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MARKET RESEARCH
1
Paul Marx
DISCLAIMER
This Presentation may contain
Copyrighted Material,
DO NOT DISTRIBUTE
2
THE MOST IMPORTANT SKILLS IN MARKETING
3
Source: “7 Habits of Effective Marketing Organizations”, Eloqua (2010)
COURSE OBJECTIVES
• Understand the role of marketing research in shaping
managerial decisions
• Get an overview of classical activities in as well as
of practical tools and methods of marketing research
• Be able to implement marketing research studies,
analyze and interpret data, and present the results
4
5
RECOMMENDED READING
Malhotra, Naresh K. (2009), “Marketing Research: An Applied Orientation”,
6th edition, Prentice Hall
Myers, James H. (1996), “Segmentation & Positioning for Strategic
Marketing Decisions”, South-Western Educational Pub
Hair, Joseph F. Jr, William C. Black, Barry J. Babin, and Rolph E.
Anderson (2009), “Multivariate Data Analysis”, 7th edition,
Prentice Hall
NICE TO HAVE (READ)
6
Kotler, Philip and Gary Armstrong (2009), “Principles of Marketing”, 13th edition, Prentice Hall
Cravens, David and Nigel Piercy (2012), “Strategic Marketing”, 10th edition, McGraw-Hill/Irwin
Wedel , Michel, and Wagner A. Kamakura (2000), “Market Segmentation: Conceptual and
Methodological Foundations”, 2nd edition, Kluwer Academic Publishers
Brunner, Gordon C. II (2012), “Marketing Scales Handbook: A Compilation of Multi-Item Measures for
Consumer Behavior & Advertising Research”, Vol. 6, available as PDF at www.marketingscales.com
Hoyer, Wayne D., Deborah J. MacInnis (2008), “Consumer Behavior”, South-Western College Pub; 5
edition
Ariely, Dan (2010), “Predictably Irrational: The Hidden Forces That Shape Our Decisions”, revised and
expanded edition, Harper Perennial
Coe, John (2003), “The Fundamentals of Business-to-Business Sales & Marketing”, McGraw-Hill
CONTENTS IN BRIEF
1. Introduction
1.1. Marketing Research
1.2. Types of Market Research
1.3. Research Methods
2. Qualitative Research Methods
2.1. Focus Groups
2.2. Depth Interview
2.3. Projective Techniques
2.4. Comparison of Qualitative Techniques
3. Observation Methods
4. Survey: Measurement and Scaling
4.1. Intorduction
4.2. Comparative Scales
4.3. Non-comparative Scales
4.4. Multi-item Scales
4.5. Reliability and Validity
5. Questionnaire
5.1. Asking Questions
5.2. Overcoming Inability to Answer
5.3. Overcoming Unwillingness to Answer
5.4. Increasing Willingness of Respondents
5.5. Determining the Order of Questions
5.6. What’s Next?
6. Sampling
6.1. Non-probability Sampling
6.2. Probability Sampling
6.3. Choosing Non-Probability vs. Probability Sampling
6.4. Sample Size
7. Data Analysis:
A Concise Overview of Statistical Techniques
7.1. Descriptive Statistics:
Some Popular Displays of Data
7.1.1. Organizing Qualitative Data
7.1.2. Organizing Quantitative Data
7.1.3. Summarizing Data Numerically
7.1.4. Cross-Tabulations
7.2. Inferential Statistics:
Can the results be generalized to population?
7.2.1. Hypothesis Testing
7.2.2. Strength of a Relationship in Cross-Tabulation
7.2.3. Describing the Relationship Between
Two (Ratio Scaled) Variables
8. Advanced Techniques of Market Analysis:
A Brief Overview of Some Useful Concepts
8.1. Conjoint Analysis
8.2. Market Simulations
8.3. Market Segmentation
8.4. Perceptual Positioning Maps
9. Reporting Results
7
8
1.Introduction
1.1.Marketing Research
1.2.Types of Market Research
1.3.Research Methods
CASE BEECHCRAFT STARSHIP
9
First civilian aircraft with
- carbon fiber composite airframe
- canard (“duck”) design
- L-shaped wings with rudders in them
- Two turbo-prop engines mounted aft to pull
- R&D costs est. $500Mio
“For the pilot and passengers, it has really got everything...
...for the money, the performance just isn’t there...
...for $5Mio, you can buy a jet. Starship just doesn’t fit in today’s market”1
“The Starship was a $500Mio mistake because of a
lack of marketing research”2
1
Dennis Murphy, a sales person at Elliot Flying Services in Des Moines, Iowa
2
Russel Munson in “The Stock Market”, 1991
CASE ELECTROLUX
10
Electrolux - a scandinavian manufacturer of inexpensive vacuum cleaners - took its rhyming
phrase “Nothing Sucks Like an Electrolux” and brought it in the early 1970s to America from
English-speaking markets overseas. They didn’t know that the word “sucks” had become a
derogatory word in the US.
CASE AMERICAN AIRLINES
11
American Airlines launched a new
leather first class seats ad campaign
(1977-78) in the Mexican market:
"Fly in Leather" (vuela encuero)
meant "Fly Naked"
CASE FOOD & BEVERAGES
12
In what must be one of the most bizarre
brand extensions ever Colgate decided to
use its name on a range of food products
called Colgate's Kitchen Entrees. Needless
to say, the products did not take off and
never left U.S. soil. The idea must have been
that consumers would eat their Colgate
meal, then brush their teeth with Colgate
toothpaste. The trouble was that for most
people the name Colgate does not exactly
get their taste buds tingling.
In the 1970s and early 80s, Coke began to
face stiff competition from other soft drink
producers. To remain in the number one
spot, Coke executives decided to cease
production on the classic cola in favor of New
Coke. The public was outraged, and Coca-
Cola was forced to re-launch its original
formula almost immediately. Lesson learned
-- don't mess with success.
Cocaine is a high-energy drink, containing
three and a half times the amount of
caffeine as Red Bull. It was pulled from U.S.
shelves in 2007, after the FDA declared that
its producers, Redux Beverages, were
"illegally marketing their drink as an
alternative to street drugs." The drink is still
available, however, online, in Europe and
even in select stores in the U.S. Despite the
controversy, Redux Beverages does not plan
to cease production any time soon. You
know what they say -- there's no such thing
as bad publicity.
RETURNS ON MARKETING ACTIONS
• 60-95% of new products fail
• 50% of advertising has no effect
• 85% of price promotions loose money
• 97% brands create 37% $ (Unilever)
13
14
• Marketing Research is there to prevent such things
from happening
RECALL
Marketing
Marketing consists of the strategies and tactics used to identify, create and maintain satisfying
relationships with customers that result in value for both the customer and the marketer.
Marketing Concept
A business philosophy based on consumer orientation, goal orientation, and systems orientation.
Consumer Orientation
Identification of and focus on the people or firms most likely to buy a product and production
of a good or service that will meet their needs most effectively.
Goal Orientation
A focus on the accomplishment of corporate goals; a limit set on consumer orientation.
Systems Orientation
Creation of systems to monitor the external environment and deliver the marketing mix to
the target market.
Marketing Mix (a.k.a. 4Ps/Cs and 7Ps Models)
The unique blend of product, pricing, promotion, offerings, and distribution designed to meet the
needs of a specific group of consumers.
15
MARKETING: A VERY PARSIMONIOUS OVERVIEW
16
Quality
Satisfaction
Profit
Needs
Wants
Preferences
Utility function
Attitudes
Intentions
Motives
Involvement
Beliefs
Emotions
Lifestyle
Habits
Buying behavior
...
Trust & Loyalty
Consumer
17
1.Introduction
1.1.Marketing Research
1.2.Types of Market Research
1.3.Research Methods
MARKETING RESEARCH: A CONCISE DEFINITION
Marketing Research
The planning, collection, and analysis of data relevant
to marketing decision making and the communication
of the results of this analysis to management.
18
19
Why marketing research?
THE IMPORTANCE OF MARKETING RESEARCH
20
Improve quality of
decision making
Trace Problems
Focus on keeping
existing customers
Understand changes
in marketplace
MARKET RESEARCH VS. MARKETING RESEARCH
(STRICTLY SPEAKING...)
21
Market Research
Marketing Research
Researching the immediate competitive environment of
the marketplace, including customers, competitors,
suppliers, distributors and retailers
Includes all the above plus:
- companies and their strategies for products and
markets
- the wider environment within which the firm operates
(e.g., political, social, etc)
TOP 10 MARKET RESEARCH ACTIVITIES
22
Market measurement 18%
New Product development / concept testing 14%
Ad or Brand awareness monitoring / tracking 13%
Customer satisfaction (incl. Mystery Shopping) 10%
Usage and Attitude studies 7%
Media research & evaluation 6%
Advertising development and pre-testing 5%
Social Surveys for central/local governments 4%
Brand/corporate reputation 4%
Omnibus studies 3%
Source: Business Management Research Associates, Inc.
MARKET RESEARCH PROCESS
23
Define the
research
problem
Decide on
budget
data sources
research
approaches
sampling plan
contact methods
methods of data
analysis
Develop the
research plan
Collect
data
Analyze
data
Report
findings
identify and clarify
information needs
define research
problem and
questions
specify research
objectives
confirm
information value
collect data
according to the
plan or
employ an
external firm
The plan needs to be
decided upfront but
flexible enough to
incorporate changes
or iterations
This phase is the most
costly and the most
liable to error
If a problem is vaguely
defined, the results
can have little bearing
on the key issues
Overall conclusions
to be presented
rather than
overwhelming
statistical
methodologies
Formulate
conclusions and
implications from
data analysis
prepare finalized
research report
Analyze data
statistically or
subjectively
and infer answers
and implications
1 2 3 4 5
Type of data analysis
depends on type of
research
Comments
Contents
WHEN NOT TO CONDUCT MARKET RESEARCH
24
Occasion Comments
Lack of resources
If quantitative research is needed, it is not worth doing unless a
statistically significant sample can be used. When funds are
insufficient to implement any decisions resulting from the research.
Closed mindset
When decision has already been made. Research is used only as a
rubber stamp of a preconceived idea.
Information not needed When decision-making information already exists.
Vague objectives
When managers cannot agree on what they need to know to make a decision.
Market research cannot be helpful unless it is probing a particular issue.
Results not actionable
Where, e.g., psychographic data is used which will not help he
company form firm decisions.
Late timing When research results come too late to influence the decision.
Poor timing
If a product is in a “decline” phase there is little point in
researching new product varieties
Costs outweigh benefits
The expected value of information should outweigh the costs
of gathering an analyzing the data.
25
1.Introduction
1.1.Marketing Research
1.2.Types of Market Research
1.3.Research Methods
TYPES OF MARKET RESEARCH
26
By Objectives By Data Source By Methodology
Exploratory
(a.k.a. diagnostic)
Descriptive
Causal
(a.k.a. predictive,
experimental)
Qualitative
Quantitative
Primary
Secondary
Exploratory
(a.k.a. diagnostic)
Explaining data or actions to help define the problem
What was the impact on sales after change
in the package design?
Do promotions at POS influence brand awareness?
MARKET RESEARCH BY OBJECTIVES
27
Descriptive
Gathering and presenting factual statements:
who, what, when, where, how
What is historic sales trend in the industry?
What are consumer attitudes toward our product?
Causal
(a.k.a. predictive,
experimental)
Probing cause-and-effect relationships; “What if?”
Specification of how to use the research to predict
the results of planned marketing decisions
Does level of advertising determine level of sales?
small scale
surveys, focus
groups,
interviews
larger scale
surveys,
observation,
etc.
experiments,
consumer
panels
ProblemIdentificationProblemSolving
Uncertaintyinfluencesthetypeofresearch
UNCERTAINTY SHAPES THE TYPE OF RESEARCH
28
Problem Identification
Research
Problem Solving Research
Market Potential Research
Market Share Research
Image Research
Market Characteristics
Research
Sales Analysis Research
Forecasting Research
Business Trends Research
Segmentation Research
Product Research
Pricing Research
Promotion Research
Distribution Research
Exploratory
research
Descriptive
research
Causal
research
AwareUncertain Certain
degree of problem/decision certainty
MARKET RESEARCH BY DATA SOURCE
29
Primary
Secondary
Original research to collect new raw data for a
specific reason. This data is then analyzed and may
be published by the researcher.
Research data that has been previously collected,
analyzed and published in the form of books,
articles, etc.
SECONDARY DATA: PROS-AND-CONS
30
Secondary
Data
Advantages Disadvantages
Saves time and money if on
target
Aids in determining direction for
primary data collection
Pinpoints the kinds of people to
approach
Serves as a basis for other data
May not give adequate
detailed information
May not be on target with the
research problem
Quality and accuracy of data
may pose a problem
Information previously collected for any purpose other than the one at hand
PRIMARY DATA: PROS-AND-CONS
31
Advantages Disadvantages
Answers a specific research
question
Data are current
Source of data is known
Secrecy can be maintained
Expensive
“Piggybacking” may confuse
respondents
Quality declines in interviews
are lengthy
Reluctance to participate in
lengthy interviews
Primary
Data
Information collected for the first time to solve the particular
problem under investigation
Disadvantages are usually offset by the advantages
of primary data
Exploratory
research
Causal
research
Descriptive
research
MARKET RESEARCH BY METHODOLOGY
32
Qualitative
Involves understanding
human behavior and the
reasons behind it
Focus is on individuals and
small groups
Objectivity is not the goal,
the aim is to understand one
point of view, not all points
of view.
Primary
Data
Secondary
Data
Quantitative
Involves collecting and
measuring data
Often requires large data
sets. For example, large
number of people.
Uses statistical methods to
analyze data
Aims to achieve objective/
scientific view of the subject
33
1.Introduction
1.1.Marketing Research
1.2.Types of Market Research
1.3.Research Methods
RESEARCH METHODOLOGY
34
research
methodology
The searching for and gathering of
information and ideas in response
to a specific question
The set of methods used to
address a specific research
problem at hand
MARKET RESEARCH METHODS
35
Primary
Secondary
Research
Approach
Society
Groups
Individuals
Research
Source
Library
Web
Database
Archive
Survey
Focus Group
Depth Interview
Projective Tech.
Observation
Research
Method
Literature
review
SOURCES OF SECONDARY DATA
Internal Corporate Information
Government Agencies
Trade and Industry Associations
Business Periodicals
News Media
Databases
Internet Sources
…
36
Secondary
Data
Secondary
Data
EVALUATING SECONDARY DATA SOURCES
37
Use the C.R.A.P. test
Currency
Reliability
Authority
Purpose
Secondary
Data
EVALUATING DATA SOURCES
38
Currency How recent is the information?
Are there more recent updates available?
Is it current enough for your topic?
Reliability
Is content of the resource primarily opinion?
Is it balanced and evidenced?
Does the creator provide references or sources for the
data?
Authority
Who is the creator or author?
What are his/her credentials?
Is s/he an expert?
Who is the publisher os sponsor? Are they reputable?
Purpose /
Point of View
Is it promotional or educational material?
Are there advertisements on the website?
is this fact or opinion?
Who is the intended audience?
39
Quantitative
Survey
Focus Groups
Depth Interview
Projective Techniques
Observation
Qualitative
Primary
Approaches
Survey
Observation
Depth Interview
Projective Tech.
Focus Groups
Survey
Observation
40Robson (1998), Visocky & Visocky (2009)
APPARENT
TRUTH
Literature Review
InterviewSurvey
Triangulation
The combination of
methods in the study
of the same topic
BUT IT IS
MESSIER
THAN THAT
42
2.Qualitative Research Methods
2.1. Focus Groups
2.2. Depth Interview
2.3. Projective Techniques
2.4. Comparison of Qualitative Techniques
43
2.Qualitative Research Methods
2.1. Focus Groups
2.2. Depth Interview
2.3. Projective Techniques
2.4. Comparison of Qualitative Techniques
FOCUS GROUPS
44
Focus Groups
organized discussions with a moderator
and limited number of participants
qualitative method to gain insights
from the appropriate target consumers
through studying their perceptions,
opinions, beliefs, and attitudes
moderator should remain neutral, ask
open ended questions, speak only
when necessary and record session
lasts between 1.5 and 2 hours
Focus Groups
FOCUS GROUPS
45
I’d like to speak
with you all about your
opinions on...
------, -----. ----- !
------.
-----?
------!
----. ----?
http://www.youtube.com/watch?v=POF3m6ZNoiY
http://www.youtube.com/watch?v=cnV1pS7qVD8
APPLICATIONS OF FOCUS GROUPS
46
Understanding consumers’ perception, preferences, and behaviors
concerning a product category
Obtaining impressions of new product concepts
Generating new ideas about older products
Developing creative concepts and copy material for advertisements
Securing price impressions
Obtaining preliminary consumer reaction to specific marketing programs
...
FOCUS GROUPS: ADVANTAGES
47
I’d like to know what you all
think about English Immersion. Do
you think we should have more, less
or the same amount of it?
More but not too
much more!
About the
same. I guess.
Less. I think.
Less, definitely.
Interesting.
Why do you all
disagree?
No, no!
Less!
FOCUS GROUPS: ADVANTAGES
48
I’d like to know what you all
think about English Immersion. Do
you think we should have more, less
or the same amount of it?
More but not too
much more!
About the
same. I guess.
Less. I think.
Less, definitely.
Interesting.
Why do you all
disagree?
No, no!
Less!
Ability to ask
many people
about “why”
Ability to observe
and
de-code
disagreements
Can learn how
groups make
sense of the
topic
Synergism
Snowballing
Stimulation
Security
Spontaneity
Serendipity
FOCUS GROUPS: DISADVANTAGES
49
Great. Thank you, Carl.
Anyone else?
And another one issue I’d l
I really hate how high gas
prices are!
Oh, and don’t
get me started
about the GST!
Ok, Carl.
Thanks.
FOCUS GROUPS: DISADVANTAGES
50
Great. Thank you, Carl.
Anyone else?
And another one issue I’d l
I really hate how high gas
prices are!
Oh, and don’t
get me started
about the GST!
Ok, Carl.
Thanks.
Difficulty in
getting people in
the same room
Difficulty
controlling
conversations
Huge amount of
data
Dominant
personalities
Misuse
Misjudge
Messy
Misrepresentation
FOCUS GROUPS: PROS-AND-CONS
51
Advantages Disadvantages
ability to ask many people about
“why”
ability to observe and de-code
disagreements
can learn how groups make
sense of the topic
synergism
snowballing
stimulation
security
spontaneity
serendipity
difficulty in getting people in
the same room
difficulty controlling
conversations
dominant personalities
huge amount of data
misuse
misjudge
messy
misrepresentation
FOCUS GROUPS: SIZE AND WHOM TO RECRUIT
52
Typically
6-10
(Morgan 1998)
8-10
(Malhotra 2004)
Small (4-5) when
there’s lots to say
or a controversity
Large (20+) when
opinions are
likely brief
homogenous in terms
of target group
characteristics
(demographics, socio-
economics…)
experienced with the
issue
have not participated
in many focus groups
HOW TO DO FOCUS GROUPS
53
Plot test
interview
guide
General
research
questions
Write
interview
guide
Determine
size of
group
Decide
participant
qualities
Secure
facility and
moderator
Recruit
Notes by
separate
note taker
Conduct
focus
group
Interpret
data
Conceptual
and
theoretical
work
Write up
findings
Recording
and/or
video
Transcript
Collection
of more
data
Tighter
specification
of question
INTERNET FOCUS GROUPS
54
INTERNET FOCUS GROUPS: ADVANTAGES
55
Geographical
constraints are
removed
Time constraints
are lessened
Ability to reach
hard-to-reach
target groups
