8. Pep soden t _________9. Pl us W hi te _________10. St ri pe _____ ____

Fig. 8.4 cont.

Form


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Co mpa rative S ca lingTe chn iqu esCo nsta nt Su m Sc al ing 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 itzero points.

If an attribute is twice as important as some otherattribute, it receives twice as many points.

The sum of all the points is 100. Hence, the name ofthe scale.


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Attr ibut esUsi ng a Constant Sum Sc aleFig. 8.5

InstructionsOn the next slide, there are eight attributes ofbathing soaps. Please allocate 100 points amongthe attributes so that your allocation reflects therelative importance you attach to each attribute.The more points an attribute receives, the more

important the attribute is. If an attribute is not atall important, assign it zero points. If an attributeis twice as important as some other attribute, itshould receive twice as many points.


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2007 P ti H ll 8 25

Fig. 8.5 cont.

FormAverage Responses of Three Segments

AttributeSegment I Segment II Segment III

1. Mildness2. Lather3. Shrinkage4. Price5. Fragrance

6. Packaging7. Moisturizing8. Cleaning Power

Sum

8 2 4

2 4 173 9 753 17 9

9 0 197 5 9

5 3 2013 60 15

100 100 100

mpor ance o a ng oa p r u esUsing a Co nstan t Sum Sc ale

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Marketing Research Module 3 Scaling