MKTG202 Marketing Research Group Report 代写

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MKTG202 Marketing Research Group Report 代写
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MKTG202 Marketing Research

Week 5

Measurement and Scales Planning your

Research Project

Progress Report B

Progress Report B ‐ Group Report on Quantitative

Research due: 23:59 Friday 15 September

Maximum 1000 words, not including

Headings

Meta‐data (authors' names & ID's)

Tables & Charts

Appendices (e.g. draft questionnaire)

References (if appropriate)

Group submission. Each group submits one file only in

DOC, DOCX, ODT, or RTF format (Please do not submit

in PDF format)

Suggested Structure

1. Summary of business problem and qualitative

research you have done in Week 2‐4

• brief review of your earlier research

MKTG202 Marketing Research Group Report 代写

2. Proposed Specific Quantitative Research Question

• What do want to find out?

3. Proposed 3‐ 5 Key Constructs to be measured

• Conceptual, Ostensive & Operational definitions

4. Proposed Sampling Method

• population of interest, sampling frame, why?

5. Example of what your answer will look like.

Marking Guide

CRITERION LESS THAN SATISFACTORY SATISFACTORY GOOD

Summary of Business

Problem and previous

qualitative findings

Vague or None qualitative stage linked to

proposed quantitative

study

Good summary qualitative

& links among problem,

qualitative and need for

further information

proposed here.

Quantitative Research

question or Research

hypothesis

Vague or None Research question should

be viable

Succinct, precise, and

workable research

question(s) and expected

outcomes.

Definitions of Key

constructs

Vague or None link between construct

definitions, research

question & expected

outcomes

Clear presentation of and

differentiation between

ostensive, constitutive and

operational definitions

Sampling No clear sampling

approach or with no

evaluation of the sampling

method adopted

Frame will probably get

the desired sample,

limited analysis of the

reason for adopting this

sampling method

Clear method for capturing

those people of interest

with a sound explanation

of the advantages/

disadvantages of the

sampling method

Example of what key

results may look like

None or Vague links between research

question(s) and likely

outcomes.

Clear connection between

question, operational

definitions & results.

Measurement and

Scales

30/08/2017

Scales

NOMINAL

ORDINAL

INTERVAL

RATIO

Scale development

The process of assigning a set of descriptors (label, rank,

number, score, etc.) to represent the range of possible

responses to a question about a particular construct

A scale is:

The combined set of points that anchor the measurement

tool

Levels of scales

• Nominal

• Ordinal

• Interval

• Ratio

Scale properties

Assignment

unique descriptors identify each object or level

Order

establishes ‘relative magnitudes’ between the descriptors,

creating hierarchical rank‐order relationships among objects

Distance

absolute differences between objects or levels

Origin

scale includes a ‘true natural zero’

Property Description and Examples

Assignment Unique descriptors to identify each object in a set

Examples:

numbers (10, 38, 44, 18, 23, etc.);

colors (red, blue, green, pink, etc.);

‘yes’ and ‘no’ for questions that place objects

into mutually‐exclusive groups

Order Establishes ‘relative magnitudes’ between the

descriptors, creating hierarchical rank‐order

relationships among objects

Examples:

First place is better than a fourth ‐ place finish;

this person is lighter than this other person

Property Description and Examples

Distance Express absolute differences between objects

Examples:

6 children is two more than 4 children;

30 o C is 10 degrees more than 20 o C

Origin Includes a ‘true natural zero’ or ‘true state of

nothing’

Examples:

weight or age;

times one shops at a supermarket;

6 children is 50% more than 4 children.

BUT:

30 o C is not 50% hotter than 20 o C because zero

point is arbitrary

30/08/2017

Relationships between Levels

of scales and scale properties

Yes Yes Yes Yes Ratio

No Yes Yes Yes Interval

No No Yes Yes Ordinal

No No No Yes Nominal

Origin Distance Order

Assign-

ment

Level of

Scale

Scale Properties

Statistical analysis of scales

Type of scale Numerical operation Descriptive statistics

Nominal Counting Frequency

Percentage

Mode

Ordinal Rank ordering Median

Range

Percentile ranking

Interval Operations that

preserve order and

relative magnitude

Mean

Standard deviation

Variance

Ratio Operations on actual

quantities

Geometric mean

Coefficient of

variation

Note: all statistics appropriate for lower‐order scales are also appropriate for

higher‐order scales (nominal is lowest, ratio is highest)

Examples of nominal scale

Example 1:

Please indicate your current marital status.