Ability to
recontact
respondents
No travel costs,
No videotaping,
No facilities to
arrange
INTERNET FOCUS GROUPS: DISADVANTAGES
56
Difficulty
ensuring the
person is in the
target group
Lack of control
over
environment and
distraction
Only intangible
stimuli
Only experienced
PC users
Not suitable for
highly emotional
issues
INTERNET FOCUS GROUPS
57
Advantages Disadvantages
geographical and time
constraints are removed or
lessened
ability to recontact respondents
ability to reach hard-to-reach
segments
lower costs
only experienced PC users can
be surveyed
hard to ensure that a person is
a member of a target group
lack of control over
respondent’s environment and
distracting external factors
products cannot be touched or
smelled
inability to explore highly
emotional issues or subject
matters
INTERNET FOCUS GROUPS: USES
58
Banner ads,
Copy testing,
Concept testing,
Usability testing
esp. suitable for
companies in the
online business
Multimedia
evaluation;
Comparisons of
icons or graphics
59
2.Qualitative Research Methods
2.1. Focus Groups
2.2. Depth Interview
2.3. Projective Techniques
2.4. Comparison of Qualitative Techniques
DEPTH INTERVIEW
60
Depth Interview
method for in-depth probing
of personal opinions, beliefs, and
values
interview is conducted one-on-one
lasts between 30 and 60 minutes
unstructured (or loosely structured)
data is obtained from a relatively
small group of respondents
data is not analyzed with inferential
statistics
Depth Interview
DEPTH INTERVIEW: TECHNIQUES
61
Laddering start with questions about external objects and external
social phenomena,
then proceed to internal attitudes and feelings
Critical
Incident
Technique
(CIT)
A critical incident is one that makes a significant
contribution - either positively or negatively - to an activity
or phenomenon.
respondents are asked to tell a story about an experience
they have had
Symbolic
Analysis
attempts to analyze the symbolic meaning of objects by
comparing them with their opposites
e.g. product non-usage, opposite types of products
Hidden Issue
Questioning
the focus is not on socially share values but rather on
personal “sore spots” and “pet peeves”;
not on general lifestyles but on deeply felt personal
concerns
EXAMPLE: LADDERING
62
Laddering start with questions about external objects and external
social phenomena,
then proceed to internal attitudes and feelings
Wide body aircraft
I can get more work done
I accomplish more
I feel good about myself
product characteristic
user characteristic
Advertisement message:
You will feel good about
yourself when flying our
airline. “You’re The Boss”
Hidden Issue
Questioning
the focus is not on socially share values but rather on
personal “sore spots” and “pet peeves”;
not on general lifestyles but on deeply felt personal
concerns
EXAMPLE: HIDDEN ISSUE QUESTIONING
63
fantasies, work lives, and
social lives
historic, elite, masculine-
camaraderie, competitive
activities
Advertisement theme:
Communicate aggressiveness,
high status, and competitive
heritage of the airline.
Symbolic
Analysis
attempts to analyze the symbolic meaning of objects by
comparing them with their opposites
e.g. product non-usage, opposite types of products
EXAMPLE: SYMBOLIC ANALYSIS
64
“What would it be like if you
could no longer use
airplanes?”
“Without planes I would have
to rely more on e-mails,
letters, and long-distance
calls”
Advertisement theme:
The airline will do the same
thing for a manger as Federal
Express does for package.
Airlines sell to the managers
face-to-face communication
Critical
Incident
Technique
(CIT)
A critical incident is one that makes a significant
contribution - either positively or negatively - to an activity
or phenomenon.
respondents are asked to tell a story about an experience
they have had
EXAMPLE: CRITICAL INCIDENT TECHNIQUE
65
“What was the worst thing
you ever experienced with
airlines?”
“The snoring guy to m
y left
who was staring onto my
shoes right after he was
awake”
Lack of privacy
66
Do you go to the cinema?
Yes No, no cinema at all
What cinema do you usually/most frequently go to?
CINESTAR?
Yes No
Do you remember any
particular positive or negative
experience regarding
CineStar?
What do you like about the
CineStar (better than other
theaters)?
What don’t you like that much?
In overall, how often do you go to the cinema?
INQUIRE UNTIL THE RESPONDENT IS OUT OF IDEAS
Do you like watching movies
though (e.g. on DVD/TV)?
STOP!
The respondent
does not count!
Do you remember any
particular positive or negative
experience regarding a
cinema?
Why not go to
the cinema?
Yes No
Which cinema?
And how often do you go to
CineStar (a year)?
Disadvantages/weaknesses of
CineStar (vs. your favorite
cinema)?
Do you remember any
particular positive or negative
experience regarding
CineStar?
Example:
Laddering +
CIT
DEPTH INTERVIEW: PROS-AND-CONS
67
Advantages Disadvantages
in-depth probing is very useful
at uncovering hidden issues
very rich depth of information
very flexible
there is no social pressure on
respondents to conform and no
group dynamics
can be time consuming
responses can be difficult to
interpret
requires skilled interviewers
expensive
interviewer bias can easily be
introduced
not representative
68
2.Qualitative Research Methods
2.1. Focus Groups
2.2. Depth Interview
2.3. Projective Techniques
2.4. Comparison of Qualitative Techniques
PROJECTIVE TECHNIQUES
69
Projective Techniques
an unstructured, indirect form of
questioning that encourages
respondents to project their underlying
motivations, beliefs, attitudes or
feelings regarding the issues of concern
they are all indirect techniques that
attempt to disguise the purpose of the
research
respondents are asked to interpret the
behavior of others
in doing so, they indirectly project their
own motivations, beliefs, attitudes, or
feelings into the situation
Projective Techniques
relate the attitudes or
feelings of a person
(minimize the social pressure
to give a pol.cor. response)
play the role of someone
else (project own feelings or
behavior into the role)
fill in an empty dialogue
balloon of a cartoon
character
make up a story about the
picture(s)
complete an incomplete
story
complete a set of
incomplete sentences
PROJECTIVE TECHNIQUES
70
Word
Association
Sentence
Completion
say the first word that comes
to mind after hearing a word
Story
Completion
Picture
Response
Cartoon
Tests
Role
Playing
Third-person
Technique
a.k.a. thematic
apperception tests
a.k.a. expressive
techniques
draw what you are feeling or
how you perceive an object
Consumer
Drawing
Word
Association
respondents are presented with a list of words, one at a
time, and asked to respond to each with the first word that
comes to mind.
only some of the words are test words, the rest are filters to
disguise the purpose of the test.
good for testing brand names
EXAMPLE: WORD ASSOCIATION
71
Analysis by calculating:
frequency with which any word is
given as a response
the amount of time elapsed before
the response is given
# of respondents who do not
response at all
washday
fresh
pure
scrub
filth
bubbles
family
towels
everyday
and sweet
air
husband does
neighborhood
bath
squabbles
dirty
ironing
clean
soiled
clean
dirt
soap and water
children
wash
Stimulus Mrs. A Mrs. N
72
A person who shops at Walmart is __________
A person who receives a gift certificate good
for Sak’s Fifth Avenue would be ____________
J.C. Penney is most liked by ________________
When I think of shopping in a department
store, I _____________________
Sentence Completion
Consumer Drawing
Consumers of Pillsbury cake-mixes are drawn grandmotherly,
whereas Duncan Hills’ consumers look svelte and contemporary
Story Completion
Hey John, I just received a
$500 bonus for suggestion my
company is now using on the
production line.I’m thinking
about putting my money in a
credit union.
Cartoon Tests
____________
____________
____________
PROJECTIVE TECHNIQUES: PROS-AND-CONS
73
Advantages Disadvantages
disguising the purpose of the
study allows to elicit responses
that subjects would be unwilling
or unable to give otherwise
esp. when the issues to be
addressed are personal,
sensitive, or subject to strong
social norms
when underlying motivations,
beliefs, and attitudes are
operating at a subconscious
level.
requires highly trained
interviewers
requires skilled interpreters
expensive
engage people in unusual
behavior
serious risk of interpretation
bias
not representative
74
2.Qualitative Research Methods
2.1. Focus Groups
2.2. Depth Interview
2.3. Projective Techniques
2.4. Comparison of Qualitative Techniques
COMPARISON OF QUALITATIVE TECHNIQUES
75
Criteria Focus Groups Depth Interviews Projective Techniques
Degree of structure relatively high relatively medium relatively low
Probing individual respondents low high medium
Moderator bias relatively medium relatively high low to high
Interpretation bias relatively low relatively medium relatively high
Uncovering subconscious information low medium to high high
Discovering innovative information high medium low
Obtaining sensitive information low medium high
Involve unusual behavior/questioning no to a limited extent yes
Overall usefulness highly useful useful somewhat useful
76
3.Observation Methods
OBSERVATION METHODS
77
observation in artificial/
experimental environment,
such as a test kitchen
respondents are aware that
they are under observation
e.g., eye-tracker, voice pitch
analysis, psychogalvanometer
observing behavior as it takes
place in the natural
environment
respondents unaware of being
observed
e.g., one-way mirrors, hidden
cameras, mystery shoppers
Structured
Disguised
researcher specifies in detail
what is to be observed and how
e.g. auditor performing
inventory analysis in the store
Natural
Undisguised
Contrived
monitor all aspects of the
phenomenon that seem
relevant for the problem
children playing with new toys
Unstructured
vs
vs
vs
Observation involves recording the behavioral patterns of people,
objects, and events in a systematic manner to obtain information
about phenomenon of interest
The observer does not question or communicate with the people
being observed
OBSERVATION BY MODE OF ADMINISTRATION
78
Personal
observation
observe actual behavior as it occurs
e.g., record traffic counts, observe traffic flows in a store
Audit
examining physical records
inventory analysis
pantry audit
Mechanical
observation
mechanical devices perform observation and recording
e.g., people meter, traffic counters, cameras, UPC scanners,
eye-tracking, voice pitch analyzer, GSR, response latency
Trace
analysis
physical traces, or evidence of past behavior
e.g., erosion of tiles in a museum; pos. of radio dials in cars
brought for service; age & condition of cars in a parking lot;
# of fingerprints on a page; donated magazines; internet
Content
analysis
when the phenomenon to be observed is communication
units: words, characters, topics, length & duration of a
message
ObservationMethods
PERSONAL OBSERVATION
79
Personal
observation
observe actual behavior as it occurs
e.g., record traffic counts, observe traffic flows in a store
Mechanical
observation
mechanical devices perform observation and recording
e.g., people meter, counting turnstiles, cameras, UPC scanners,
eye-tracking, voice pitch analyzer, GSR, response latency
MECHANICAL OBSERVATION
80
Audit
examining physical records
inventory analysis
pantry audit
AUDIT
81
Content
analysis
when the phenomenon to be observed is communication
units: words, characters, topics, length & duration of a
message
CONTENT ANALYSIS
82
Trace
analysis
physical traces, or evidence of past behavior
e.g., erosion of tiles in a museum; pos. of radio dials in cars
brought for service; age & condition of cars in a parking lot;
# of fingerprints on a page; donated magazines; internet
TRACE ANALYSIS
83
COMPARISON OF OBSERVATION METHODS
84
Criteria
Personal
Observation
Mechanical
Observation
Audit
Content
Analysis
Trace
Analysis
Degree of structure low low to high high high medium
Degree of disguise medium low to high low high high
Ability to observe
in natural setting
high low to high high medium low
Observation bias high low to high low medium medium
Analysis bias high
low to
medium
low low medium
General remarks most flexible
can be
intrusive
expensive
limited to
communications
method of
last resort
OBSERVATION METHODS: PROS-AND-CONS
85
Advantages Disadvantages
measurement of actual rather
than intended or preferred
behavior
no interviewer or reporting bias
capable of revealing behavior
patterns that respondents are
unaware of or unable to
communicate (e.g., spontaneous
purchases, babies’ preferences
of toys)
may be cheaper and faster than
survey methods
reasons for the observed
behavior may not be
determined (underlying
motives, beliefs, attitudes,
preferences)
selective perception bias on
the observer’s side
may be unethical in certain
cases
best used as a compliment to survey methods
86
4.Survey: Measurement and Scaling
4.1. Introduction
4.2. Comparative Scales
4.3. Non-comparative Scales
4.4. Multi-item Scales
4.5. Reliability and Validity
SURVEY RESEARCH
87
Improve quality of
decision making
Trace Problems
Focus on keeping
existing customers
Understand changes
in marketplace
The most popular
technique for gathering
primary data in which a
researcher interacts with
people to obtain facts,
opinions, and attitudes.
Survey Research
SURVEY METHODS
8876
Telephone
Interviewing
traditional (outdated)
computer assisted (CATI)
Mail
Interviewing
mail
mail panel
Personal
Interviewing
in-home
mall intercept
computer assisted (CAPI)
Electronic
Interviewing
e-mail
internet
internet panel
SurveyMethods
panelizable
89
4.Survey: Measurement and Scaling
4.1. Introduction
4.2. Comparative Scales
4.3. Non-comparative Scales
4.4. Multi-item Scales
4.5. Reliability and Validity
MEASUREMENT
90
Measurement
assigning numbers or other symbols
to characteristics of objects according
to certain pre-specified rule.
one-to-one correspondence
between the numbers and
characteristics being measured
the rules for assigning numbers
should be standardized and
applied uniformly
rules must not change over
objects or time
Measurement
SCALING
91
involves creating a
continuum upon which
measured objects are
located.
Scaling
Extremely
unfavorable
Extremely
favorable
PRIMARY SCALES OF MEASUREMENT
92
differences between objects can
be compared
zero point is arbitrary
numbers indicate the relative
positions of objects
but not the magnitude of difference
between them
Ordinal
Interval
numbers serve as labels for
identifying and classifying objects
not continuos
Nominal
zero point is fixed
ratios of scale values can be
computed
Ratio
NOT
1 2
or
1 2 1 2
3
1
2
My preference as a snack food
less more
0 25 50 75 100
Amount sold (kg)
1 2 3
a.k.a. metric
PRIMARY SCALES OF MEASUREMENT
93
Scale Basic Characteristics Common Examples
Marketing
Examples
Permissible StatisticsPermissible Statistics
Scale Basic Characteristics Common Examples
Marketing
Examples
Descriptive Inferential
Nominal Numbers identify and
classify objects
Social security
numbers, numbering of
football players
Brand
numbers, store
types sex,
classification
Percentages,
mode
Chi-square,
binomial test
Ordinal Numbers indicate the
relative positions of the
objects but not the
magnitude of differences
between them
Quality rankings,
ranking of teams in
tournament
Preference
rankings,
market
position, social
class
Percentile,
median
Rank-order
correlation,
Friedman
ANOVA
Interval Differences between
objects can be compared;
zero point is arbitrary
Temperature
(Fahrenheit,
Centigrade)
Attitudes,
opinions, index
numbers
Range, mean,
standard
deviation
Product-moment
correlations, t-
tests, ANOVA,
regression,
factor analysis
Ratio Zero point is fixed; ratios
of scale values can be
compared
Length, weight, time,
money
Age, income,
costs, sales,
market shares
Geometric mean,
harmonic mean
Coefficient of
variation
CLASSIFICATION OF SCALING TECHNIQUES
94
Scaling Techniques
Comparative
Scales
Non-comparative
Scales
Paired
Comparison
Rank
Order
Constant
Sum
Q-Sort &
others
Continuous
Rating Scales
Itemized
Rating Scales
Likert
Semantic
Differential
Stapel
COMPARISON OF SCALING TECHNIQUES
95
Non-comparative
Scales
each object is scaled
independently
resulting data is generally
assumed to be interval or
ratio scaled
Comparative
Scales
involve the direct
comparison of stimulus
objects.
data must be interpreted in
relative terms
have only ordinal and rank-
order properties
96
4.Survey: Measurement and Scaling
4.1. Introduction
4.2. Comparative Scales
4.3. Non-comparative Scales
4.4. Multi-item Scales
4.5. Reliability and Validity
CLASSIFICATION OF SCALING TECHNIQUES
97
Scaling Techniques
Comparative
Scales
Non-comparative
Scales
Paired
Comparison
Rank
Order
Constant
Sum
Q-Sort &
others
Continuous
Rating Scales
Itemized
Rating Scales
Likert
Semantic
Differential
Stapel
RELATIVE ADVANTAGES OF COMPARATIVE SCALES
98
same known reference points for
all respondents
easily understood and can be
applied
small differences between
stimulus objects can be detected
involve fewer theoretical
assumptions
tend to reduce halo or carryover
effects from one judgement to
another
Comparative
Scales
involve the direct
comparison of stimulus
objects.
data must be interpreted in
relative terms
have only ordinal and rank-
order properties
COMPARATIVE SCALES: PAIRED COMPARISON
99
Jhirmack Finesse
Vidal
Sasoon
Head &
Shoulders
Pert
Jhirmack
Finesse
Vidal
Sasoon
Head &
Shoulders
Pert
Preferred 3 2 0 4 1
We are going to present you with ten pairs of shampoo brands. For each pair, please
indicate which one of the two brands of of shampoo you would prefer for personal use.
“ “ (1) indicates that the brand in the column is preferred over the in the corresponding row. “ “ (0) means that the row brand is preferred over the column brand.
Recording form:
Respondent is presented with two objects and asked to select one according to some criterion
EXAMPLES
100
Paired
Comp.
PROS-AND-CONS
101
Advantages Disadvantages
direct comparison and overt
choice
good for blind tests, physical
products, and MDS
allows for calculation of
percentage of respondents who
prefer one stimulus to another
can assess rank-orders of stimuli
(under assumption of transitivity)
possible extensions: “no
difference” alternative; graded
comparison
# of comparisons grows
quicker than # of stimuli (for n
objects n(n-1)/2 comparisons)
violations of transitivity may
occur
presentation order bias
possible
preference of A over B does
not imply subject’s liking of A
little similarity to real choice
situation with mult. alternatives
Paired
Comp.
Respondents are presented with several objects simultaneously and are asked to order or rank them
according to some criterion
COMPARATIVE SCALES: RANK ORDER SCALING
102
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 5.
No two brands should receive the same rank number.
The criterion 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. Elmex ___________
4. Pepsodent ___________
5. Aqua Fresh ___________
EXAMPLES
103
Paired
Comp.Rank
Order
EXAMPLES
104
Paired
Comp.Rank
Order
EXAMPLES
105
Paired
Comp.Rank
Order
PROS-AND-CONS
106
Advantages Disadvantages
direct comparison
more realistic than paired
comparison
# of comparisons is only (n-1)
easier to understand
takes less time
no intransitive responses
can be converted to paired
comparison data
good for measuring preferences
of brands or attributes; conjoint
analysis
preference of A over B does
not imply subject’s liking of A
no zero point / separation
between liking and disliking
only ordinal data
Paired
Comp.Rank
Order
Respondents allocate a constant sum of units (points, dollars, chips, %) among a set of stimulus
objects with respect to some criterion
COMPARATIVE SCALES: CONSTANT SUM SCALING
107
Below are eight attributes of toilet 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.
Segment 1 Segment 2 Segment 3
