Marketing Research - Measurement And Scaling

Front 1

Measurement

Back 1

assigning numbers to characteristics of objects according to prespecified rules

Front 2

Scaling

Back 2

creating a continuum upon which measured objects are located

Front 3

Description

Back 3

Nominal Scales
Unique labels or descriptors that are used to designate each value of the scale
All scales posses description

Front 4

Scale Characteristics

Back 4

Description
Order
Distance
Origin

Front 5

Order

Back 5

Ordinal Scales
relative sizes or positions of descriptors
denoted by greater than less than equal to

Front 6

Distance

Back 6

Interval
Absolute differences between the scale descriptors are known and may be expressed in units

Front 7

Origin

Back 7

The origin of characteristics means that the scale has a unique or fixed beginning or true zero point

Front 8

4 Primary Scales of Measurement

Back 8

Nominal
Ordinal
Interval
Ratio

Front 9

Nominal

Back 9

numbers serve as labels and tags
one to one correspondence
numbers don't reflect amount of characteristics

Front 10

Ordinal Scale

Back 10

ranking scale
which do you prefer better?
how many hours of hw did you do last night? more than ten, less than ten etc

Front 11

Interval Scale

Back 11

-Numerically equal distances on the scale represent equal values
-Zero Pt Not fixed
-Can do most statistical analysis. Even Mean.

Front 12

With an interval scale, can you say, people prefer the brand 2X as much?

Back 12

No

Front 13

Ratio Level

Back 13

Represents a real #
Fill in the blank

Constant Sum

Difficult because people are sensitive and can't remember data

Front 14

3 Comparative Scaling Techniques

Back 14

Rank Order
Paired Comparison
Constant Sum

Front 15

What's the formula for #'s of comparisons you need to make for Paired Comparison?

Back 15

[n(n-1)]/2

Front 16

Paired comparison

Back 16

Respondent is presented with two objects and asked to select one according to some criteria

Data is ordinal

Most widely used comparitive scaling technique

Possible to to convert data to a rank order

Front 17

Comparative vs Non Comparative

Back 17

Comparative compares two objects

Noncomparative - item is evaluated alone

Front 18

Rank Order Scaling

Back 18

respondents are presented with several objects simultaneously and asked to order or rank them

ordinal data

n-1 scaling decisions need to be made

Front 19

Constant Sum Scaling

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Respondents allocate a constant sum of units such as 100 points to attributes of a product to t reflect their importance.

can say 2x as important here

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What measurements can you get from nominal scales /

Back 20

%
mode

Front 21

What measurements can you get from Ordinal

Back 21

%
median

Front 22

What measurements can you get from Interval

Back 22

range, mean, st dev

Front 23

What measurements can you get from Ratio

Back 23

all, geometric mean

Front 24

Advantages of Comparative Scales

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smalle differences btw stimulus objects can be detected

same known reference pt 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

Front 25

disadvantages of comparative scales

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ordinal nature of data
inability to generalize beyond stimulus objects detected

Front 26

2 Non comparative rating scales

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Continuous

Non continuous

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Continuous Rating scale

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respondents rate the object by placing a mark at the appropriate position on a line that runs from one extreme of the criterion variable to the other

Front 28

Itemized Rating Scales 3

Back 28

Likert
Stapel
Semantic Differential

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Likert

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Most Common
Requires respondents to indicate a degree of agreement of disagreement with each of the series of statements about the stimulus objects


IE
Strongly Disagree
Disagree
Neither Agree/Disagree
Agree
Strongly Agree

Front 30

When do you reverse Scale

Back 30

Likert scales--- to check for reliability to make sure respondent is paying attention.... write reverse statements.

Front 31

Semantic Differential Scale

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7 or 5 * or 3 point rating scale with end points associated with bipolar labels that have semantic meaning

Sears is
POWERFUL ............ WEAK

Front 32

Stapel Scale

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Unipolar rating scale with ten categories numbered from -5 to +5 without a neutral point. This scale is usually presented vertically.

Kind of confusing

Front 33

Reliability Measures

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test-retest

alternative forms

internal consistency

split half reliability

coefficient alpha

Front 34

Validity Measures

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Criterion validity
Construct validity
Convergent validity
Discriminant validity
Nomological validity

Front 35

test retest reliability

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respondents are administered identical sets of scale items at two different times

degree of similarity btw the two is measured

Front 36

alternative forms reliability

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two equivalent forms of the scale are constructed and the same respondents are measured at two different times with a different form being measured each time

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internal consistency reliability

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determines the extent to which different parts of a summated scale are consistent in what they indicate about the characteristic being measured

Front 38

split half reliability

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the items on the scale are divided into two halves and the resulting half scores are correlated

Front 39

coefficient alpha

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or Cronbachs alpha is the average of all possible split-half coefficients resulting from different ways of splitting the scale items.

Front 40

criterion validity

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reflects whether a scale performs as expected in relation to other variables selected as meaningful criteria

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construct validity

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addresses the question of what construct or characteristic the scale is in fact measuring

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convergent

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construct validity
extent to which scale correlates positively with other measures of the same construct

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discriminant

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is the extent to which a measure does not correlate with other constructs from which it is supposed to differ

Front 44

nomological validity

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is the extent to which the scale correlates in theoretically predicted ways with measured but related constructs

Front 45

if a measure is perfectly valid, is it perfectly reliable?

Back 45

yes

Front 46

if a measure is unreliable, can it be valid?

Back 46

no
@ minimum, random error is present

Front 47

if a measure is reliable, can it be valid?

Back 47

no
systematic error