__ Married __ Single __ Single never married __ Widowed

Example 2:

Do you like or dislike chocolate ice cream?

____ Like ____ Dislike

Example 3:

Please check those health care practitioner (HCP) service areas in which you have had a

telephone conversation with a HCP representative in the past six months. (Check as many as

apply.)

____ Appointments ____ Treatment at home ____ Referral to other HCP

____ Prescriptions ____ Medical test results ____ Hospital stay

Some other service area(s); Please specify ____________________________________

Example of ordinal scales

Please rank the following characteristics of the cellular

phone service (1 is most important and 6 is the least

important, no ties allowed)

____ Total cost of service

____ Reception clarity

____ Low fixed cost

____ Reliability of service

____ 24‐hour customer service

____ Size of local coverage area

Examples of interval scales

Temperature:

Centigrade (Celsius)

Ice to steam: 0° – 100°

Fahrenheit

Ice to steam: 32° – 212°

Kelvin

Ice to steam: ‐273.15° – 373.15°

Note that Zero is arbitrary in C & F scales.

Examples of interval scales

Strongly

Disagree

Somewhat

disagree

Neither

agree or

disagree

Somewhat

agree

Strongly

agree

1 2 3 4 5

Technically, a Likert scale is an Ordinal scale, but it tends to be

treated as an Interval scale with few problems

30/08/2017

Examples of ordinally interval scales

Statement Definitely

agree

Generally

agree

Slightly

agree

Slightly

disagree

Generally

disagree

Definitely

disagree

It is good to have charge

accounts 6 5 4 3 2 1

I buy many things with a

bank (or credit) card 6 5 4 3 2 1

I like to pay cash for

everything 6 5 4 3 2 1

I buy at department

stores 6 5 4 3 2 1

I wish my family had a lot

more money 6 5 4 3 2 1

For each of the following statements, please circle the response that best expresses the extent

to which you either agree or disagree with that statement

Is there any real difference?

Agree Disagree

1 2 3 4 5

Disagree Agree

‐2 ‐1 0 1 2

Disagree Agree

‐4 ‐2 0 2 4

Zero Point and Units

of measurement are

arbitrary in an

Interval Scale

Examples of ratio scales

1. Please circle the number of children under 18 years

of age currently living in your household.

0 1 2 3 4 5 6 7

(If more than 7, please specify: ____)

2. In the past seven days, how many times did you go

shopping at a retail shopping mall?

____ # of times

3. In whole years, what is your current age?

____ # years old

Interval scales?

1. Approximately how many times has ‘your’ bank

charged you for an overdrawn account in the past

year?

___ None __ 1–2 __ 3–7 ___ 8–15 ___ 16–25 ___ More than 25

2. Approximately how long have you lived at your

current address?

____<1 year ____1‐3 years ____4‐6 years ____7‐10 years

____11‐20 years ____ >20 years

3. In which of the following categories does your

current age fall?

____Under 18 ____18‐25 ____26‐35 ____36‐45 ____46‐55

____56‐65 ____Over 65

Attitude

Measurement

Attitude measurement

Attitudinal measurement is difficult because it deals

with:

People’s thoughts, feelings, intended behaviours and

characteristics

The features or attributes of objects

Concepts and ideas

An attitude is a learned predisposition to react in some

consistent manner

To measure attitudes, researchers may use one of (1) the

trilogy, (2) attitude‐towards‐object, or (3) the affect global

approach

30/08/2017

Trilogy (tri‐part) three‐part

Cognition

• Thoughts & Beliefs (measure what you know)

• A person’s information about an object, e.g., recall of

laptop brand names

Affect

• Feelings (measure how you feel)

• Summarizes overall feelings towards an object, e.g., like

or dislike for a laptop brand

Connation

• Actions (or tendency towards action) (measure what you

do, or want to do)

• Expectations of future behaviour toward an object, e.g.,

likelihood to purchase a laptop brand

Attitude‐towards‐object

See appendix to Chapter 8

Attitude is sum of perceptions of the components of an

object or action, weighted by the relative importance

of each component.

? ? ? ? ? ? ? ?

???

Attitude as an affect‐global measure

“Thinking about the upcoming election,

overall, how strongly do you favour the

Nasty Party over the Stupid Party?”

Likert Scale

Ordinal scale that asks respondents to indicate the

extent to which they agree or disagree with a series of

mental or behavioural beliefs about a given object

Initially, five scale descriptors were used:

Strongly agree

Agree

Neither agree nor disagree

Disagree

Strongly disagree

Likert Scales

& related rating scales

Likert Scale

A modified Likert scale expands this set to six or seven

categories.