Mildness 8 2 4
Lather 2 4 17
Shrinkage 3 9 7
Price 53 17 9
Fragrance 9 0 19
Packaging 7 5 9
Moisturizing 5 3 20
Cleaning power 13 60 15
Sum 100 100 100
Average response of three segments
108
EXAMPLES
Paired
Comp.Rank
Order
Constant
Sum
EXAMPLES
109
Paired
Comp.Rank
Order
Constant
Sum
PROS-AND-CONS
110
Advantages Disadvantages
allows for for fine discrimination
among stimulus objects without
requiring too much time
ratio scaled
results are limited to the
context of stimuli scaled, i.e.,
not generalizable to other
stimuli not included in the
study
relatively high cognitive
burden for respondents, esp.
when # of items is large
prone to calc. errors (e.g.,
allocation of 108 or 94
points)
Paired
Comp.Rank
Order
Constant
Sum
A rank order procedure in which objects are sorted into piles based on similarity with respect to some
criterion. Usually used to discriminate among a relatively large number (60-140) of objects quickly.
COMPARATIVE SCALES: Q-SORT SCALING
111
most highly
agreed with
least highly
agreed with
EXAMPLE
112
Paired
Comp.Rank
Order
Constant
SumQ-Sort
113
4.Survey: Measurement and Scaling
4.1. Introduction
4.2. Comparative Scales
4.3. Non-comparative Scales
4.4. Multi-item Scales
4.5. Reliability and Validity
CLASSIFICATION OF SCALING TECHNIQUES
114
Scaling Techniques
Comparative
Scales
Non-comparative
Scales
Paired
Comparison
Rank
Order
Constant
Sum
Q-Sort &
others
Continuous
Rating Scales
Itemized
Rating Scales
Likert
Semantic
Differential
Stapel
Respondents rate objects by placing a mark at the appropriate position on a line that runs from one
extreme of the criterion variable to the other.
NON-COMPARATIVE SCALES: CONTINUOUS RATING SCALE
115
How would you rate Wal-Mart as a department store?
Probably the worst Probably the best
Probably the worst Probably the best
Probably the worst Probably the best
0 10 20 30 40 50 60 70 80 90 100
Probably the worst Probably the best
very bad neither good
nor bad
very good
0 10 20 30 40 50 60 70 80 90 100
Version 1
Version 2
Version 3
Version 4
PERCEPTION ANALYZER
116
Continuous
Rating
Requires respondents to indicate a degree of agreement or disagreement with each of a series of
statements about the stimulus object within typically five to seven response categories.
ITEMIZED RATING SCALES: LIKERT SCALE
117
Listed below are different opinions about Sears. Please indicate how strongly you agree or
disagree with each by using the following scale:
Strongly
disagree Disagree
Neither
agree
nor
disagree Agree
Strongly
agree
1 Sears sells high-quality merchandise [1] [x] [3] [4] [5]
2 Sears has poor in-store service [1] [x] [3] [4] [5]
3 I like to shop in Sears [1] [2] [x] [4] [5]
4
Sears does not offer a good mix of different
brands within a product category
[1] [2] [3] [x] [5]
5 The credit policies at Sears are terrible [1] [2] [3] [x] [5]
6 Sears is where America shops [x] [2] [3] [4] [5]
7 I do not like advertising done by Sears [1] [2] [3] [x] [5]
8 Sears sells a wide variety of merchandise [1] [2] [3] [x] [5]
9 Sears charges fair prices [1] [x] [3] [4] [5]
1 = Strongly agree
2 = Disagree
3 = Neither agree nor disagree
4 = Agree
5 = Strongly agree
NOTICE the reversed scoring of items 2,4,5, and 7. Reverse the scale for these items prior analyzing to be consistent with the whole set of items, i.e. a higher score should
denote a more favorable attitude.
Continuous
Rating
EXAMPLES
118
Likert
SOME COMMONLY USED SCALES IN MARKETING
119
Construct Scale DescriptorsScale DescriptorsScale DescriptorsScale DescriptorsScale 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 (Somewhat)
Dissatisfied
Neither
Dissatisfied Nor
Satisfied /
Neutral
(Somewhat)
Satisfied
Very Satisfied
Purchase Intention Definitely Will
Not Buy
Probably will
Not Buy
Might or Might
Not Buy
Probably Will
Buy
Definitely Will
Buy
Purchase Frequency Never Rarely Sometimes Often Very Often
Agreement Strongly
Disagree
Disagree Neither Agree
Nor Disagree
Agree Strongly Agree
Continuous
RatingLikert
EXAMPLES OF LABELING OF 7 AND 9 POINT SCALES
120
 Strongly agree
 Agree to a large extent
 Rather agree
 50/50
 Rather disagree
 Disagree to a large extent
 Strongly disagree
Like extremely
Like very much
Like moderately
Like slightly
Neither like nor dislike
Dislike slightly
Dislike moderately
Dislike very much
Dislike extremely
Continuous
RatingLikert
A rating scale with end point associated with bipolar labels that have semantic meaning.
Respondents are to indicate how accurately or inaccurately each term describes the object.
ITEMIZED RATING SCALES: SEMANTIC DIFFERENTIAL
121
This part of the study measures what certain department stores mean to you by having you
judge them on a series of descriptive scales bounded at each end by one of two bipolar
adjectives. Please mark (X) the blank that best indicates how accurately one or the other
adjective describes what the store means to you. Please be sure to mark every scale; do not
omit any scale.
NOTE: The negative adjective 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.
Powerful [ ] [ ] [ ] [ ] [X] [ ] [ ] Weak
Unreliable [ ] [ ] [ ] [ ] [ ] [X] [ ] Reliable
Modern [ ] [ ] [ ] [ ] [ ] [ ] [X] Old fashioned
Cold [ ] [ ] [ ] [ ] [ ] [X] [ ] Warm
Careful [ ] [X] [ ] [ ] [ ] [ ] [ ] Careless
Sears is:
A SEMANTIC DIFFERENTIAL SCALE FOR MEASURING
SELF-CONCEPTS, PERSON CONCEPTS, AND PRODUCT CONCEPTS
122
Rugged [ ] [ ] [ ] [ ] [ ] [ ] [ ] Delicate
Excitable [ ] [ ] [ ] [ ] [ ] [ ] [ ] Calm
Uncomfortable [ ] [ ] [ ] [ ] [ ] [ ] [ ] Comfortable
Dominating [ ] [ ] [ ] [ ] [ ] [ ] [ ] Submissive
Thrifty [ ] [ ] [ ] [ ] [ ] [ ] [ ] Indulgent
Pleasant [ ] [ ] [ ] [ ] [ ] [ ] [ ] Unpleasant
Contemporary [ ] [ ] [ ] [ ] [ ] [ ] [ ] Non-contemporary
Organized [ ] [ ] [ ] [ ] [ ] [ ] [ ] Unorganized
Rational [ ] [ ] [ ] [ ] [ ] [ ] [ ] Emotional
Youthful [ ] [ ] [ ] [ ] [ ] [ ] [ ] Mature
Formal [ ] [ ] [ ] [ ] [ ] [ ] [ ] Informal
Orthodox [ ] [ ] [ ] [ ] [ ] [ ] [ ] Liberal
Complex [ ] [ ] [ ] [ ] [ ] [ ] [ ] Simple
Colorless [ ] [ ] [ ] [ ] [ ] [ ] [ ] Colorful
Modest [ ] [ ] [ ] [ ] [ ] [ ] [ ] Vain
Rating profiles of different objects / respondents / segments.
Each point corresponds to a mean or median of the respective scale.
LikertSemantic
Diff.
SEMANTIC PROFILES
123
LikertSemantic
Diff.
EXAMPLE
124
LikertSemantic
Diff.
An unipolar rating scale with 10 categories numbered from -5 to +5 without neutral point (zero).
ITEMIZED RATING SCALES: STAPEL SCALE
125
Please evaluate how accurately each word or phrase describes each of department stores.
Select a plus number for phrases you think describe the store accurately. The more
accurately you think the phrase describes the store, the larger the plus number you should
choose. You should select a minus number for phrases you think do not describe in
accurately. The less accurately you think the phrase describes the store, the larger the
minus number you should choose. You can select any number, from +5 for phrases you
think are very accurate, to -5 for phrases you think are very inaccurate.
Sears:+5
+4
+3
+2
+1
High Quality
-1
-2
-3
-4
-5
+5
+4
+3
+2
+1
Poor service
-1
-2
-3
-4
-5
BASIC NON-COMPARATIVE SCALES
126
Scale 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
Likert
Scale
Degrees of agreements on a 1
(strongly disagree) to 5
(strongly agree) scale
Measurement of attitudes Easy to construct,
administer and
understand
More time-
consuming
Semantic
Differential
Seven-point scale with bipolar
labels
Brand, product, and
company images
Versatile Controversy as to
whether the data are
interval
Stapel
Scale
Unipolar ten-point scale, -5 to
+5, without a neutral point
(zero)
Measurement of attitudes
and images
Easy to construct,
administer over
telephone
Confusing an
difficult to apply
NON-COMPARATIVE ITEMIZED RATING SCALE DECISIONS
127
Number of
categories
Although there is no single, optimal number, traditional
guidelines suggest that there should be between five and
nine categories.
Odd/even no.
of categories
If a neutral or indifferent scale response is possible for at least
some respondents, an odd number of categories should be
used
Balanced vs.
unbalanced
In general, the scale should be balanced to obtain objective
data
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.
Forced vs.
non-forced
In situations where the respondents are expected to have no
opinion, the accuracy of the data may be improved by a non-
forced scale
Involvement and knowledge
more cat. when respondents are
interested in the scaling task or are
knowledgable about the objects
Nature of the objects
do objects lend themselves to fine
discrimination?
Mode of data collection
less categories in telephone
interviews
Data analysis
less cat. for aggregation, broad
generalizations or group comp.
more cat. for sophisticated
statistical analysis, esp. correlation
based ones
Considerations
The greater the number of
scale categories, the finer
the discrimination among
stimulus objects that is
possible
Most respondents cannot
handle more than a few
categories
NUMBER OF SCALE CATEGORIES
128
Number of
categories
Although there is no single, optimal number, traditional
guidelines suggest that there should be between five and
nine categories.
BALANCED VS. UNBALANCED SCALES
129
Balanced Scale Unbalanced Scale
Extremely good
Very good
Bad
Very bad
Extremely bad
Extremely good
Very good
Good
Somewhat good
Bad
Very bad
Balanced vs.
unbalanced
In general, the scale should be balanced to obtain objective
data
The middle option of an attitudinal
scale attracts a substantial # of
respondents who might be unsure
about their opinion or reluctant to
disclose it
This can distort measures of central
tendency and variance
Questions that exclude the "don't
know" option tend to produce a
greater volume of accurate data
ODD VS. EVEN / FORCED VS. NON-FORCED
130
Odd/even no.
of categories
If a neutral or indifferent scale response is possible for at least
some respondents, an odd number of categories should be
used
Forced vs.
non-forced
In situations where the respondents are expected to have no
opinion, the accuracy of the data may be improved by a non-
forced scale
Do we want/need “contrast” in
controversial attitudes?
Are respondents unwilling to answer
vs. don’t have an opinion?
Use "don't know" or better “not
applicable” option for factual
questions, but not for attitude
questions
Use branching to ensue concept
familiarity on the respondent’s side
Considerations
Considerations
Providing a verbal
description for each
category may not improve
the accuracy or reliability of
the data vs. scale
ambiguity
Peaked vs. flat response
distributions
VERBAL DESCRIPTION
131
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.
completely
disagree
completely
agree
generally
disagree
generally
agree
132
4.Survey: Measurement and Scaling
4.1. Introduction
4.2. Comparative Scales
4.3. Non-comparative Scales
4.4. Multi-item Scales
4.5. Reliability and Validity
LATENT CONSTRUCTS
133
A Latent Construct
is a variable that cannot be observed or
measured directly but can be inferred from other
observable measurable variables.
Thus, the researcher must capture the
variable through questions representing the
presence/level of the variable in question.
A Latent Construct
satisfied [ ] [ ] [ ] [ ] [ ] [ ] [ ] dissatisfied
pleased [ ] [ ] [ ] [ ] [ ] [ ] [ ] displeased
favorable [ ] [ ] [ ] [ ] [ ] [ ] [ ] unfavorable
pleasant [ ] [ ] [ ] [ ] [ ] [ ] [ ] unpleasant
I like it very much [ ] [ ] [ ] [ ] [ ] [ ] [ ] I didn't like it at all
contented [ ] [ ] [ ] [ ] [ ] [ ] [ ] frustrated
delighted [ ] [ ] [ ] [ ] [ ] [ ] [ ] terrible
Please indicate how satisfied you were with your purchase of _____
by checking the space that best gives your answer.
α=.84
LATENT CONSTRUCTS & MULTI-ITEM SCALES
134
Advantages
allow to assess abstract concepts
make it easier to understand the
data and phenomenon
reduce dimensionality of data
through aggregating a large
number of observable variables in
a model to represent an
underlying concept
link observable (“sub-symbolic”)
data of the real world to symbolic
data in the modeled world
Satisfaction
Loyalty
Trust
Service Quality
Purchase intention
Attitude Toward the Brand
Involvement
Price Perception
Website Ease-of-Use
...
Examples
SECURE CUSTOMER INDEXTM
ASSESSING CONSUMER LOYALTY AND RETENTION
135
Secure
Customer
Very satisfied
Definitely would
recommend
Definitely
will use again
D. Randall Brandt (1996), “Secure Customer Index”, Maritz Research
Secure Customers % very satisfied/definitely would repeat/definitely
would recommend
Favorable Customers % giving at least "second best" response on all
three measures of satisfaction and loyalty
Vulnerable Customers % somewhat satisfied/might or might not repeat/
might or might not recommend
At Risk Customers % somewhat satisfied or dissatisfied/probably or
definitely would not repeat/probably or definitely
would not recommend
Overall Satisfaction 4 = very satisfied
3 = somewhat satisfied
2 = somewhat dissatisfied
1 = very dissatisfied
Willingness to
Recommend
5 = definitely would recommend
4 = probably would recommend
3 = might or might not recommend
2= probably would not recommend
1= definitely would recommend
Likelihood to Use
Again
5 = definitely will use again
4 = probably will use again
3= might or might not use again
2= probably will not use again
1 = definitely will not use again
MULTI-ITEM SCALES: MAKE OR STEAL
136
Develop a theory
Generate an initial pool of items:
theory, secondary data, and qualitative research
Select a reduced set of items based on
qualitative judgement
Collect data from a large pretest sample
Perform statistical analysis
Develop a purified scale
Collect more data from a different sample
Evaluate scale reliability, validity, and
generalizability
Prepare the final scale
Brunner, Gordon C. II (2012), “Marketing Scales
Handbook: A Compilation of Multi-Item
Measures for Consumer Behavior & Advertising
Research”, Vol. 6, available as PDF at
www.marketingscales.com
Journal of the Academy of Marketing Science (JAMS)
Journal of Advertising (JA)
Journal of Consumer Research (JCR)
Journal of Marketing (JM)
Journal of Marketing Research (JMR)
Journal of Retailing (JR)
MARKETING SCALES HANDBOOK: EXAMPLES
137
Excerpt from Table of Contents: Satisfaction Scales
Example of a Scale
Copyright material. For educational purposes and use within the current
class only!!! Reproduction, copying, and/or dissemination in any form is
strictly prohibited by the copyright holder.
138
Scale Variants to Measure a Construct
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class only!!! Reproduction, copying, and/or dissemination in any form is
strictly prohibited by the copyright holder.
139
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140
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141
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142
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143
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144
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145
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146
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148
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149
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150
4.Survey: Measurement and Scaling
4.1. Introduction
4.2. Comparative Scales
4.3. Non-comparative Scales
4.4. Multi-item Scales
4.5. Reliability and Validity
MULTI-ITEM SCALES: MEASUREMENT ACCURACY
151
Measurement
A measurement is not the true value
of the characteristic of interest but
rather an observation of it.
XO = XT + XS + XR
where
XO = the observed score of measurement
XT = the true score of characteristic
XS = systematic error
XR = random error
The True Score Model
RELIABILITY & VALIDITY
152
XO = XT + XS + XR
Reliability
extent to which a scale produces
consistent results in repeated
measurements
absence of random error ( XR → 0)
reliability of a multi-item scale is
denoted as Cronbach’s alpha
(0≥α≥1)
values of α≥0.7 are conside-
red satisfactory
Validity
extent to which differences in observed
scale scores reflect true differences
among objects on the characteristic
being measured
no measurement error
( XO → XT, XS → 0, XR → 0)
RELATIONSHIP BETWEEN RELIABILITY & VALIDITY
153
XO = XT + XS + XR
validity implies reliability
( XO = XT | XS = 0, XR = 0)
unreliability implies invalidity
( XR ≠ 0 | XO = XT +XR ≠ XT)
reliability does not imply validity
( XR = 0, XS ≠ 0 | XO = XT +XS ≠ XT)
reliability is a necessary, but not
sufficient, condition of validity
“The purpose of a scale is to allow us to represent
respondents with the highest accuracy and reliability.
We can’t have one without the other and still believe in
our data.”
Bart Gamble,
vice president,
client services,
Burke, Inc.
154
NET PROMOTER SCORE®
COMPETITIVE GROWTH RATES?
155
How likely are you to recommend company/brand/product X
to a friend/colleague/relative?
Reichheld, Fred (2003) "One Number You Need to Grow", Harvard Business Review
Is the scale valid?
Is the scale reliable?
156
5.Questionnaire
5.1. Asking Questions
5.2. Overcoming Inability to Answer
5.3. Overcoming Unwillingness to Answer
5.4. Increasing Willingness of Respondents
5.5. Determining the Order of Questions
5.6. What’s Next?
QUESTIONNAIRE
157
A Questionnaire
is a formalized set of questions for
obtaining information from respondents.
Objectives of a Questionnaire:
translate the information need into a set of
specific questions that the respondents can
and will answer
uplift, motivate, and encourage respondents
to become involved in the interview, to
cooperate, and to complete the interview
minimize response error
A Questionnaire
ISSUES TO CONSIDER IN QUESTIONNAIRE DESIGN
158
Is the question necessary?
Are several questions needed instead
of one?
Is the respondent informed?
Can the respondent remember?
Effort required of the respondents
Sensitivity of question
Legitimate purpose
Cultural issues
Ease of completion
Comprehensiveness
Bias in formulation
Do you actually believe in the big love?
Do you believe in the big love?
BIAS IN FORMULATION
159
Q: Do you approve smoking
whilst praying?
A: No
Q: Do you approve praying
whilst smoking?
A: Yes
0 15 30 45 60
Yes
No
Uncertain
Basis: n = 2100, p <.05
Noelle-Neumann and Petersen (1998), p. 192
160
5.Questionnaire
5.1. Asking Questions
5.2. Overcoming Inability to Answer
5.3. Overcoming Unwillingness to Answer
5.4. Increasing Willingness of Respondents
5.5. Determining the Order of Questions
5.6. What’s Next?
ASKING QUESTIONS
161
Avoid ambiguity, confusion, and
vagueness
Avoid jargon, slang, abbreviations
Avoid double-barreled questions
Avoid leading
Avoid implicit assumptions
Avoid implicit alternatives
Avoid treating respondent’s belief
about a hypothesis as a test of the
hypothesis
Avoid generalizations and estimates
“It is not every question that
deserves an answer”
Publius Syrus
(roman, 1st century B.C.)
Define the issue in terms of who, what, when,
where, why, and way (the six Ws). Who, what,
when, and where are particularly important.
Example:
Which brand of shampoo do you use?
Ask instead:
Which brand or brands of shampoo have you
personally used at home during the last month?
In case of more than one brand, please list all the
brands that apply.
Avoid Ambiguity, confusion and vagueness
ASKING QUESTIONS
162
The W’s Defining the Question
Who The Respondent
It is not clear whether this question relates to the individual respondent or, e.g., the
respondent’s total household
What The Brand of Shampoo
It is unclear how the respondent is to answer this question if more than one brand is used
When Unclear
The time frame is not specified in this question. The respondent could interpret it as
meaning the shampoo used this morning, this, week, or over the past year.
Where Unclear
At home, at gym, on the road?
163
Which brand of shampoo do you use?
Example:
What brand of computer do you own?

 ☐ Windows PC

 ☐ Apple
Ask instead:
Do you own a Windows PC? (☐ Yes ☐ No)
Do you own an Apple computer? (☐ Yes ☐ No)
Even better:
What brand of computer do you own?

 ☐ Do not own a computer

 ☐ Windows PC

 ☐ Apple

 ☐ Other
Avoid Ambiguity, confusion and vagueness
Example:
Are you satisfied with your current auto insurance?

 ☐ Yes

 ☐ No
Ask instead:
Are you satisfied with your current auto insurance?

 ☐ Yes

 ☐ No

 ☐ Don’t have auto insurance
Even better:
1. Do you currently have a life insurance policy?
(☐ Yes ☐ No). If no, go to question 3
2. Are you satisfied with your current auto insurance?
(☐ Yes ☐ No)
ASKING QUESTIONS
164
Example:
In a typical month, how often do you shop in department
stores?

 ☐ Never

 ☐ Occasionally

 ☐ Sometimes

 ☐ Often

 ☐ Regularly
Ask instead:
In a typical month, how often do you shop in department
stores?

 ☐ Less than once

 ☐ 1 or 2 times

 ☐ 3 or 4 times

 ☐ More than 4 times
Avoid Ambiguity, confusion and vagueness
ASKING QUESTIONS
165
Whenever using words “will”,
“could”, “might”, or “may” in
a question, you might suspect
that the question asks a time-
related question.
scales and options
should be
unambiguous too
Use ordinary words
Avoid jargon, slang, abbreviations
Example:
Do you think the distribution of soft drinks is
adequate?
Ask instead:
Do you think soft drinks are readily available
when you want to buy them?
ASKING QUESTIONS
166
Example:
What was your AGI last year?
$ _______
Are several questions needed instead of one?
Avoid double-barreled questions
Example:
Do you think Coca-Cola is a tasty and refreshing
soft drink?
Ask instead:
1. Do you think Coca-Cola is a tasty soft drink?
2. Do you think Coca-Cola is a refreshing soft
drink?
ASKING QUESTIONS
167
If you want a certain answer - why ask?
Avoid leading
Example:
Do you help the environment by using canvas
shopping bags?
Ask instead:
Do you use canvas shopping bags?
ASKING QUESTIONS
168
The answer should not depend on upon implicit
assumptions about what will happen as a
consequence.
Example:
Are you in favor of a balanced budget?
Ask instead:
Are you in favor of a balanced budget it it would
result in an increase in the personal income tax?
ASKING QUESTIONS
169
Avoid implicit assumptions
An alternative that is not explicitly expressed in the
options is an implicit alternative.
ASKING QUESTIONS
170
Avoid implicit alternatives
Example:
Do you like to fly when traveling short distances?
Ask instead:
Do you like to fly when traveling short distances, or
would you rather drive?
Beliefs are only a biased representation of reality
Example:
Do you think more educated people wear fur
clothing?
Ask instead:
1. What is your education level?
2. Do you wear fur clothing?
ASKING QUESTIONS
171
Avoid treating beliefs as real facts
Don’t task respondents’ memory and math skills
Example:
What is the annual per capita expenditure on
groceries in your household?
Ask instead:
1. What is the monthly (or weekly) expenditure on
groceries in your household?
2. How many member are there in your household?
ASKING QUESTIONS
172
Avoid generalizations and estimates
173
5.Questionnaire
5.1. Asking Questions
5.2. Overcoming Inability to Answer
5.3. Overcoming Unwillingness to Answer
5.4. Increasing Willingness of Respondents
5.5. Determining the Order of Questions
5.6. What’s Next?
OVERCOMING INABILITY TO ANSWER
174
Can the Respondent Remember?
Can the Respondent Articulate?
Is the Respondent Informed?
Respondents will often answer questions even
though they are not informed
Example:
Please indicate how strongly you agree or disagree with the
following statement:
“The National Bureau of Consumer Complaints provides an
effective means for consumers who have purchased a
defective product to obtain relief”
51.9% of the lawyers and 75% of the public expressed
their opinion, although there is no such entity as the
NBCC
Use Filter Questions
e.g. ask about familiarity and/or frequency of
patronage in a study of 10 department stores
Use “don’t know” Option
OVERCOMING INABILITY TO ANSWER
175
Is the Respondent Informed?
The inability to remember leads to errors of
omission, telescoping, and creation
Example:
How many liters of soft drinks did you consume during
the last four weeks?
Ask instead:
How often do you consume soft drinks in a typical week?

 ☐ Less than once a week

 ☐ 1 to 3 times per week

 ☐ 4 or 6 times per week

 ☐ 7 or more times per week
Use aided recall approach (where appropriate)
“What brands of soft drinks do you remember being advertised
last night on TV?”
vs
“Which of these brands were advertised last night on TV?”
OVERCOMING INABILITY TO ANSWER
176
Can the Respondent Remember?
If unable to articulate their responses, respondents
are likely to ignore the question and quit the survey
Example:
If asked to describe the atmosphere of the
department store they would prefer to
patronage, most respondents may be unable to
phrase their answers.
Provide aids, e.g., pictures, maps,
descriptions
If the respondents are given alternative
descriptions of store atmosphere, they will be
able to indicate the one they like the best.
OVERCOMING INABILITY TO ANSWER
177
Can the Respondent Articulate?
178
5.Questionnaire
5.1. Asking Questions
5.2. Overcoming Inability to Answer
5.3. Overcoming Unwillingness to Answer
5.4. Increasing Willingness of Respondents
5.5. Determining the Order of Questions
5.6. What’s Next?
OVERCOMING UNWILLINGNESS TO ANSWER
179
Most respondents are unwilling to
devote a lot of effort to provide information
respond to questions that they consider to
be inappropriate for the given context
divulge information they do not see as
serving a legitimate purpose
disclose sensitive information
Provide
context
Legitimate
purpose
Reduce
effort
Minimize the effort required of respondents
Example:
Please list all the departments from which you purchased
merchandise on your most recent shopping to a department
store.
Ask instead:
In the list that follows, please check all the departments from
which you purchased merchandise on your most recent
shopping to a department store.

 ☐ Women’s dresses

 ☐ Men’s apparel

 ☐ Children’s apparel

 ☐ Cosmetics

 …….

 ☐ Jewelry

 ☐ Other (please specify) _________________
OVERCOMING UNWILLINGNESS TO ANSWER
180
Reduce
effort
Some questions may seem appropriate in certain
contexts but not in others
Example:
Questions about personal hygiene habits may be
appropriate when asked in a survey sponsored by
the Medical Association, but not in one
sponsored by a fast-food restaurant
Provide context by making a statement:
“As a fast-food restaurant, we are very concerned
about providing a clean and hygienic environment
for our customers. Therefore, we would like to ask
you some questions related to personal hygiene.”
Provide
context
OVERCOMING UNWILLINGNESS TO ANSWER
181
Explain why the data is needed
Example:
Why should a firm marketing cereals want to
know the respondents’ age, income, and
occupation?
Legitimate the request information:
“To determine
 how the consumption of cereals
vary among people of different ages, incomes,
and occupation, we need information on ...”
Legitimate
purpose
OVERCOMING UNWILLINGNESS TO ANSWER
182
183
5.Questionnaire
5.1. Asking Questions
5.2. Overcoming Inability to Answer
5.3. Overcoming Unwillingness to Answer
5.4. Increasing Willingness of Respondents
5.5. Determining the Order of Questions
5.6. What’s Next?
INCREASING WILLINGNESS OF RESPONDENTS
184
Place sensitive topics at the end of the
questionnaire
Preface questions with a statement that the
behavior is of interest in common
Ask the question using third-person
technique: phrase the question as if it
referred to other people
Hide the question in a group of other
questions
Provide response categories rather than
asking for specific figures
Sensitive Topics:
- money
- family life
- political and religious beliefs
- involvement in accidents
or crimes
185
5.Questionnaire
5.1. Asking Questions
5.2. Overcoming Inability to Answer
5.3. Overcoming Unwillingness to Answer
5.4. Increasing Willingness of Respondents
5.5. Determining the Order of Questions
5.6. What’s Next?
DETERMINING THE ORDER OF QUESTIONS
186
Opening Questions
The opening questions should be
interesting, simple, and non-threatening.
Type of Information
As a general guideline, basic information
should be obtained first, followed by
classification, and, finally, identification
information.
Difficult Questions
Difficult questions or questions which are
sensitive, embarrassing, complex, or dull,
should be placed late in the sequence.
DETERMINING THE ORDER OF QUESTIONS
187
Effect on Subsequent Questions (funneling)
General questions should precede the specific
questions
1. What considerations are important to you in
selecting a department store?
2. In selecting a department store, how important
is convenience of location?
Logical Order / Branching Questions
The question being branched should be placed as
close as possible to the question causing the
branching.
The branching questions should be ordered so
that the respondents cannot anticipate what
additional information will be reuired.
EXAMPLE: FLOWCHART OF A QUESTIONNAIRE
188
Introduction
Store
Charge
Card
Ownership of Store, Bank, and/or other Charge
Cards
Purchased products in a specific department store
during the last two months
How was payment made? Ever purchased products in a
departments store?
Bank
Charge
Card
Other
Charge
Card
Intention to use Store, Bank,
or Other Charge Cards
yes no
yes
no
Cash
Other
Credit
189
5.Questionnaire
5.1. Asking Questions
5.2. Overcoming Inability to Answer
5.3. Overcoming Unwillingness to Answer
5.4. Increasing Willingness of Respondents
5.5. Determining the Order of Questions
5.6. What’s Next?
What’s Next?
190
Introduction
Catch the respondents’ interest
Explain the reasons & objectives
Ask for their help
Tell that their support is valuable
Tell how much time it will last
Emphasize the anonymity
Incentivize
(non-monetary incentives)
What’s Next?
191
Pretest! Pretest! Pretest!!!
question content
wording
sequence
form and layout
question difficulty
instructions…
analysis procedures
RECAP
192
1. Develop a flow chart of the information required based on the marketing
research problem
Once the entire sequence is laid out, the interrelationships should become
clear
Match up the actual data you would expect to collect from the
questionnaire against the information needs listed in the flow chart
Be specific in the objective for each area of information and data. You
should be able to write an objective for each area so specifically that it
guides your construction of the questions.
2. At this stage, put on your “critic’s” hat and go back over the flowchart and ask
Do I need to know it and know exactly what I am going to do with it? or
It would be nice to know it but I do not have to have it
193
6.Sampling
6.1. Non-probability Sampling
6.2. Probability Sampling
6.3. Choosing Non-Probability vs. Probability Sampling
6.4. Sample Size
194
The world’s most famous
newspaper error
President Harry Truman against
Thomas Dewey
Chicago Tribute prepared an
incorrect headline without first
getting accurate information
Reason?
→ bias
→ inaccurate opinion polls
195
196
Yes, dear Dilbert, it was the wrong Sample
SAMPLING
197
Population
the group of people we wish to
understand. Populations are
often
segmented by demographic or
Sample
a subset of population
that represents the whole
Most research cannot test
everyone. Instead a sample
of the whole population is
selected and tested.
If this is done well, the
results can be applied to the
whole population.
This selection and testing of
a sample is called sampling.
If a sample is poorly chosen,
all the data may be useless.
SAMPLING: TWO GENERAL METHODS
198
This relies on personal judgement of theresearcher (often on people available, e.g.,people passing in the street or walkingthrough a mall).
This may yield good estimates of populationcharacteristics, however, doesn’t allow forobjective evaluation of the precision ofsample results. That is, the results are notprojectable to the population.
Non-
probability
Sampling
Here, sampling units are selected by
chance, i.e., randomly.
This randomness allows applying
statistical techniques to determine the
precision of the sample estimates and
their confidence intervals. The results
are generalizable and projectable to
the population from which the sample
is drawn.
Probability
Sampling
CLASSIFICATION OF SAMPLING TECHNIQUES
199
Sampling Techniques
Non-probability Probability
Convenience
Sampling
Judgmental
Sampling
Quota
Sampling
Snowball
Sampling
Stratified
Sampling
Cluster
Sampling
Other Samp-
ling Techniques
Systematic
Sampling
Simple Random
Sampling
Proportionate Disproportionate
200
6.Sampling
6.1. Non-probability Sampling
6.2. Probability Sampling
6.3. Choosing Non-Probability vs. Probability Sampling
6.4. Sample Size
CLASSIFICATION OF SAMPLING TECHNIQUES
201
Sampling Techniques
Non-probability Probability
Convenience
Sampling
Judgmental
Sampling
Quota
Sampling
Snowball
Sampling
Stratified
Sampling
Cluster
Sampling
Other Samp-
ling Techniques
Systematic
Sampling
Simple Random
Sampling
Proportionate Disproportionate
CONVENIENCE SAMPLING
202
Depth Interview
attempts to obtain a sample of convenient
respondents. Often, respondents are
selected because they happen to be in
the right place at right time.
students or members of social
organizations
mall intercept interviews without
qualifying the respondents
“people on the street” interviews
tear-out questionnaires in magazines
Convenience Sampling
JUDGMENTAL SAMPLING
203
a form of convenience sampling in
which the population elements are
selected based on the judgement
of the researcher
test markets
purchase engineers selected in
industrial marketing research
mothers as diaper “users”
Judgmental Sampling
Control
Characteristic
Population
Composition
Sample CompositionSample Composition
Control
Characteristic
Percentage Percentage Number
Sex
Male
Female
48
52
-------
100
48
52
-------
100
480
520
-------
1000
Age
18-30
31-45
45-60
Over 60
27
39
16
18
-------
100
27
39
16
18
-------
100
270
390
160
180
-------
1000
QUOTA SAMPLING
204
develop control categories, or quotas, of
population elements (e.g., sex, age, race,
income, company size, turnover, etc.) so that
the proportion of the elements possessing
these characteristics in the sample reflects
their distribution in the population.
The elements themselves are selected based
on convenience or judgment. The only
requirement, however, is that the elements
selected fit the control characteristics (quota).
Quota Sampling
Often used in
online
surveys
SNOWBALL SAMPLING
205
an initial group of respondents is selected (usually)
at random.
After being interviewed, these respondents are
asked to identify others who belong to the target
population of interest.
Subsequent respondents are selected based on
the referrals.
Good for locating the desired characteristic in the
population:
reaching hard-to-reach respondents (e.g.,
government services, “food stamps”, drug users)
estimating characteristics that are rare in the
population
identifying buyer-seller pairs in industrial research
Snowball Sampling
Often used in
online
surveys
Very favored
by students
206
6.Sampling
6.1. Non-probability Sampling
6.2. Probability Sampling
6.3. Choosing Non-Probability vs. Probability Sampling
6.4. Sample Size
CLASSIFICATION OF SAMPLING TECHNIQUES
207
Sampling Techniques
Non-probability Probability
Convenience
Sampling
Judgmental
Sampling
Quota
Sampling
Snowball
Sampling
Stratified
Sampling
Cluster
Sampling
Other Samp-
ling Techniques
Systematic
Sampling
Simple Random
Sampling
Proportionate Disproportionate
Require
knowledge
about the
population
Each element in the population has a
known and equal probability of selection
Each possible sample of a given size (n)
has a known probability of being the
sample actually selected
This implies that every element is selected
independently of every other element.
Simple Random Sampling
SRS & SYSTEMATIC SAMPLING
208
The sample is chosen by selecting a random
starting point and then picking every i-th
element in succession from the sampling
frame
The sampling interval, i, is determined by
dividing the population size N by the sample
size n, i.e., i=N/n
Systematic Sampling
Require
knowledge
about the
population
start here
take every
i-th element
select randomly
i
i
i
STRATIFIED SAMPLING
209
is obtained by separating the population into
non-overlapping groups called strata and then
obtaining a proportional simple random sample
from each group. The individuals within each
group should be similar in some way.
Good for:
highlighting a specific subgroup within the
population
observing existing relationships between two
or more subgroups
representative sampling of even the smallest
and most inaccessible subgroups in the
population
a higher statistical precision
Stratified Sampling
Proportionate
Stratum A B C
Population Size 100 200 300
Sampling Fraction 1/2 1/2 1/2
Final Sample Size 50 100 150
Stratum A B C
Population Size 100 200 300
Sampling Fraction 1/5 1/2 1/3
Final Sample Size 20 100 100
Disproportionate
Require
knowledge
about the
population
CLUSTER SAMPLING
210
the target population is first divided into
mutually exclusive and collectively exhaustive
subpopulations, or clusters. Than a random
sample of clusters is selected, based on SRS.
Good for:
covering large geographic areas
reducing survey costs
when constructing a complete list of
population elements is difficult
when the population concentrated in
natural clusters (e.g., blocks, cities, schools,
hospitals, boxes, etc.)
Cluster Sampling
Require
knowledge
about the
population
For each cluster, either all the
elements are included in the sample
(one-stage) or a sample of elements
is drawn probabilistically (two-sage).
211
6.Sampling
6.1. Non-probability Sampling
6.2. Probability Sampling
6.3. Choosing Non-Probability vs. Probability Sampling
6.4. Sample Size
STRENGTHS AND WEAKNESSES OF BASIC SAMPLING TECHNIQUES
212
Technique Strengths Weaknesses
Non-probability Sampling
Convenience sampling Least expensive, least time consuming,
most convenient
Selection bias, sample not
representative, not recommended for
descriptive or causal research
Judgmental sampling Low cost, convenient, not time
consuming
Does not allow generalization, subjective
Quota sampling Sample can be controlled for certain
characteristics
Selection bias, no assurance of
representativeness
Snowball sampling Can estimate rare characteristics Time consuming in the field research
Probability Sampling
Simple random sampling (SRS) Easily understood, results projectable Difficult to construct sampling frame,
expensive, lower precision, no assurance
of representativeness
Systematic sampling Can increase representativeness, easier
to implement than SRS
Can decrease representativeness
Stratified sampling Includes all important subpopulations,
precision
Difficult to select relevant stratification
variables, not feasible to stratify on many
variables, expensive
Cluster sampling Easy to implement, cost effective Imprecise, difficult to compute and
interpret results
213
The middle option of an attitudinal
scale attracts a substantial # of
respondents who might be unsure
about their opinion or reluctant to
disclose it
This can distort measures of central
tendency and variance
Questions that exclude the "don't
know" option tend to produce a
greater volume of accurate data
Do we want/need “contrast” in
controversial attitudes?
Are respondents unwilling to answer
vs. don’t have an opinion?
Use "don't know" or better “not
applicable” option for factual
questions, but not for attitude
questions
Use branching to ensue concept
familiarity on the respondent’s side
Non-probability Probability
214
Non-comparative
Scales
each object is scaled
independently
resulting data is generally
assumed to be interval or
ratio scaled
Comparative
Scales
involve the direct
comparison of stimulus
objects.
data must be interpreted in
relative terms
have only ordinal and rank-
order properties
nature of the research
variability in the population
statistical considerations
215
6.Sampling
6.1. Non-probability Sampling
6.2. Probability Sampling
6.3. Choosing Non-Probability vs. Probability Sampling
6.4. Sample Size
DETERMINING THE SAMPLE SIZE
216
The sample size does not depend on the size of
the population being studied, but rather it
depends on qualitative factors of the research.
desired precision of estimates
knowledge of population parameters
number of variables
nature of the analysis
importance of the decision
incidence and completion rates
resource constraints
Determining the Sample Size
SAMPLE SIZES USED IN MARKETING RESEARCH STUDIES
217
Type of Study Minimum Size Typical Size
Problem identification research
(e.g., market potential)
500 1,000 - 2,000
Problem solving research
(e.g., pricing)
200 300 - 500
Product tests 200 300 - 500
Test-market studies 200 300 - 500
TV/Radio/Print advertising
(per commercial ad tested)
150 200 - 300
Test-market audits 10 stores 10 - 20 stores
Focus groups 6 groups 10 - 15 groups
MARGIN OF ERROR APPROACH TO DETERMINING SAMPLE SIZE
218
MARGIN OF ERROR APPROACH TO DETERMINING SAMPLE SIZE
219
What is your
primary daily
media
channel?
MARGIN OF ERROR APPROACH TO DETERMINING SAMPLE SIZE
220
What is your
primary daily
media
channel?
How accurate
is this statistic?
What is the
margin of
error?
The Margin of Error is the
measure of accuracy of a survey.
The smaller the margin of error,
the more accurate are the
estimates of a survey.
MARGIN OF ERROR APPROACH TO DETERMINING SAMPLE SIZE
221
Means
use this formula when evaluating estimatesof population means
Proportions
use this when evaluating estimates of
proportions
Means Proportions
E = z
σ
n
E = z
π(1−π)
n
x = real population parameter
x = sample statistic
E = margin of error
^
x = ˆx ± E
z = z-value for a given level of confidenceσ = standard deviation of a population parametern = sample size
z = z-value for a given level of confidence
π = estimate of the proportion in the population
n = sample size
MARGIN OF ERROR APPROACH TO DETERMINING SAMPLE SIZE
222
Means
use this formula when evaluating estimatesof population means
Proportions
use this when evaluating estimates of
proportions
Means Proportions
E = z
σ
n
E = z
π(1−π)
n
x = real population parameter
x = sample statistic
E = margin of error
^
x = ˆx ± E
z = z-value for a given level of confidenceσ = standard deviation of a population parametern = sample size
z = z-value for a given level of confidence
π = estimate of the proportion in the population
n = sample size
unlikely to be known
MARGIN OF ERROR APPROACH TO DETERMINING SAMPLE SIZE
223
Means
use this formula when evaluating estimatesof population means
Proportions
use this when evaluating estimates of
proportions
Means Proportions
E = z
σ
n
E = z
π(1−π)
n
x = real population parameter
x = sample statistic
E = margin of error
^
x = ˆx ± E
z = z-value for a given level of confidenceσ = standard deviation of a population parametern = sample size
z = z-value for a given level of confidence
π = estimate of the proportion in the population
n = sample size
unlikely to be known
has a maximum at π = .5
MARGIN OF ERROR APPROACH TO DETERMINING SAMPLE SIZE
224
maximum margin of error for 95%
level of confidence
Proportions
E = z
π(1−π)
n
x = real population parameter
x = sample statistic
E = margin of error
^
x = ˆx ± E
z-values
z = 1.96
for 95% level of confidence
z = 2.58
for 99% level of confidence
=1.96
0.5(1− 0.5)
n
≈
1
n
MARGIN OF ERROR APPROACH TO DETERMINING SAMPLE SIZE
225
What is your
primary daily
media
channel?
How accurate
is this statistic?
What is the
margin of
error?
Margin of Error = 1/√n
48,804 people in sample
√48,804 = 220.916
1/221 = 0.0045
*100 = 0.45%
x = 61% ± 0.45%
60.55% to 61.45%
x = ˆx ± E
calculations are approximate values for 95% level of confidence
MARGIN OF ERROR APPROACH TO DETERMINING SAMPLE SIZE
226
What is your
primary daily
media
channel?
How big should
the sample be
taking margin
of error of ±1%
into account?
Sample Size n = (1/Margin of Error)^2
n±1%= (1/0.01)^2 = (100)^2 = 10,000
n±2%= (1/0.02)^2 = (50)^2 = 2,500
n±5%= (1/0.05)^2 = (20)^2 = 400
n±10%= (1/0.1)^2 = (10)^2 = 100
n ≈
1
E
"
#
$
%
&
'
2
E ≈
1
n
calculations are approximate values for 95% level of confidence
MARGIN OF ERROR APPROACH TO DETERMINING SAMPLE SIZE
227
What is your
primary daily
media
channel?
calculations are approximate values for 95% level of confidence
Sample Size n = (1/Margin of Error)^2
Sample Size does not depend on population.
n±1%= (1/0.01)^2 = (100)^2 = 10,000
What if the population under study consists of
only 100 elements? (e.g., firms producing
cars)
Corrections
needed, when
sample size
exceeds 10% of
the population
MARGIN OF ERROR APPROACH TO DETERMINING SAMPLE SIZE
228
What is your
primary daily
media
channel?
calculations are approximate values for 95% level of confidence
Correction of the Sample Size
ncorr =
n
(1+(n −1) / population)
Corrections
needed, when
sample size
exceeds 10% of
the population
MARGIN OF ERROR APPROACH TO DETERMINING SAMPLE SIZE
229
What is your
primary daily
media
channel?
calculations are approximate values for 95% level of confidence
n±1%= (1/0.01)^2 = (100)^2 = 10,000
What if the population under study consists of
only 100 elements? (e.g., firms producing cars)
ncorr =
n
(1+(n −1) / population)
ncorr =
10,000
(1+(10,000 −1) /100)
=
10,000
(1+ 9,999 /100)
=
10,000
(100.99)
= 99.02
Corrections
needed, when
sample size
exceeds 10% of
the population
MARGIN OF ERROR APPROACH TO DETERMINING SAMPLE SIZE
230
What is your
primary daily
media
channel?
calculations are approximate values for 95% level of confidence
n±5%= (1/0.05)^2 = (20)^2 = 400
What if the population under study consists of
only 100 elements? (e.g., firms producing cars)
ncorr =
n
(1+(n −1) / population)
ncorr =
400
(1+(400 −1) /100)
=
400
(1+399 /100)
=
400
(4.99)
= 80.16
Corrections
needed, when
sample size
exceeds 10% of
the population
MARGIN OF ERROR APPROACH TO DETERMINING SAMPLE SIZE
231
What is your
primary daily
media
channel?
calculations are approximate values for 95% level of confidence
Corrections
needed, when
sample size
exceeds 10% of
the population
n±10%= (1/0.1)^2 = (10)^2 = 100
What if the population under study consists of
only 100 elements? (e.g., firms producing cars)
ncorr =
n
(1+(n −1) / population)
ncorr =
100
(1+(100 −1) /100)
=
100
(1+ 99 /100)
=
100
(1.99)
= 50.25
A NOTE ON CONFIDENCE INTERVAL
232
A confidence interval estimate is an interval of
numbers, along with a measure of the likelihood
that the interval contains the unknown parameter.
The level of confidence is the expected proportion
of intervals that will contain the parameter if a large
number of samples is maintained.
Confidence Interval & Level of Confidence
Suppose we're wondering what the average number of hours
that people at Siemens spend working. We might take a sample
of 30 individuals and find a sample mean of 7.5 hours. If we say
that we're 95% confident that the real mean is somewhere
between 7.2 and 7.8, we're saying that if we were to repeat this
with new samples, and gave a margin of ±0.3 hours every time,
our interval would contain the actual mean 95% of the time.
The higher the confidence we
need, the wider the confidence
interval and the greater the
margin of error will be
CONFIDENCE INTERVAL, MARGIN OF ERROR, AND SAMPLE SIZE
233
maximum margin of error for 99%
level of confidence
E = z
π(1−π)
n
z-values
z = 1.96
for 95% level of confidence
z = 2.58
for 99% level of confidence
= 2.58
0.5(1− 0.5)
n
=
1.29
n
The higher the confidence we
need, the wider the confidence
interval and the greater the
margin of error will be
CONFIDENCE INTERVAL, MARGIN OF ERROR, AND SAMPLE SIZE
234
maximum margin of error for 99%
level of confidence
E = z
π(1−π)
n
z-values
z = 1.96
for 95% level of confidence
z = 2.58
for 99% level of confidence
= 2.58
0.5(1− 0.5)
n
=
1.29
n
To reduce the
margin of error
we have to
increase the
sample size
higher levels of confidence
require larger samples
smaller margins of error
require larger samples
235
7.Data Analysis:
A Concise Overview of Statistical Techniques
7.1. Descriptive Statistics:
Some Popular Displays of Data
7.1.1. Organizing Qualitative Data
7.1.2. Organizing Quantitative Data
7.1.3. Summarizing Data Numerically
7.1.4. Cross-Tabulations
7.2. Inferential Statistics:
Can the results be generalized to population?
7.2.1. Hypothesis Testing
7.2.2. Strength of a Relationship in Cross-Tabulation
7.2.3. Describing the Relationship Between
Two (Ratio Scaled) Variables
TYPES OF STATISTICAL DATA ANALYSIS
236
Inferential
Inferential statistics are
techniques that allow making
generalizations about a
population based on
random samples drawn from
the population.
Allow assessing causality and
quantifying relationships
between variables.
Descriptive
Descriptive statistics provide
simple summaries about the
sample and about the
observations that have been
made.
Include the numbers, tables,
charts, and graphs used to
describe, organize,
summarize, and present raw
data.
237
7.Data Analysis:
A Concise Overview of Statistical Techniques
7.1. Descriptive Statistics: Some Popular Displays of Data
7.1.1. Organizing Qualitative Data
7.1.2. Organizing Quantitative Data
7.1.3. Summarizing Data Numerically
7.1.4. Cross-Tabulations
7.2. Inferential Statistics:
Can the results be generalized to population?
FREQUENCY AND RELATIVE FREQUENCY TABLES
238
Original Data
A frequency distribution lists each
category of data and the number of
occurrences for each category
The relative frequency is the
proportion (or percent) of
observations within a category
A relative frequency distribution lists
each category of data together with the
relative frequency of each category.
relative frequency =
frequency
sumof all frequencies
Excel how to video: http://faculty.elgin.edu/dkernler/statistics/videos/excel2007/freq-table-1.mov
BAR GRAPHS
239
Original Data
Bar Graphs / Bar Charts
1. heights can be frequency
or relative frequency
2. bars must not touch
Excel how to video: http://faculty.elgin.edu/dkernler/statistics/videos/excel2007/bar-graph.mov
PIE CHARTS
240
Pie Charts
1. should always include the relative frequency
2. also should include labels, either directly or as a legend
Excel how to video: http://faculty.elgin.edu/dkernler/statistics/videos/excel2007/pie-chart.mov
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Market Research - course slides