Characteristics of the Likert scale include:

• Only summated rating scale that uses a set of

agree/disagreement scale descriptors

• Measures cognitive components; does not measure

affective or conative components

• Best utilised when self‐administered surveys or personal

interviews are used to collect data

30/08/2017

Involvement = level of personal

importance

Cognitive

component

Affective

component

Important 

Relevant 

Means a lot to me 

Valuable 

Needed 

Interesting 

Exciting 

Appealing 

Fascinating 

Zaichkowsky, J.L. (1994) The Personal Involvement Inventory: Reduction, Revision, and Application

to Advertising. Journal of Advertising (December)

Zaichkowsky PII 2

Strongly

agree

Agree Neither

agree nor

disagree

Disagree Strongly

disagree

Important

Relevant

Means a

lot to me

Valuable

Needed

Interesting

Exciting

Appealing

Fascinating

Zaichkowsky, J.L. (1994) The Personal Involvement Inventory: Reduction, Revision, and Application

to Advertising. Journal of Advertising (December)

Example: Personal Importance of Laptop Computer

Strongly

agree

Agree Neither

agree nor

disagree

Disagree Strongly

disagree

Important 

Relevant 

Means a

lot to me



Valuable 

Needed 

Interesting 

Exciting 

Appealing 

Fascinating 

Zaichkowsky, J.L. (1994) The Personal Involvement Inventory: Reduction, Revision, and Application

to Advertising. Journal of Advertising (December)

Example: Personal Importance of Laptop Computer

Strongly

agree

Agree Neither

agree nor

disagree

Disagree Strongly

disagree

Important  5

Relevant  4

Means a

lot to me

 4

Valuable  5

Needed  5

Interesting  3

Exciting  2

Appealing  3

Fascinating  1

Zaichkowsky, J.L. (1994) The Personal Involvement Inventory: Reduction, Revision, and Application

to Advertising. Journal of Advertising (December)

Example: Personal Importance of Laptop Computer

Cognitive

component

Affective

component

Important  5

Relevant  4

Means a lot to me  4

Valuable  5

Needed  5

Interesting  3

Exciting  2

Appealing  3

Fascinating  1

Total 23 (mean = 4.6) 9 (mean = 2.25)

Zaichkowsky, J.L. (1994) The Personal Involvement Inventory: Reduction, Revision, and Application to Advertising. Journal of Advertising (December)

Using the mean of

several related

items dampens out

random error in

responses.

We do not use the

scores for individual

items, only the scale

scores (averages)

Modified Likert Scale

___ ___ ___ ___ ___ ___

I am never

influenced by

advertisements.

___ ___ ___ ___ ___ ___

My friends often

come to me for

advice.

___ ___ ___ ___ ___ ___

I wish we had a

lot more money.

___ ___ ___ ___ ___ ___

I buy many

things with a

credit card.

Definitely

Disagree

Generally

Disagree

Slightly

Disagree

Slightly

Agree

Generally

Agree

Definitely

Agree Statements

For each of the listed statements, please check the one response that best

expresses the extent to which you agree or disagree with that statement.

30/08/2017

Example: Semantic Differential

Scale

Attractiveness:

Sexy ___ ___ ___ ___ ___ ___ Not sexy

Beautiful ___ ___ ___ ___ ___ ___ Ugly

Attractive ___ ___ ___ ___ ___ ___ Unattractive

Classy ___ ___ ___ ___ ___ ___ Not classy

Elegant ___ ___ ___ ___ ___ ___ Plain

Example:

Behavioural Intention Scale

Type

of Event

Definitely

would

consider

attending

Probably

would

consider

attending

Probably

would not

consider

attending

Definitely

would not

consider

attending

Music

Concerts

Popular

Music

Jazz Music

Country

Music

Classical

Music

Chamber

Music

Juster 11‐Point Probability Scale:

A predictive measure of future intentions

In 1966, F. Thomas Juster argued

that, since verbal intentions are

simply disguised probability

statements, then why not directly

capture the probabilities

themselves as measured by the

respondents?

Estimates the average probability

that a population will do

something by a future time. The

mean response estimates the

proportion of the population that

will perform the action.