  • 2. DISCLAIMER This Presentation may contain Copyrighted Material, DO NOT DISTRIBUTE 2
  • 3. THE MOST IMPORTANT SKILLS IN MARKETING 3 Source: “7 Habits of Effective Marketing Organizations”, Eloqua (2010)
  • 4. COURSE OBJECTIVES • Understand the role of marketing research in shaping managerial decisions • Get an overview of classical activities in as well as of practical tools and methods of marketing research • Be able to implement marketing research studies, analyze and interpret data, and present the results 4
  • 5. 5 RECOMMENDED READING Malhotra, Naresh K. (2009), “Marketing Research: An Applied Orientation”, 6th edition, Prentice Hall Myers, James H. (1996), “Segmentation & Positioning for Strategic Marketing Decisions”, South-Western Educational Pub Hair, Joseph F. Jr, William C. Black, Barry J. Babin, and Rolph E. Anderson (2009), “Multivariate Data Analysis”, 7th edition, Prentice Hall
  • 6. NICE TO HAVE (READ) 6 Kotler, Philip and Gary Armstrong (2009), “Principles of Marketing”, 13th edition, Prentice Hall Cravens, David and Nigel Piercy (2012), “Strategic Marketing”, 10th edition, McGraw-Hill/Irwin Wedel , Michel, and Wagner A. Kamakura (2000), “Market Segmentation: Conceptual and Methodological Foundations”, 2nd edition, Kluwer Academic Publishers Brunner, Gordon C. II (2012), “Marketing Scales Handbook: A Compilation of Multi-Item Measures for Consumer Behavior & Advertising Research”, Vol. 6, available as PDF at www.marketingscales.com Hoyer, Wayne D., Deborah J. MacInnis (2008), “Consumer Behavior”, South-Western College Pub; 5 edition Ariely, Dan (2010), “Predictably Irrational: The Hidden Forces That Shape Our Decisions”, revised and expanded edition, Harper Perennial Coe, John (2003), “The Fundamentals of Business-to-Business Sales & Marketing”, McGraw-Hill
  • 7. CONTENTS IN BRIEF 1. Introduction 1.1. Marketing Research 1.2. Types of Market Research 1.3. Research Methods 2. Qualitative Research Methods 2.1. Focus Groups 2.2. Depth Interview 2.3. Projective Techniques 2.4. Comparison of Qualitative Techniques 3. Observation Methods 4. Survey: Measurement and Scaling 4.1. Intorduction 4.2. Comparative Scales 4.3. Non-comparative Scales 4.4. Multi-item Scales 4.5. Reliability and Validity 5. Questionnaire 5.1. Asking Questions 5.2. Overcoming Inability to Answer 5.3. Overcoming Unwillingness to Answer 5.4. Increasing Willingness of Respondents 5.5. Determining the Order of Questions 5.6. What’s Next? 6. Sampling 6.1. Non-probability Sampling 6.2. Probability Sampling 6.3. Choosing Non-Probability vs. Probability Sampling 6.4. Sample Size 7. Data Analysis: A Concise Overview of Statistical Techniques 7.1. Descriptive Statistics: Some Popular Displays of Data 7.1.1. Organizing Qualitative Data 7.1.2. Organizing Quantitative Data 7.1.3. Summarizing Data Numerically 7.1.4. Cross-Tabulations 7.2. Inferential Statistics: Can the results be generalized to population? 7.2.1. Hypothesis Testing 7.2.2. Strength of a Relationship in Cross-Tabulation 7.2.3. Describing the Relationship Between Two (Ratio Scaled) Variables 8. Advanced Techniques of Market Analysis: A Brief Overview of Some Useful Concepts 8.1. Conjoint Analysis 8.2. Market Simulations 8.3. Market Segmentation 8.4. Perceptual Positioning Maps 9. Reporting Results 7
  • 8. 8 1.Introduction 1.1.Marketing Research 1.2.Types of Market Research 1.3.Research Methods
  • 9. CASE BEECHCRAFT STARSHIP 9 First civilian aircraft with - carbon fiber composite airframe - canard (“duck”) design - L-shaped wings with rudders in them - Two turbo-prop engines mounted aft to pull - R&D costs est. $500Mio “For the pilot and passengers, it has really got everything... ...for the money, the performance just isn’t there... ...for $5Mio, you can buy a jet. Starship just doesn’t fit in today’s market”1 “The Starship was a $500Mio mistake because of a lack of marketing research”2 1 Dennis Murphy, a sales person at Elliot Flying Services in Des Moines, Iowa 2 Russel Munson in “The Stock Market”, 1991
  • 10. CASE ELECTROLUX 10 Electrolux - a scandinavian manufacturer of inexpensive vacuum cleaners - took its rhyming phrase “Nothing Sucks Like an Electrolux” and brought it in the early 1970s to America from English-speaking markets overseas. They didn’t know that the word “sucks” had become a derogatory word in the US.
  • 11. CASE AMERICAN AIRLINES 11 American Airlines launched a new leather first class seats ad campaign (1977-78) in the Mexican market: "Fly in Leather" (vuela encuero) meant "Fly Naked"
  • 12. CASE FOOD & BEVERAGES 12 In what must be one of the most bizarre brand extensions ever Colgate decided to use its name on a range of food products called Colgate's Kitchen Entrees. Needless to say, the products did not take off and never left U.S. soil. The idea must have been that consumers would eat their Colgate meal, then brush their teeth with Colgate toothpaste. The trouble was that for most people the name Colgate does not exactly get their taste buds tingling. In the 1970s and early 80s, Coke began to face stiff competition from other soft drink producers. To remain in the number one spot, Coke executives decided to cease production on the classic cola in favor of New Coke. The public was outraged, and Coca- Cola was forced to re-launch its original formula almost immediately. Lesson learned -- don't mess with success. Cocaine is a high-energy drink, containing three and a half times the amount of caffeine as Red Bull. It was pulled from U.S. shelves in 2007, after the FDA declared that its producers, Redux Beverages, were "illegally marketing their drink as an alternative to street drugs." The drink is still available, however, online, in Europe and even in select stores in the U.S. Despite the controversy, Redux Beverages does not plan to cease production any time soon. You know what they say -- there's no such thing as bad publicity.
  • 13. RETURNS ON MARKETING ACTIONS • 60-95% of new products fail • 50% of advertising has no effect • 85% of price promotions loose money • 97% brands create 37% $ (Unilever) 13
  • 14. 14 • Marketing Research is there to prevent such things from happening
  • 15. RECALL Marketing Marketing consists of the strategies and tactics used to identify, create and maintain satisfying relationships with customers that result in value for both the customer and the marketer. Marketing Concept A business philosophy based on consumer orientation, goal orientation, and systems orientation. Consumer Orientation Identification of and focus on the people or firms most likely to buy a product and production of a good or service that will meet their needs most effectively. Goal Orientation A focus on the accomplishment of corporate goals; a limit set on consumer orientation. Systems Orientation Creation of systems to monitor the external environment and deliver the marketing mix to the target market. Marketing Mix (a.k.a. 4Ps/Cs and 7Ps Models) The unique blend of product, pricing, promotion, offerings, and distribution designed to meet the needs of a specific group of consumers. 15
  • 16. MARKETING: A VERY PARSIMONIOUS OVERVIEW 16 Quality Satisfaction Profit Needs Wants Preferences Utility function Attitudes Intentions Motives Involvement Beliefs Emotions Lifestyle Habits Buying behavior ... Trust & Loyalty Consumer
  • 17. 17 1.Introduction 1.1.Marketing Research 1.2.Types of Market Research 1.3.Research Methods
  • 18. MARKETING RESEARCH: A CONCISE DEFINITION Marketing Research The planning, collection, and analysis of data relevant to marketing decision making and the communication of the results of this analysis to management. 18
  • 19. 19
  • 20. Why marketing research? THE IMPORTANCE OF MARKETING RESEARCH 20 Improve quality of decision making Trace Problems Focus on keeping existing customers Understand changes in marketplace
  • 21. MARKET RESEARCH VS. MARKETING RESEARCH (STRICTLY SPEAKING...) 21 Market Research Marketing Research Researching the immediate competitive environment of the marketplace, including customers, competitors, suppliers, distributors and retailers Includes all the above plus: - companies and their strategies for products and markets - the wider environment within which the firm operates (e.g., political, social, etc)
  • 22. TOP 10 MARKET RESEARCH ACTIVITIES 22 Market measurement 18% New Product development / concept testing 14% Ad or Brand awareness monitoring / tracking 13% Customer satisfaction (incl. Mystery Shopping) 10% Usage and Attitude studies 7% Media research & evaluation 6% Advertising development and pre-testing 5% Social Surveys for central/local governments 4% Brand/corporate reputation 4% Omnibus studies 3% Source: Business Management Research Associates, Inc.
  • 23. MARKET RESEARCH PROCESS 23 Define the research problem Decide on budget data sources research approaches sampling plan contact methods methods of data analysis Develop the research plan Collect data Analyze data Report findings identify and clarify information needs define research problem and questions specify research objectives confirm information value collect data according to the plan or employ an external firm The plan needs to be decided upfront but flexible enough to incorporate changes or iterations This phase is the most costly and the most liable to error If a problem is vaguely defined, the results can have little bearing on the key issues Overall conclusions to be presented rather than overwhelming statistical methodologies Formulate conclusions and implications from data analysis prepare finalized research report Analyze data statistically or subjectively and infer answers and implications 1 2 3 4 5 Type of data analysis depends on type of research Comments Contents
  • 24. WHEN NOT TO CONDUCT MARKET RESEARCH 24 Occasion Comments Lack of resources If quantitative research is needed, it is not worth doing unless a statistically significant sample can be used. When funds are insufficient to implement any decisions resulting from the research. Closed mindset When decision has already been made. Research is used only as a rubber stamp of a preconceived idea. Information not needed When decision-making information already exists. Vague objectives When managers cannot agree on what they need to know to make a decision. Market research cannot be helpful unless it is probing a particular issue. Results not actionable Where, e.g., psychographic data is used which will not help he company form firm decisions. Late timing When research results come too late to influence the decision. Poor timing If a product is in a “decline” phase there is little point in researching new product varieties Costs outweigh benefits The expected value of information should outweigh the costs of gathering an analyzing the data.
  • 25. 25 1.Introduction 1.1.Marketing Research 1.2.Types of Market Research 1.3.Research Methods
  • 26. TYPES OF MARKET RESEARCH 26 By Objectives By Data Source By Methodology Exploratory (a.k.a. diagnostic) Descriptive Causal (a.k.a. predictive, experimental) Qualitative Quantitative Primary Secondary
  • 27. Exploratory (a.k.a. diagnostic) Explaining data or actions to help define the problem What was the impact on sales after change in the package design? Do promotions at POS influence brand awareness? MARKET RESEARCH BY OBJECTIVES 27 Descriptive Gathering and presenting factual statements: who, what, when, where, how What is historic sales trend in the industry? What are consumer attitudes toward our product? Causal (a.k.a. predictive, experimental) Probing cause-and-effect relationships; “What if?” Specification of how to use the research to predict the results of planned marketing decisions Does level of advertising determine level of sales? small scale surveys, focus groups, interviews larger scale surveys, observation, etc. experiments, consumer panels ProblemIdentificationProblemSolving Uncertaintyinfluencesthetypeofresearch
  • 28. UNCERTAINTY SHAPES THE TYPE OF RESEARCH 28 Problem Identification Research Problem Solving Research Market Potential Research Market Share Research Image Research Market Characteristics Research Sales Analysis Research Forecasting Research Business Trends Research Segmentation Research Product Research Pricing Research Promotion Research Distribution Research Exploratory research Descriptive research Causal research AwareUncertain Certain degree of problem/decision certainty
  • 29. MARKET RESEARCH BY DATA SOURCE 29 Primary Secondary Original research to collect new raw data for a specific reason. This data is then analyzed and may be published by the researcher. Research data that has been previously collected, analyzed and published in the form of books, articles, etc.
  • 30. SECONDARY DATA: PROS-AND-CONS 30 Secondary Data Advantages Disadvantages Saves time and money if on target Aids in determining direction for primary data collection Pinpoints the kinds of people to approach Serves as a basis for other data May not give adequate detailed information May not be on target with the research problem Quality and accuracy of data may pose a problem Information previously collected for any purpose other than the one at hand
  • 31. PRIMARY DATA: PROS-AND-CONS 31 Advantages Disadvantages Answers a specific research question Data are current Source of data is known Secrecy can be maintained Expensive “Piggybacking” may confuse respondents Quality declines in interviews are lengthy Reluctance to participate in lengthy interviews Primary Data Information collected for the first time to solve the particular problem under investigation Disadvantages are usually offset by the advantages of primary data
  • 32. Exploratory research Causal research Descriptive research MARKET RESEARCH BY METHODOLOGY 32 Qualitative Involves understanding human behavior and the reasons behind it Focus is on individuals and small groups Objectivity is not the goal, the aim is to understand one point of view, not all points of view. Primary Data Secondary Data Quantitative Involves collecting and measuring data Often requires large data sets. For example, large number of people. Uses statistical methods to analyze data Aims to achieve objective/ scientific view of the subject
  • 33. 33 1.Introduction 1.1.Marketing Research 1.2.Types of Market Research 1.3.Research Methods
  • 34. RESEARCH METHODOLOGY 34 research methodology The searching for and gathering of information and ideas in response to a specific question The set of methods used to address a specific research problem at hand
  • 36. SOURCES OF SECONDARY DATA Internal Corporate Information Government Agencies Trade and Industry Associations Business Periodicals News Media Databases Internet Sources … 36 Secondary Data
  • 37. Secondary Data EVALUATING SECONDARY DATA SOURCES 37 Use the C.R.A.P. test Currency Reliability Authority Purpose
  • 38. Secondary Data EVALUATING DATA SOURCES 38 Currency How recent is the information? Are there more recent updates available? Is it current enough for your topic? Reliability Is content of the resource primarily opinion? Is it balanced and evidenced? Does the creator provide references or sources for the data? Authority Who is the creator or author? What are his/her credentials? Is s/he an expert? Who is the publisher os sponsor? Are they reputable? Purpose / Point of View Is it promotional or educational material? Are there advertisements on the website? is this fact or opinion? Who is the intended audience?
  • 39. 39 Quantitative Survey Focus Groups Depth Interview Projective Techniques Observation Qualitative Primary Approaches Survey Observation Depth Interview Projective Tech. Focus Groups Survey Observation
  • 40. 40Robson (1998), Visocky & Visocky (2009) APPARENT TRUTH Literature Review InterviewSurvey Triangulation The combination of methods in the study of the same topic
  • 42. 42 2.Qualitative Research Methods 2.1. Focus Groups 2.2. Depth Interview 2.3. Projective Techniques 2.4. Comparison of Qualitative Techniques
  • 43. 43 2.Qualitative Research Methods 2.1. Focus Groups 2.2. Depth Interview 2.3. Projective Techniques 2.4. Comparison of Qualitative Techniques
  • 44. FOCUS GROUPS 44 Focus Groups organized discussions with a moderator and limited number of participants qualitative method to gain insights from the appropriate target consumers through studying their perceptions, opinions, beliefs, and attitudes moderator should remain neutral, ask open ended questions, speak only when necessary and record session lasts between 1.5 and 2 hours Focus Groups
  • 45. FOCUS GROUPS 45 I’d like to speak with you all about your opinions on... ------, -----. ----- ! ------. -----? ------! ----. ----? http://www.youtube.com/watch?v=POF3m6ZNoiY http://www.youtube.com/watch?v=cnV1pS7qVD8
  • 46. APPLICATIONS OF FOCUS GROUPS 46 Understanding consumers’ perception, preferences, and behaviors concerning a product category Obtaining impressions of new product concepts Generating new ideas about older products Developing creative concepts and copy material for advertisements Securing price impressions Obtaining preliminary consumer reaction to specific marketing programs ...
  • 47. FOCUS GROUPS: ADVANTAGES 47 I’d like to know what you all think about English Immersion. Do you think we should have more, less or the same amount of it? More but not too much more! About the same. I guess. Less. I think. Less, definitely. Interesting. Why do you all disagree? No, no! Less!
  • 48. FOCUS GROUPS: ADVANTAGES 48 I’d like to know what you all think about English Immersion. Do you think we should have more, less or the same amount of it? More but not too much more! About the same. I guess. Less. I think. Less, definitely. Interesting. Why do you all disagree? No, no! Less! Ability to ask many people about “why” Ability to observe and de-code disagreements Can learn how groups make sense of the topic Synergism Snowballing Stimulation Security Spontaneity Serendipity
  • 49. FOCUS GROUPS: DISADVANTAGES 49 Great. Thank you, Carl. Anyone else? And another one issue I’d l I really hate how high gas prices are! Oh, and don’t get me started about the GST! Ok, Carl. Thanks.
  • 50. FOCUS GROUPS: DISADVANTAGES 50 Great. Thank you, Carl. Anyone else? And another one issue I’d l I really hate how high gas prices are! Oh, and don’t get me started about the GST! Ok, Carl. Thanks. Difficulty in getting people in the same room Difficulty controlling conversations Huge amount of data Dominant personalities Misuse Misjudge Messy Misrepresentation
  • 51. FOCUS GROUPS: PROS-AND-CONS 51 Advantages Disadvantages ability to ask many people about “why” ability to observe and de-code disagreements can learn how groups make sense of the topic synergism snowballing stimulation security spontaneity serendipity difficulty in getting people in the same room difficulty controlling conversations dominant personalities huge amount of data misuse misjudge messy misrepresentation
  • 52. FOCUS GROUPS: SIZE AND WHOM TO RECRUIT 52 Typically 6-10 (Morgan 1998) 8-10 (Malhotra 2004) Small (4-5) when there’s lots to say or a controversity Large (20+) when opinions are likely brief homogenous in terms of target group characteristics (demographics, socio- economics…) experienced with the issue have not participated in many focus groups
  • 53. HOW TO DO FOCUS GROUPS 53 Plot test interview guide General research questions Write interview guide Determine size of group Decide participant qualities Secure facility and moderator Recruit Notes by separate note taker Conduct focus group Interpret data Conceptual and theoretical work Write up findings Recording and/or video Transcript Collection of more data Tighter specification of question
  • 55. INTERNET FOCUS GROUPS: ADVANTAGES 55 Geographical constraints are removed Time constraints are lessened Ability to reach hard-to-reach target groups Ability to recontact respondents No travel costs, No videotaping, No facilities to arrange
  • 56. INTERNET FOCUS GROUPS: DISADVANTAGES 56 Difficulty ensuring the person is in the target group Lack of control over environment and distraction Only intangible stimuli Only experienced PC users Not suitable for highly emotional issues
  • 57. INTERNET FOCUS GROUPS 57 Advantages Disadvantages geographical and time constraints are removed or lessened ability to recontact respondents ability to reach hard-to-reach segments lower costs only experienced PC users can be surveyed hard to ensure that a person is a member of a target group lack of control over respondent’s environment and distracting external factors products cannot be touched or smelled inability to explore highly emotional issues or subject matters
  • 58. INTERNET FOCUS GROUPS: USES 58 Banner ads, Copy testing, Concept testing, Usability testing esp. suitable for companies in the online business Multimedia evaluation; Comparisons of icons or graphics
  • 59. 59 2.Qualitative Research Methods 2.1. Focus Groups 2.2. Depth Interview 2.3. Projective Techniques 2.4. Comparison of Qualitative Techniques
  • 60. DEPTH INTERVIEW 60 Depth Interview method for in-depth probing of personal opinions, beliefs, and values interview is conducted one-on-one lasts between 30 and 60 minutes unstructured (or loosely structured) data is obtained from a relatively small group of respondents data is not analyzed with inferential statistics Depth Interview
  • 61. DEPTH INTERVIEW: TECHNIQUES 61 Laddering start with questions about external objects and external social phenomena, then proceed to internal attitudes and feelings Critical Incident Technique (CIT) A critical incident is one that makes a significant contribution - either positively or negatively - to an activity or phenomenon. respondents are asked to tell a story about an experience they have had Symbolic Analysis attempts to analyze the symbolic meaning of objects by comparing them with their opposites e.g. product non-usage, opposite types of products Hidden Issue Questioning the focus is not on socially share values but rather on personal “sore spots” and “pet peeves”; not on general lifestyles but on deeply felt personal concerns
  • 62. EXAMPLE: LADDERING 62 Laddering start with questions about external objects and external social phenomena, then proceed to internal attitudes and feelings Wide body aircraft I can get more work done I accomplish more I feel good about myself product characteristic user characteristic Advertisement message: You will feel good about yourself when flying our airline. “You’re The Boss”
  • 63. Hidden Issue Questioning the focus is not on socially share values but rather on personal “sore spots” and “pet peeves”; not on general lifestyles but on deeply felt personal concerns EXAMPLE: HIDDEN ISSUE QUESTIONING 63 fantasies, work lives, and social lives historic, elite, masculine- camaraderie, competitive activities Advertisement theme: Communicate aggressiveness, high status, and competitive heritage of the airline.
  • 64. Symbolic Analysis attempts to analyze the symbolic meaning of objects by comparing them with their opposites e.g. product non-usage, opposite types of products EXAMPLE: SYMBOLIC ANALYSIS 64 “What would it be like if you could no longer use airplanes?” “Without planes I would have to rely more on e-mails, letters, and long-distance calls” Advertisement theme: The airline will do the same thing for a manger as Federal Express does for package. Airlines sell to the managers face-to-face communication
  • 65. Critical Incident Technique (CIT) A critical incident is one that makes a significant contribution - either positively or negatively - to an activity or phenomenon. respondents are asked to tell a story about an experience they have had EXAMPLE: CRITICAL INCIDENT TECHNIQUE 65 “What was the worst thing you ever experienced with airlines?” “The snoring guy to m y left who was staring onto my shoes right after he was awake” Lack of privacy
  • 66. 66 Do you go to the cinema? Yes No, no cinema at all What cinema do you usually/most frequently go to? CINESTAR? Yes No Do you remember any particular positive or negative experience regarding CineStar? What do you like about the CineStar (better than other theaters)? What don’t you like that much? In overall, how often do you go to the cinema? INQUIRE UNTIL THE RESPONDENT IS OUT OF IDEAS Do you like watching movies though (e.g. on DVD/TV)? STOP! The respondent does not count! Do you remember any particular positive or negative experience regarding a cinema? Why not go to the cinema? Yes No Which cinema? And how often do you go to CineStar (a year)? Disadvantages/weaknesses of CineStar (vs. your favorite cinema)? Do you remember any particular positive or negative experience regarding CineStar? Example: Laddering + CIT
  • 67. DEPTH INTERVIEW: PROS-AND-CONS 67 Advantages Disadvantages in-depth probing is very useful at uncovering hidden issues very rich depth of information very flexible there is no social pressure on respondents to conform and no group dynamics can be time consuming responses can be difficult to interpret requires skilled interviewers expensive interviewer bias can easily be introduced not representative
  • 68. 68 2.Qualitative Research Methods 2.1. Focus Groups 2.2. Depth Interview 2.3. Projective Techniques 2.4. Comparison of Qualitative Techniques
  • 69. PROJECTIVE TECHNIQUES 69 Projective Techniques an unstructured, indirect form of questioning that encourages respondents to project their underlying motivations, beliefs, attitudes or feelings regarding the issues of concern they are all indirect techniques that attempt to disguise the purpose of the research respondents are asked to interpret the behavior of others in doing so, they indirectly project their own motivations, beliefs, attitudes, or feelings into the situation Projective Techniques
  • 70. relate the attitudes or feelings of a person (minimize the social pressure to give a pol.cor. response) play the role of someone else (project own feelings or behavior into the role) fill in an empty dialogue balloon of a cartoon character make up a story about the picture(s) complete an incomplete story complete a set of incomplete sentences PROJECTIVE TECHNIQUES 70 Word Association Sentence Completion say the first word that comes to mind after hearing a word Story Completion Picture Response Cartoon Tests Role Playing Third-person Technique a.k.a. thematic apperception tests a.k.a. expressive techniques draw what you are feeling or how you perceive an object Consumer Drawing
  • 71. Word Association respondents are presented with a list of words, one at a time, and asked to respond to each with the first word that comes to mind. only some of the words are test words, the rest are filters to disguise the purpose of the test. good for testing brand names EXAMPLE: WORD ASSOCIATION 71 Analysis by calculating: frequency with which any word is given as a response the amount of time elapsed before the response is given # of respondents who do not response at all washday fresh pure scrub filth bubbles family towels everyday and sweet air husband does neighborhood bath squabbles dirty ironing clean soiled clean dirt soap and water children wash Stimulus Mrs. A Mrs. N
  • 72. 72 A person who shops at Walmart is __________ A person who receives a gift certificate good for Sak’s Fifth Avenue would be ____________ J.C. Penney is most liked by ________________ When I think of shopping in a department store, I _____________________ Sentence Completion Consumer Drawing Consumers of Pillsbury cake-mixes are drawn grandmotherly, whereas Duncan Hills’ consumers look svelte and contemporary Story Completion Hey John, I just received a $500 bonus for suggestion my company is now using on the production line.I’m thinking about putting my money in a credit union. Cartoon Tests ____________ ____________ ____________
  • 73. PROJECTIVE TECHNIQUES: PROS-AND-CONS 73 Advantages Disadvantages disguising the purpose of the study allows to elicit responses that subjects would be unwilling or unable to give otherwise esp. when the issues to be addressed are personal, sensitive, or subject to strong social norms when underlying motivations, beliefs, and attitudes are operating at a subconscious level. requires highly trained interviewers requires skilled interpreters expensive engage people in unusual behavior serious risk of interpretation bias not representative
  • 74. 74 2.Qualitative Research Methods 2.1. Focus Groups 2.2. Depth Interview 2.3. Projective Techniques 2.4. Comparison of Qualitative Techniques
  • 75. COMPARISON OF QUALITATIVE TECHNIQUES 75 Criteria Focus Groups Depth Interviews Projective Techniques Degree of structure relatively high relatively medium relatively low Probing individual respondents low high medium Moderator bias relatively medium relatively high low to high Interpretation bias relatively low relatively medium relatively high Uncovering subconscious information low medium to high high Discovering innovative information high medium low Obtaining sensitive information low medium high Involve unusual behavior/questioning no to a limited extent yes Overall usefulness highly useful useful somewhat useful
  • 77. OBSERVATION METHODS 77 observation in artificial/ experimental environment, such as a test kitchen respondents are aware that they are under observation e.g., eye-tracker, voice pitch analysis, psychogalvanometer observing behavior as it takes place in the natural environment respondents unaware of being observed e.g., one-way mirrors, hidden cameras, mystery shoppers Structured Disguised researcher specifies in detail what is to be observed and how e.g. auditor performing inventory analysis in the store Natural Undisguised Contrived monitor all aspects of the phenomenon that seem relevant for the problem children playing with new toys Unstructured vs vs vs Observation involves recording the behavioral patterns of people, objects, and events in a systematic manner to obtain information about phenomenon of interest The observer does not question or communicate with the people being observed
  • 78. OBSERVATION BY MODE OF ADMINISTRATION 78 Personal observation observe actual behavior as it occurs e.g., record traffic counts, observe traffic flows in a store Audit examining physical records inventory analysis pantry audit Mechanical observation mechanical devices perform observation and recording e.g., people meter, traffic counters, cameras, UPC scanners, eye-tracking, voice pitch analyzer, GSR, response latency Trace analysis physical traces, or evidence of past behavior e.g., erosion of tiles in a museum; pos. of radio dials in cars brought for service; age & condition of cars in a parking lot; # of fingerprints on a page; donated magazines; internet Content analysis when the phenomenon to be observed is communication units: words, characters, topics, length & duration of a message ObservationMethods
  • 79. PERSONAL OBSERVATION 79 Personal observation observe actual behavior as it occurs e.g., record traffic counts, observe traffic flows in a store
  • 80. Mechanical observation mechanical devices perform observation and recording e.g., people meter, counting turnstiles, cameras, UPC scanners, eye-tracking, voice pitch analyzer, GSR, response latency MECHANICAL OBSERVATION 80
  • 81. Audit examining physical records inventory analysis pantry audit AUDIT 81
  • 82. Content analysis when the phenomenon to be observed is communication units: words, characters, topics, length & duration of a message CONTENT ANALYSIS 82
  • 83. Trace analysis physical traces, or evidence of past behavior e.g., erosion of tiles in a museum; pos. of radio dials in cars brought for service; age & condition of cars in a parking lot; # of fingerprints on a page; donated magazines; internet TRACE ANALYSIS 83
  • 84. COMPARISON OF OBSERVATION METHODS 84 Criteria Personal Observation Mechanical Observation Audit Content Analysis Trace Analysis Degree of structure low low to high high high medium Degree of disguise medium low to high low high high Ability to observe in natural setting high low to high high medium low Observation bias high low to high low medium medium Analysis bias high low to medium low low medium General remarks most flexible can be intrusive expensive limited to communications method of last resort
  • 85. OBSERVATION METHODS: PROS-AND-CONS 85 Advantages Disadvantages measurement of actual rather than intended or preferred behavior no interviewer or reporting bias capable of revealing behavior patterns that respondents are unaware of or unable to communicate (e.g., spontaneous purchases, babies’ preferences of toys) may be cheaper and faster than survey methods reasons for the observed behavior may not be determined (underlying motives, beliefs, attitudes, preferences) selective perception bias on the observer’s side may be unethical in certain cases best used as a compliment to survey methods
  • 86. 86 4.Survey: Measurement and Scaling 4.1. Introduction 4.2. Comparative Scales 4.3. Non-comparative Scales 4.4. Multi-item Scales 4.5. Reliability and Validity
  • 87. SURVEY RESEARCH 87 Improve quality of decision making Trace Problems Focus on keeping existing customers Understand changes in marketplace The most popular technique for gathering primary data in which a researcher interacts with people to obtain facts, opinions, and attitudes. Survey Research
  • 88. SURVEY METHODS 8876 Telephone Interviewing traditional (outdated) computer assisted (CATI) Mail Interviewing mail mail panel Personal Interviewing in-home mall intercept computer assisted (CAPI) Electronic Interviewing e-mail internet internet panel SurveyMethods panelizable
  • 89. 89 4.Survey: Measurement and Scaling 4.1. Introduction 4.2. Comparative Scales 4.3. Non-comparative Scales 4.4. Multi-item Scales 4.5. Reliability and Validity
  • 90. MEASUREMENT 90 Measurement assigning numbers or other symbols to characteristics of objects according to certain pre-specified rule. one-to-one correspondence between the numbers and characteristics being measured the rules for assigning numbers should be standardized and applied uniformly rules must not change over objects or time Measurement
  • 91. SCALING 91 involves creating a continuum upon which measured objects are located. Scaling Extremely unfavorable Extremely favorable
  • 92. PRIMARY SCALES OF MEASUREMENT 92 differences between objects can be compared zero point is arbitrary numbers indicate the relative positions of objects but not the magnitude of difference between them Ordinal Interval numbers serve as labels for identifying and classifying objects not continuos Nominal zero point is fixed ratios of scale values can be computed Ratio NOT 1 2 or 1 2 1 2 3 1 2 My preference as a snack food less more 0 25 50 75 100 Amount sold (kg) 1 2 3 a.k.a. metric
  • 93. PRIMARY SCALES OF MEASUREMENT 93 Scale Basic Characteristics Common Examples Marketing Examples Permissible StatisticsPermissible Statistics Scale Basic Characteristics Common Examples Marketing Examples Descriptive Inferential Nominal Numbers identify and classify objects Social security numbers, numbering of football players Brand numbers, store types sex, classification Percentages, mode Chi-square, binomial test Ordinal Numbers indicate the relative positions of the objects but not the magnitude of differences between them Quality rankings, ranking of teams in tournament Preference rankings, market position, social class Percentile, median Rank-order correlation, Friedman ANOVA Interval Differences between objects can be compared; zero point is arbitrary Temperature (Fahrenheit, Centigrade) Attitudes, opinions, index numbers Range, mean, standard deviation Product-moment correlations, t- tests, ANOVA, regression, factor analysis Ratio Zero point is fixed; ratios of scale values can be compared Length, weight, time, money Age, income, costs, sales, market shares Geometric mean, harmonic mean Coefficient of variation
  • 94. CLASSIFICATION OF SCALING TECHNIQUES 94 Scaling Techniques Comparative Scales Non-comparative Scales Paired Comparison Rank Order Constant Sum Q-Sort & others Continuous Rating Scales Itemized Rating Scales Likert Semantic Differential Stapel
  • 95. COMPARISON OF SCALING TECHNIQUES 95 Non-comparative Scales each object is scaled independently resulting data is generally assumed to be interval or ratio scaled Comparative Scales involve the direct comparison of stimulus objects. data must be interpreted in relative terms have only ordinal and rank- order properties
  • 96. 96 4.Survey: Measurement and Scaling 4.1. Introduction 4.2. Comparative Scales 4.3. Non-comparative Scales 4.4. Multi-item Scales 4.5. Reliability and Validity
  • 97. CLASSIFICATION OF SCALING TECHNIQUES 97 Scaling Techniques Comparative Scales Non-comparative Scales Paired Comparison Rank Order Constant Sum Q-Sort & others Continuous Rating Scales Itemized Rating Scales Likert Semantic Differential Stapel
  • 98. RELATIVE ADVANTAGES OF COMPARATIVE SCALES 98 same known reference points for all respondents easily understood and can be applied small differences between stimulus objects can be detected involve fewer theoretical assumptions tend to reduce halo or carryover effects from one judgement to another Comparative Scales involve the direct comparison of stimulus objects. data must be interpreted in relative terms have only ordinal and rank- order properties
  • 99. COMPARATIVE SCALES: PAIRED COMPARISON 99 Jhirmack Finesse Vidal Sasoon Head & Shoulders Pert Jhirmack Finesse Vidal Sasoon Head & Shoulders Pert Preferred 3 2 0 4 1 We are going to present you with ten pairs of shampoo brands. For each pair, please indicate which one of the two brands of of shampoo you would prefer for personal use. “ “ (1) indicates that the brand in the column is preferred over the in the corresponding row. “ “ (0) means that the row brand is preferred over the column brand. Recording form: Respondent is presented with two objects and asked to select one according to some criterion
  • 101. PROS-AND-CONS 101 Advantages Disadvantages direct comparison and overt choice good for blind tests, physical products, and MDS allows for calculation of percentage of respondents who prefer one stimulus to another can assess rank-orders of stimuli (under assumption of transitivity) possible extensions: “no difference” alternative; graded comparison # of comparisons grows quicker than # of stimuli (for n objects n(n-1)/2 comparisons) violations of transitivity may occur presentation order bias possible preference of A over B does not imply subject’s liking of A little similarity to real choice situation with mult. alternatives Paired Comp.
  • 102. Respondents are presented with several objects simultaneously and are asked to order or rank them according to some criterion COMPARATIVE SCALES: RANK ORDER SCALING 102 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 5. No two brands should receive the same rank number. The criterion 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. Elmex ___________ 4. Pepsodent ___________ 5. Aqua Fresh ___________
  • 106. PROS-AND-CONS 106 Advantages Disadvantages direct comparison more realistic than paired comparison # of comparisons is only (n-1) easier to understand takes less time no intransitive responses can be converted to paired comparison data good for measuring preferences of brands or attributes; conjoint analysis preference of A over B does not imply subject’s liking of A no zero point / separation between liking and disliking only ordinal data Paired Comp.Rank Order
  • 107. Respondents allocate a constant sum of units (points, dollars, chips, %) among a set of stimulus objects with respect to some criterion COMPARATIVE SCALES: CONSTANT SUM SCALING 107 Below are eight attributes of toilet 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. Segment 1 Segment 2 Segment 3 Mildness 8 2 4 Lather 2 4 17 Shrinkage 3 9 7 Price 53 17 9 Fragrance 9 0 19 Packaging 7 5 9 Moisturizing 5 3 20 Cleaning power 13 60 15 Sum 100 100 100 Average response of three segments