Score Verbal equivalent

0 No chance, almost no chance (1 in 100)

1 Very slight possibility (1 in 10)

2 Slight possibility (2 in 10)

3 Some possibility (3 in 10)

4 Fair possibility (4 in 10)

5 Fairly good possibility (5 in 10)

6 Good possibility (6 in 10)

7 Probable (7 in 10)

8 Very probable (8 in 10)

9 Almost sure (9 in 10)

10 Certain, practically certain (99 in 100)

“On a scale of 0 – 10 where 0 indicates no chance and 10

indicates certainty, what is the chance that you will buy a

laptop computer before the end of the year?”

Score Verbal equivalent

0 No chance, almost no chance (1 in 100)

1 Very slight possibility (1 in 10)

2 Slight possibility (2 in 10)

3 Some possibility (3 in 10)

4 Fair possibility (4 in 10)

5 Fairly good possibility (5 in 10)

6 Good possibility (6 in 10)

7 Probable (7 in 10)

8 Very probable (8 in 10)

9 Almost sure (9 in 10)

10 Certain, practically certain (99 in 100)

Averaged over a

representative

population, Juster

Scale shown to be

very accurate

measure of future

behaviour!

Other rating scales: slider…

A. Graphic Rating Scales Usage (Quantity Descriptors):

Never

Use

Use All

the Time

0 10 20 30 40 50 60 70 80 90 100

Other rating scales: smiley…

B: Smiling Face Descriptors:

1 2 3 4 5 6 7

30/08/2017

Other rating scales: annotated rating

scale…

1 2 3 4 5 6 7

C. Performance Rating Scales

Performance Level Descriptors:

Truly

Terrible

Poor Fair Average Good Excellent

Truly

Exceptional

Recap of key measurement design

issues

Construct development issues

Scale issues

Screening questions

Skip question

Ethical responsibility

Rules of thumb for scale

development

• Are the questions intelligible?

• Are the scale descriptors appropriate?

• Do the scale descriptors have discriminatory power?

• Are the scales reliable?

• Are the scales balanced appropriate to the research

endeavour?

• A neutral response option, where relevant and

applicable?

• What measures of central tendency apply?

• What measures of dispersion apply?

Scale Reliability & Validity

Three criteria for good measurement

Reliability

The degree to which measures are free from random error

and therefore yield consistent results.

Validity

The ability of a scale to measure what was intended to be

measured.

Sensitivity

The ability to accurately measure variability in stimuli or

responses.

Reliability

Applies to a measure when similar results are obtained

over time and across situations.

For example, Tailor measuring with a tape measure obtains a

true value of length repeatedly.

Two dimensions: repeatability and internal consistency

Test‐retest method used to determine repeatability by

administering the same scale at two separate points in

time to test for stability.

30/08/2017

Validity

To measure what we intend to measure.

For example, Say we want to measure students’ ability to

understand statistics …

Construct:

Understanding of Statistics

Operationalisation: (Measurement)

Quiz which focuses on memorising formulae and doing arithmetic.

Valid? Why?

Not Valid!

Memorising formulae and

doing arithmetic has very

little to do with

understanding Statistics!

Validity

Three approaches to establishing validity:

Face or content validity

Criterion validity

Construct validity

Establishing validity

Face or content validity

professional agreement that a scale’s content logically

appears to accurately reflect what was intended to be

measured.

Criterion validity

the ability of a measure to correlate with other standard

measures of the same construct or established criterion.

Construct validity

the ability of a measure to provide empirical evidence

consistent with a theory–based concept.

Face/Content validity for

Personal Involvement Inventory?

MKTG202 Marketing Research Group Report 代写

Important

Boring

Irrelevant

Unexciting

Appealing

Mundane

Worthless

Not needed

Involving

Means a lot

Any other items that should

be on the list?

• JUDGE OTHER PEOPLE BY

THEIR BRAND

• EXPRESSION OF SELF

• WANT TO HAVE

• MUST HAVE

Face Validity ‐ are the items telling you

the same thing?

Content Validity – is there any missing

part of the measure to depict the fact?

Construct Validity for PII?

Important

Boring

Irrelevant

Unexciting

Appealing

Mundane

Worthless

Not needed

Involving

Means a lot

Predicting events, or association with

other attitudes or behaviour?

• PURCHASE LEVEL

• PRICE ELASTICITY (WILLING TO

PAY MORE)

• JUDGING OF SELF AND OTHERS

• DETAILED KNOWLEDGE OF

BRANDS AND APPLICATIONS

Construct Validity – the degree to

which a test measures what it claims

to be measuring

Criterion Validity for PII?

Important

Boring

Irrelevant

Unexciting

Appealing

Mundane

Worthless

Not needed

Involving

Means a lot

• KNOWLEDGE ABOUT PRODUCT