  • 110. PROS-AND-CONS 110 Advantages Disadvantages allows for for fine discrimination among stimulus objects without requiring too much time ratio scaled results are limited to the context of stimuli scaled, i.e., not generalizable to other stimuli not included in the study relatively high cognitive burden for respondents, esp. when # of items is large prone to calc. errors (e.g., allocation of 108 or 94 points) Paired Comp.Rank Order Constant Sum
  • 111. A rank order procedure in which objects are sorted into piles based on similarity with respect to some criterion. Usually used to discriminate among a relatively large number (60-140) of objects quickly. COMPARATIVE SCALES: Q-SORT SCALING 111 most highly agreed with least highly agreed with
  • 113. 113 4.Survey: Measurement and Scaling 4.1. Introduction 4.2. Comparative Scales 4.3. Non-comparative Scales 4.4. Multi-item Scales 4.5. Reliability and Validity
  • 114. CLASSIFICATION OF SCALING TECHNIQUES 114 Scaling Techniques Comparative Scales Non-comparative Scales Paired Comparison Rank Order Constant Sum Q-Sort & others Continuous Rating Scales Itemized Rating Scales Likert Semantic Differential Stapel
  • 115. Respondents rate objects by placing a mark at the appropriate position on a line that runs from one extreme of the criterion variable to the other. NON-COMPARATIVE SCALES: CONTINUOUS RATING SCALE 115 How would you rate Wal-Mart as a department store? Probably the worst Probably the best Probably the worst Probably the best Probably the worst Probably the best 0 10 20 30 40 50 60 70 80 90 100 Probably the worst Probably the best very bad neither good nor bad very good 0 10 20 30 40 50 60 70 80 90 100 Version 1 Version 2 Version 3 Version 4
  • 117. Requires respondents to indicate a degree of agreement or disagreement with each of a series of statements about the stimulus object within typically five to seven response categories. ITEMIZED RATING SCALES: LIKERT SCALE 117 Listed below are different opinions about Sears. Please indicate how strongly you agree or disagree with each by using the following scale: Strongly disagree Disagree Neither agree nor disagree Agree Strongly agree 1 Sears sells high-quality merchandise [1] [x] [3] [4] [5] 2 Sears has poor in-store service [1] [x] [3] [4] [5] 3 I like to shop in Sears [1] [2] [x] [4] [5] 4 Sears does not offer a good mix of different brands within a product category [1] [2] [3] [x] [5] 5 The credit policies at Sears are terrible [1] [2] [3] [x] [5] 6 Sears is where America shops [x] [2] [3] [4] [5] 7 I do not like advertising done by Sears [1] [2] [3] [x] [5] 8 Sears sells a wide variety of merchandise [1] [2] [3] [x] [5] 9 Sears charges fair prices [1] [x] [3] [4] [5] 1 = Strongly agree 2 = Disagree 3 = Neither agree nor disagree 4 = Agree 5 = Strongly agree NOTICE the reversed scoring of items 2,4,5, and 7. Reverse the scale for these items prior analyzing to be consistent with the whole set of items, i.e. a higher score should denote a more favorable attitude.
  • 119. SOME COMMONLY USED SCALES IN MARKETING 119 Construct Scale DescriptorsScale DescriptorsScale DescriptorsScale DescriptorsScale 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 (Somewhat) Dissatisfied Neither Dissatisfied Nor Satisfied / Neutral (Somewhat) Satisfied Very Satisfied Purchase Intention Definitely Will Not Buy Probably will Not Buy Might or Might Not Buy Probably Will Buy Definitely Will Buy Purchase Frequency Never Rarely Sometimes Often Very Often Agreement Strongly Disagree Disagree Neither Agree Nor Disagree Agree Strongly Agree Continuous RatingLikert
  • 120. EXAMPLES OF LABELING OF 7 AND 9 POINT SCALES 120  Strongly agree  Agree to a large extent  Rather agree  50/50  Rather disagree  Disagree to a large extent  Strongly disagree Like extremely Like very much Like moderately Like slightly Neither like nor dislike Dislike slightly Dislike moderately Dislike very much Dislike extremely Continuous RatingLikert
  • 121. A rating scale with end point associated with bipolar labels that have semantic meaning. Respondents are to indicate how accurately or inaccurately each term describes the object. ITEMIZED RATING SCALES: SEMANTIC DIFFERENTIAL 121 This part of the study measures what certain department stores mean to you by having you judge them on a series of descriptive scales bounded at each end by one of two bipolar adjectives. Please mark (X) the blank that best indicates how accurately one or the other adjective describes what the store means to you. Please be sure to mark every scale; do not omit any scale. NOTE: The negative adjective 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. Powerful [ ] [ ] [ ] [ ] [X] [ ] [ ] Weak Unreliable [ ] [ ] [ ] [ ] [ ] [X] [ ] Reliable Modern [ ] [ ] [ ] [ ] [ ] [ ] [X] Old fashioned Cold [ ] [ ] [ ] [ ] [ ] [X] [ ] Warm Careful [ ] [X] [ ] [ ] [ ] [ ] [ ] Careless Sears is:
  • 122. A SEMANTIC DIFFERENTIAL SCALE FOR MEASURING SELF-CONCEPTS, PERSON CONCEPTS, AND PRODUCT CONCEPTS 122 Rugged [ ] [ ] [ ] [ ] [ ] [ ] [ ] Delicate Excitable [ ] [ ] [ ] [ ] [ ] [ ] [ ] Calm Uncomfortable [ ] [ ] [ ] [ ] [ ] [ ] [ ] Comfortable Dominating [ ] [ ] [ ] [ ] [ ] [ ] [ ] Submissive Thrifty [ ] [ ] [ ] [ ] [ ] [ ] [ ] Indulgent Pleasant [ ] [ ] [ ] [ ] [ ] [ ] [ ] Unpleasant Contemporary [ ] [ ] [ ] [ ] [ ] [ ] [ ] Non-contemporary Organized [ ] [ ] [ ] [ ] [ ] [ ] [ ] Unorganized Rational [ ] [ ] [ ] [ ] [ ] [ ] [ ] Emotional Youthful [ ] [ ] [ ] [ ] [ ] [ ] [ ] Mature Formal [ ] [ ] [ ] [ ] [ ] [ ] [ ] Informal Orthodox [ ] [ ] [ ] [ ] [ ] [ ] [ ] Liberal Complex [ ] [ ] [ ] [ ] [ ] [ ] [ ] Simple Colorless [ ] [ ] [ ] [ ] [ ] [ ] [ ] Colorful Modest [ ] [ ] [ ] [ ] [ ] [ ] [ ] Vain Rating profiles of different objects / respondents / segments. Each point corresponds to a mean or median of the respective scale. LikertSemantic Diff.
  • 125. An unipolar rating scale with 10 categories numbered from -5 to +5 without neutral point (zero). ITEMIZED RATING SCALES: STAPEL SCALE 125 Please evaluate how accurately each word or phrase describes each of department stores. Select a plus number for phrases you think describe the store accurately. The more accurately you think the phrase describes the store, the larger the plus number you should choose. You should select a minus number for phrases you think do not describe in accurately. The less accurately you think the phrase describes the store, the larger the minus number you should choose. You can select any number, from +5 for phrases you think are very accurate, to -5 for phrases you think are very inaccurate. Sears:+5 +4 +3 +2 +1 High Quality -1 -2 -3 -4 -5 +5 +4 +3 +2 +1 Poor service -1 -2 -3 -4 -5
  • 126. BASIC NON-COMPARATIVE SCALES 126 Scale 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 Likert Scale Degrees of agreements on a 1 (strongly disagree) to 5 (strongly agree) scale Measurement of attitudes Easy to construct, administer and understand More time- consuming Semantic Differential Seven-point scale with bipolar labels Brand, product, and company images Versatile Controversy as to whether the data are interval Stapel Scale Unipolar ten-point scale, -5 to +5, without a neutral point (zero) Measurement of attitudes and images Easy to construct, administer over telephone Confusing an difficult to apply
  • 127. NON-COMPARATIVE ITEMIZED RATING SCALE DECISIONS 127 Number of categories Although there is no single, optimal number, traditional guidelines suggest that there should be between five and nine categories. Odd/even no. of categories If a neutral or indifferent scale response is possible for at least some respondents, an odd number of categories should be used Balanced vs. unbalanced In general, the scale should be balanced to obtain objective data 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. Forced vs. non-forced In situations where the respondents are expected to have no opinion, the accuracy of the data may be improved by a non- forced scale
  • 128. Involvement and knowledge more cat. when respondents are interested in the scaling task or are knowledgable about the objects Nature of the objects do objects lend themselves to fine discrimination? Mode of data collection less categories in telephone interviews Data analysis less cat. for aggregation, broad generalizations or group comp. more cat. for sophisticated statistical analysis, esp. correlation based ones Considerations The greater the number of scale categories, the finer the discrimination among stimulus objects that is possible Most respondents cannot handle more than a few categories NUMBER OF SCALE CATEGORIES 128 Number of categories Although there is no single, optimal number, traditional guidelines suggest that there should be between five and nine categories.
  • 129. BALANCED VS. UNBALANCED SCALES 129 Balanced Scale Unbalanced Scale Extremely good Very good Bad Very bad Extremely bad Extremely good Very good Good Somewhat good Bad Very bad Balanced vs. unbalanced In general, the scale should be balanced to obtain objective data
  • 130. The middle option of an attitudinal scale attracts a substantial # of respondents who might be unsure about their opinion or reluctant to disclose it This can distort measures of central tendency and variance Questions that exclude the "don't know" option tend to produce a greater volume of accurate data ODD VS. EVEN / FORCED VS. NON-FORCED 130 Odd/even no. of categories If a neutral or indifferent scale response is possible for at least some respondents, an odd number of categories should be used Forced vs. non-forced In situations where the respondents are expected to have no opinion, the accuracy of the data may be improved by a non- forced scale Do we want/need “contrast” in controversial attitudes? Are respondents unwilling to answer vs. don’t have an opinion? Use "don't know" or better “not applicable” option for factual questions, but not for attitude questions Use branching to ensue concept familiarity on the respondent’s side Considerations
  • 131. Considerations Providing a verbal description for each category may not improve the accuracy or reliability of the data vs. scale ambiguity Peaked vs. flat response distributions VERBAL DESCRIPTION 131 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. completely disagree completely agree generally disagree generally agree
  • 132. 132 4.Survey: Measurement and Scaling 4.1. Introduction 4.2. Comparative Scales 4.3. Non-comparative Scales 4.4. Multi-item Scales 4.5. Reliability and Validity
  • 133. LATENT CONSTRUCTS 133 A Latent Construct is a variable that cannot be observed or measured directly but can be inferred from other observable measurable variables. Thus, the researcher must capture the variable through questions representing the presence/level of the variable in question. A Latent Construct satisfied [ ] [ ] [ ] [ ] [ ] [ ] [ ] dissatisfied pleased [ ] [ ] [ ] [ ] [ ] [ ] [ ] displeased favorable [ ] [ ] [ ] [ ] [ ] [ ] [ ] unfavorable pleasant [ ] [ ] [ ] [ ] [ ] [ ] [ ] unpleasant I like it very much [ ] [ ] [ ] [ ] [ ] [ ] [ ] I didn't like it at all contented [ ] [ ] [ ] [ ] [ ] [ ] [ ] frustrated delighted [ ] [ ] [ ] [ ] [ ] [ ] [ ] terrible Please indicate how satisfied you were with your purchase of _____ by checking the space that best gives your answer. α=.84
  • 134. LATENT CONSTRUCTS & MULTI-ITEM SCALES 134 Advantages allow to assess abstract concepts make it easier to understand the data and phenomenon reduce dimensionality of data through aggregating a large number of observable variables in a model to represent an underlying concept link observable (“sub-symbolic”) data of the real world to symbolic data in the modeled world Satisfaction Loyalty Trust Service Quality Purchase intention Attitude Toward the Brand Involvement Price Perception Website Ease-of-Use ... Examples
  • 135. SECURE CUSTOMER INDEXTM ASSESSING CONSUMER LOYALTY AND RETENTION 135 Secure Customer Very satisfied Definitely would recommend Definitely will use again D. Randall Brandt (1996), “Secure Customer Index”, Maritz Research Secure Customers % very satisfied/definitely would repeat/definitely would recommend Favorable Customers % giving at least "second best" response on all three measures of satisfaction and loyalty Vulnerable Customers % somewhat satisfied/might or might not repeat/ might or might not recommend At Risk Customers % somewhat satisfied or dissatisfied/probably or definitely would not repeat/probably or definitely would not recommend Overall Satisfaction 4 = very satisfied 3 = somewhat satisfied 2 = somewhat dissatisfied 1 = very dissatisfied Willingness to Recommend 5 = definitely would recommend 4 = probably would recommend 3 = might or might not recommend 2= probably would not recommend 1= definitely would recommend Likelihood to Use Again 5 = definitely will use again 4 = probably will use again 3= might or might not use again 2= probably will not use again 1 = definitely will not use again
  • 136. MULTI-ITEM SCALES: MAKE OR STEAL 136 Develop a theory Generate an initial pool of items: theory, secondary data, and qualitative research Select a reduced set of items based on qualitative judgement Collect data from a large pretest sample Perform statistical analysis Develop a purified scale Collect more data from a different sample Evaluate scale reliability, validity, and generalizability Prepare the final scale Brunner, Gordon C. II (2012), “Marketing Scales Handbook: A Compilation of Multi-Item Measures for Consumer Behavior & Advertising Research”, Vol. 6, available as PDF at www.marketingscales.com Journal of the Academy of Marketing Science (JAMS) Journal of Advertising (JA) Journal of Consumer Research (JCR) Journal of Marketing (JM) Journal of Marketing Research (JMR) Journal of Retailing (JR)
  • 137. MARKETING SCALES HANDBOOK: EXAMPLES 137 Excerpt from Table of Contents: Satisfaction Scales Example of a Scale Copyright material. For educational purposes and use within the current class only!!! Reproduction, copying, and/or dissemination in any form is strictly prohibited by the copyright holder.
  • 138. 138 Scale Variants to Measure a Construct Copyright material. For educational purposes and use within the current class only!!! Reproduction, copying, and/or dissemination in any form is strictly prohibited by the copyright holder.
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  • 150. 150 4.Survey: Measurement and Scaling 4.1. Introduction 4.2. Comparative Scales 4.3. Non-comparative Scales 4.4. Multi-item Scales 4.5. Reliability and Validity
  • 151. MULTI-ITEM SCALES: MEASUREMENT ACCURACY 151 Measurement A measurement is not the true value of the characteristic of interest but rather an observation of it. XO = XT + XS + XR where XO = the observed score of measurement XT = the true score of characteristic XS = systematic error XR = random error The True Score Model
  • 152. RELIABILITY & VALIDITY 152 XO = XT + XS + XR Reliability extent to which a scale produces consistent results in repeated measurements absence of random error ( XR → 0) reliability of a multi-item scale is denoted as Cronbach’s alpha (0≥α≥1) values of α≥0.7 are conside- red satisfactory Validity extent to which differences in observed scale scores reflect true differences among objects on the characteristic being measured no measurement error ( XO → XT, XS → 0, XR → 0)
  • 153. RELATIONSHIP BETWEEN RELIABILITY & VALIDITY 153 XO = XT + XS + XR validity implies reliability ( XO = XT | XS = 0, XR = 0) unreliability implies invalidity ( XR ≠ 0 | XO = XT +XR ≠ XT) reliability does not imply validity ( XR = 0, XS ≠ 0 | XO = XT +XS ≠ XT) reliability is a necessary, but not sufficient, condition of validity
  • 154. “The purpose of a scale is to allow us to represent respondents with the highest accuracy and reliability. We can’t have one without the other and still believe in our data.” Bart Gamble, vice president, client services, Burke, Inc. 154
  • 155. NET PROMOTER SCORE® COMPETITIVE GROWTH RATES? 155 How likely are you to recommend company/brand/product X to a friend/colleague/relative? Reichheld, Fred (2003) "One Number You Need to Grow", Harvard Business Review Is the scale valid? Is the scale reliable?
  • 156. 156 5.Questionnaire 5.1. Asking Questions 5.2. Overcoming Inability to Answer 5.3. Overcoming Unwillingness to Answer 5.4. Increasing Willingness of Respondents 5.5. Determining the Order of Questions 5.6. What’s Next?
  • 157. QUESTIONNAIRE 157 A Questionnaire is a formalized set of questions for obtaining information from respondents. Objectives of a Questionnaire: translate the information need into a set of specific questions that the respondents can and will answer uplift, motivate, and encourage respondents to become involved in the interview, to cooperate, and to complete the interview minimize response error A Questionnaire
  • 158. ISSUES TO CONSIDER IN QUESTIONNAIRE DESIGN 158 Is the question necessary? Are several questions needed instead of one? Is the respondent informed? Can the respondent remember? Effort required of the respondents Sensitivity of question Legitimate purpose Cultural issues Ease of completion Comprehensiveness Bias in formulation
  • 159. Do you actually believe in the big love? Do you believe in the big love? BIAS IN FORMULATION 159 Q: Do you approve smoking whilst praying? A: No Q: Do you approve praying whilst smoking? A: Yes 0 15 30 45 60 Yes No Uncertain Basis: n = 2100, p <.05 Noelle-Neumann and Petersen (1998), p. 192
  • 160. 160 5.Questionnaire 5.1. Asking Questions 5.2. Overcoming Inability to Answer 5.3. Overcoming Unwillingness to Answer 5.4. Increasing Willingness of Respondents 5.5. Determining the Order of Questions 5.6. What’s Next?
  • 161. ASKING QUESTIONS 161 Avoid ambiguity, confusion, and vagueness Avoid jargon, slang, abbreviations Avoid double-barreled questions Avoid leading Avoid implicit assumptions Avoid implicit alternatives Avoid treating respondent’s belief about a hypothesis as a test of the hypothesis Avoid generalizations and estimates “It is not every question that deserves an answer” Publius Syrus (roman, 1st century B.C.)
  • 162. Define the issue in terms of who, what, when, where, why, and way (the six Ws). Who, what, when, and where are particularly important. Example: Which brand of shampoo do you use? Ask instead: Which brand or brands of shampoo have you personally used at home during the last month? In case of more than one brand, please list all the brands that apply. Avoid Ambiguity, confusion and vagueness ASKING QUESTIONS 162
  • 163. The W’s Defining the Question Who The Respondent It is not clear whether this question relates to the individual respondent or, e.g., the respondent’s total household What The Brand of Shampoo It is unclear how the respondent is to answer this question if more than one brand is used When Unclear The time frame is not specified in this question. The respondent could interpret it as meaning the shampoo used this morning, this, week, or over the past year. Where Unclear At home, at gym, on the road? 163 Which brand of shampoo do you use?
  • 164. Example: What brand of computer do you own? ☐ Windows PC ☐ Apple Ask instead: Do you own a Windows PC? (☐ Yes ☐ No) Do you own an Apple computer? (☐ Yes ☐ No) Even better: What brand of computer do you own? ☐ Do not own a computer ☐ Windows PC ☐ Apple ☐ Other Avoid Ambiguity, confusion and vagueness Example: Are you satisfied with your current auto insurance? ☐ Yes ☐ No Ask instead: Are you satisfied with your current auto insurance? ☐ Yes ☐ No ☐ Don’t have auto insurance Even better: 1. Do you currently have a life insurance policy? (☐ Yes ☐ No). If no, go to question 3 2. Are you satisfied with your current auto insurance? (☐ Yes ☐ No) ASKING QUESTIONS 164
  • 165. Example: In a typical month, how often do you shop in department stores? ☐ Never ☐ Occasionally ☐ Sometimes ☐ Often ☐ Regularly Ask instead: In a typical month, how often do you shop in department stores? ☐ Less than once ☐ 1 or 2 times ☐ 3 or 4 times ☐ More than 4 times Avoid Ambiguity, confusion and vagueness ASKING QUESTIONS 165 Whenever using words “will”, “could”, “might”, or “may” in a question, you might suspect that the question asks a time- related question. scales and options should be unambiguous too
  • 166. Use ordinary words Avoid jargon, slang, abbreviations Example: Do you think the distribution of soft drinks is adequate? Ask instead: Do you think soft drinks are readily available when you want to buy them? ASKING QUESTIONS 166 Example: What was your AGI last year? $ _______
  • 167. Are several questions needed instead of one? Avoid double-barreled questions Example: Do you think Coca-Cola is a tasty and refreshing soft drink? Ask instead: 1. Do you think Coca-Cola is a tasty soft drink? 2. Do you think Coca-Cola is a refreshing soft drink? ASKING QUESTIONS 167
  • 168. If you want a certain answer - why ask? Avoid leading Example: Do you help the environment by using canvas shopping bags? Ask instead: Do you use canvas shopping bags? ASKING QUESTIONS 168
  • 169. The answer should not depend on upon implicit assumptions about what will happen as a consequence. Example: Are you in favor of a balanced budget? Ask instead: Are you in favor of a balanced budget it it would result in an increase in the personal income tax? ASKING QUESTIONS 169 Avoid implicit assumptions
  • 170. An alternative that is not explicitly expressed in the options is an implicit alternative. ASKING QUESTIONS 170 Avoid implicit alternatives Example: Do you like to fly when traveling short distances? Ask instead: Do you like to fly when traveling short distances, or would you rather drive?
  • 171. Beliefs are only a biased representation of reality Example: Do you think more educated people wear fur clothing? Ask instead: 1. What is your education level? 2. Do you wear fur clothing? ASKING QUESTIONS 171 Avoid treating beliefs as real facts
  • 172. Don’t task respondents’ memory and math skills Example: What is the annual per capita expenditure on groceries in your household? Ask instead: 1. What is the monthly (or weekly) expenditure on groceries in your household? 2. How many member are there in your household? ASKING QUESTIONS 172 Avoid generalizations and estimates
  • 173. 173 5.Questionnaire 5.1. Asking Questions 5.2. Overcoming Inability to Answer 5.3. Overcoming Unwillingness to Answer 5.4. Increasing Willingness of Respondents 5.5. Determining the Order of Questions 5.6. What’s Next?
  • 174. OVERCOMING INABILITY TO ANSWER 174 Can the Respondent Remember? Can the Respondent Articulate? Is the Respondent Informed?
  • 175. Respondents will often answer questions even though they are not informed Example: Please indicate how strongly you agree or disagree with the following statement: “The National Bureau of Consumer Complaints provides an effective means for consumers who have purchased a defective product to obtain relief” 51.9% of the lawyers and 75% of the public expressed their opinion, although there is no such entity as the NBCC Use Filter Questions e.g. ask about familiarity and/or frequency of patronage in a study of 10 department stores Use “don’t know” Option OVERCOMING INABILITY TO ANSWER 175 Is the Respondent Informed?
  • 176. The inability to remember leads to errors of omission, telescoping, and creation Example: How many liters of soft drinks did you consume during the last four weeks? Ask instead: How often do you consume soft drinks in a typical week? ☐ Less than once a week ☐ 1 to 3 times per week ☐ 4 or 6 times per week ☐ 7 or more times per week Use aided recall approach (where appropriate) “What brands of soft drinks do you remember being advertised last night on TV?” vs “Which of these brands were advertised last night on TV?” OVERCOMING INABILITY TO ANSWER 176 Can the Respondent Remember?
  • 177. If unable to articulate their responses, respondents are likely to ignore the question and quit the survey Example: If asked to describe the atmosphere of the department store they would prefer to patronage, most respondents may be unable to phrase their answers. Provide aids, e.g., pictures, maps, descriptions If the respondents are given alternative descriptions of store atmosphere, they will be able to indicate the one they like the best. OVERCOMING INABILITY TO ANSWER 177 Can the Respondent Articulate?
  • 178. 178 5.Questionnaire 5.1. Asking Questions 5.2. Overcoming Inability to Answer 5.3. Overcoming Unwillingness to Answer 5.4. Increasing Willingness of Respondents 5.5. Determining the Order of Questions 5.6. What’s Next?
  • 179. OVERCOMING UNWILLINGNESS TO ANSWER 179 Most respondents are unwilling to devote a lot of effort to provide information respond to questions that they consider to be inappropriate for the given context divulge information they do not see as serving a legitimate purpose disclose sensitive information Provide context Legitimate purpose Reduce effort
  • 180. Minimize the effort required of respondents Example: Please list all the departments from which you purchased merchandise on your most recent shopping to a department store. Ask instead: In the list that follows, please check all the departments from which you purchased merchandise on your most recent shopping to a department store. ☐ Women’s dresses ☐ Men’s apparel ☐ Children’s apparel ☐ Cosmetics ……. ☐ Jewelry ☐ Other (please specify) _________________ OVERCOMING UNWILLINGNESS TO ANSWER 180 Reduce effort
  • 181. Some questions may seem appropriate in certain contexts but not in others Example: Questions about personal hygiene habits may be appropriate when asked in a survey sponsored by the Medical Association, but not in one sponsored by a fast-food restaurant Provide context by making a statement: “As a fast-food restaurant, we are very concerned about providing a clean and hygienic environment for our customers. Therefore, we would like to ask you some questions related to personal hygiene.” Provide context OVERCOMING UNWILLINGNESS TO ANSWER 181
  • 182. Explain why the data is needed Example: Why should a firm marketing cereals want to know the respondents’ age, income, and occupation? Legitimate the request information: “To determine how the consumption of cereals vary among people of different ages, incomes, and occupation, we need information on ...” Legitimate purpose OVERCOMING UNWILLINGNESS TO ANSWER 182
  • 183. 183 5.Questionnaire 5.1. Asking Questions 5.2. Overcoming Inability to Answer 5.3. Overcoming Unwillingness to Answer 5.4. Increasing Willingness of Respondents 5.5. Determining the Order of Questions 5.6. What’s Next?
  • 184. INCREASING WILLINGNESS OF RESPONDENTS 184 Place sensitive topics at the end of the questionnaire Preface questions with a statement that the behavior is of interest in common Ask the question using third-person technique: phrase the question as if it referred to other people Hide the question in a group of other questions Provide response categories rather than asking for specific figures Sensitive Topics: - money - family life - political and religious beliefs - involvement in accidents or crimes
  • 185. 185 5.Questionnaire 5.1. Asking Questions 5.2. Overcoming Inability to Answer 5.3. Overcoming Unwillingness to Answer 5.4. Increasing Willingness of Respondents 5.5. Determining the Order of Questions 5.6. What’s Next?
  • 186. DETERMINING THE ORDER OF QUESTIONS 186 Opening Questions The opening questions should be interesting, simple, and non-threatening. Type of Information As a general guideline, basic information should be obtained first, followed by classification, and, finally, identification information. Difficult Questions Difficult questions or questions which are sensitive, embarrassing, complex, or dull, should be placed late in the sequence.
  • 187. DETERMINING THE ORDER OF QUESTIONS 187 Effect on Subsequent Questions (funneling) General questions should precede the specific questions 1. What considerations are important to you in selecting a department store? 2. In selecting a department store, how important is convenience of location? Logical Order / Branching Questions The question being branched should be placed as close as possible to the question causing the branching. The branching questions should be ordered so that the respondents cannot anticipate what additional information will be reuired.
  • 188. EXAMPLE: FLOWCHART OF A QUESTIONNAIRE 188 Introduction Store Charge Card Ownership of Store, Bank, and/or other Charge Cards Purchased products in a specific department store during the last two months How was payment made? Ever purchased products in a departments store? Bank Charge Card Other Charge Card Intention to use Store, Bank, or Other Charge Cards yes no yes no Cash Other Credit
  • 189. 189 5.Questionnaire 5.1. Asking Questions 5.2. Overcoming Inability to Answer 5.3. Overcoming Unwillingness to Answer 5.4. Increasing Willingness of Respondents 5.5. Determining the Order of Questions 5.6. What’s Next?
  • 190. What’s Next? 190 Introduction Catch the respondents’ interest Explain the reasons & objectives Ask for their help Tell that their support is valuable Tell how much time it will last Emphasize the anonymity Incentivize (non-monetary incentives)
  • 191. What’s Next? 191 Pretest! Pretest! Pretest!!! question content wording sequence form and layout question difficulty instructions… analysis procedures
  • 192. RECAP 192 1. Develop a flow chart of the information required based on the marketing research problem Once the entire sequence is laid out, the interrelationships should become clear Match up the actual data you would expect to collect from the questionnaire against the information needs listed in the flow chart Be specific in the objective for each area of information and data. You should be able to write an objective for each area so specifically that it guides your construction of the questions. 2. At this stage, put on your “critic’s” hat and go back over the flowchart and ask Do I need to know it and know exactly what I am going to do with it? or It would be nice to know it but I do not have to have it
  • 193. 193 6.Sampling 6.1. Non-probability Sampling 6.2. Probability Sampling 6.3. Choosing Non-Probability vs. Probability Sampling 6.4. Sample Size
  • 194. 194 The world’s most famous newspaper error President Harry Truman against Thomas Dewey Chicago Tribute prepared an incorrect headline without first getting accurate information Reason? → bias → inaccurate opinion polls
  • 195. 195
  • 196. 196 Yes, dear Dilbert, it was the wrong Sample
  • 197. SAMPLING 197 Population the group of people we wish to understand. Populations are often segmented by demographic or Sample a subset of population that represents the whole Most research cannot test everyone. Instead a sample of the whole population is selected and tested. If this is done well, the results can be applied to the whole population. This selection and testing of a sample is called sampling. If a sample is poorly chosen, all the data may be useless.
  • 198. SAMPLING: TWO GENERAL METHODS 198 This relies on personal judgement of theresearcher (often on people available, e.g.,people passing in the street or walkingthrough a mall). This may yield good estimates of populationcharacteristics, however, doesn’t allow forobjective evaluation of the precision ofsample results. That is, the results are notprojectable to the population. Non- probability Sampling Here, sampling units are selected by chance, i.e., randomly. This randomness allows applying statistical techniques to determine the precision of the sample estimates and their confidence intervals. The results are generalizable and projectable to the population from which the sample is drawn. Probability Sampling
  • 199. CLASSIFICATION OF SAMPLING TECHNIQUES 199 Sampling Techniques Non-probability Probability Convenience Sampling Judgmental Sampling Quota Sampling Snowball Sampling Stratified Sampling Cluster Sampling Other Samp- ling Techniques Systematic Sampling Simple Random Sampling Proportionate Disproportionate
  • 200. 200 6.Sampling 6.1. Non-probability Sampling 6.2. Probability Sampling 6.3. Choosing Non-Probability vs. Probability Sampling 6.4. Sample Size
  • 201. CLASSIFICATION OF SAMPLING TECHNIQUES 201 Sampling Techniques Non-probability Probability Convenience Sampling Judgmental Sampling Quota Sampling Snowball Sampling Stratified Sampling Cluster Sampling Other Samp- ling Techniques Systematic Sampling Simple Random Sampling Proportionate Disproportionate
  • 202. CONVENIENCE SAMPLING 202 Depth Interview attempts to obtain a sample of convenient respondents. Often, respondents are selected because they happen to be in the right place at right time. students or members of social organizations mall intercept interviews without qualifying the respondents “people on the street” interviews tear-out questionnaires in magazines Convenience Sampling
  • 203. JUDGMENTAL SAMPLING 203 a form of convenience sampling in which the population elements are selected based on the judgement of the researcher test markets purchase engineers selected in industrial marketing research mothers as diaper “users” Judgmental Sampling
  • 204. Control Characteristic Population Composition Sample CompositionSample Composition Control Characteristic Percentage Percentage Number Sex Male Female 48 52 ------- 100 48 52 ------- 100 480 520 ------- 1000 Age 18-30 31-45 45-60 Over 60 27 39 16 18 ------- 100 27 39 16 18 ------- 100 270 390 160 180 ------- 1000 QUOTA SAMPLING 204 develop control categories, or quotas, of population elements (e.g., sex, age, race, income, company size, turnover, etc.) so that the proportion of the elements possessing these characteristics in the sample reflects their distribution in the population. The elements themselves are selected based on convenience or judgment. The only requirement, however, is that the elements selected fit the control characteristics (quota). Quota Sampling Often used in online surveys
  • 205. SNOWBALL SAMPLING 205 an initial group of respondents is selected (usually) at random. After being interviewed, these respondents are asked to identify others who belong to the target population of interest. Subsequent respondents are selected based on the referrals. Good for locating the desired characteristic in the population: reaching hard-to-reach respondents (e.g., government services, “food stamps”, drug users) estimating characteristics that are rare in the population identifying buyer-seller pairs in industrial research Snowball Sampling Often used in online surveys Very favored by students
  • 206. 206 6.Sampling 6.1. Non-probability Sampling 6.2. Probability Sampling 6.3. Choosing Non-Probability vs. Probability Sampling 6.4. Sample Size
  • 207. CLASSIFICATION OF SAMPLING TECHNIQUES 207 Sampling Techniques Non-probability Probability Convenience Sampling Judgmental Sampling Quota Sampling Snowball Sampling Stratified Sampling Cluster Sampling Other Samp- ling Techniques Systematic Sampling Simple Random Sampling Proportionate Disproportionate Require knowledge about the population
  • 208. Each element in the population has a known and equal probability of selection Each possible sample of a given size (n) has a known probability of being the sample actually selected This implies that every element is selected independently of every other element. Simple Random Sampling SRS & SYSTEMATIC SAMPLING 208 The sample is chosen by selecting a random starting point and then picking every i-th element in succession from the sampling frame The sampling interval, i, is determined by dividing the population size N by the sample size n, i.e., i=N/n Systematic Sampling Require knowledge about the population start here take every i-th element select randomly i i i
  • 209. STRATIFIED SAMPLING 209 is obtained by separating the population into non-overlapping groups called strata and then obtaining a proportional simple random sample from each group. The individuals within each group should be similar in some way. Good for: highlighting a specific subgroup within the population observing existing relationships between two or more subgroups representative sampling of even the smallest and most inaccessible subgroups in the population a higher statistical precision Stratified Sampling Proportionate Stratum A B C Population Size 100 200 300 Sampling Fraction 1/2 1/2 1/2 Final Sample Size 50 100 150 Stratum A B C Population Size 100 200 300 Sampling Fraction 1/5 1/2 1/3 Final Sample Size 20 100 100 Disproportionate Require knowledge about the population
  • 210. CLUSTER SAMPLING 210 the target population is first divided into mutually exclusive and collectively exhaustive subpopulations, or clusters. Than a random sample of clusters is selected, based on SRS. Good for: covering large geographic areas reducing survey costs when constructing a complete list of population elements is difficult when the population concentrated in natural clusters (e.g., blocks, cities, schools, hospitals, boxes, etc.) Cluster Sampling Require knowledge about the population For each cluster, either all the elements are included in the sample (one-stage) or a sample of elements is drawn probabilistically (two-sage).
  • 211. 211 6.Sampling 6.1. Non-probability Sampling 6.2. Probability Sampling 6.3. Choosing Non-Probability vs. Probability Sampling 6.4. Sample Size
  • 212. STRENGTHS AND WEAKNESSES OF BASIC SAMPLING TECHNIQUES 212 Technique Strengths Weaknesses Non-probability Sampling Convenience sampling Least expensive, least time consuming, most convenient Selection bias, sample not representative, not recommended for descriptive or causal research Judgmental sampling Low cost, convenient, not time consuming Does not allow generalization, subjective Quota sampling Sample can be controlled for certain characteristics Selection bias, no assurance of representativeness Snowball sampling Can estimate rare characteristics Time consuming in the field research Probability Sampling Simple random sampling (SRS) Easily understood, results projectable Difficult to construct sampling frame, expensive, lower precision, no assurance of representativeness Systematic sampling Can increase representativeness, easier to implement than SRS Can decrease representativeness Stratified sampling Includes all important subpopulations, precision Difficult to select relevant stratification variables, not feasible to stratify on many variables, expensive Cluster sampling Easy to implement, cost effective Imprecise, difficult to compute and interpret results
  • 213. 213 The middle option of an attitudinal scale attracts a substantial # of respondents who might be unsure about their opinion or reluctant to disclose it This can distort measures of central tendency and variance Questions that exclude the "don't know" option tend to produce a greater volume of accurate data Do we want/need “contrast” in controversial attitudes? Are respondents unwilling to answer vs. don’t have an opinion? Use "don't know" or better “not applicable” option for factual questions, but not for attitude questions Use branching to ensue concept familiarity on the respondent’s side Non-probability Probability
  • 214. 214 Non-comparative Scales each object is scaled independently resulting data is generally assumed to be interval or ratio scaled Comparative Scales involve the direct comparison of stimulus objects. data must be interpreted in relative terms have only ordinal and rank- order properties nature of the research variability in the population statistical considerations
  • 215. 215 6.Sampling 6.1. Non-probability Sampling 6.2. Probability Sampling 6.3. Choosing Non-Probability vs. Probability Sampling 6.4. Sample Size
  • 216. DETERMINING THE SAMPLE SIZE 216 The sample size does not depend on the size of the population being studied, but rather it depends on qualitative factors of the research. desired precision of estimates knowledge of population parameters number of variables nature of the analysis importance of the decision incidence and completion rates resource constraints Determining the Sample Size
  • 217. SAMPLE SIZES USED IN MARKETING RESEARCH STUDIES 217 Type of Study Minimum Size Typical Size Problem identification research (e.g., market potential) 500 1,000 - 2,000 Problem solving research (e.g., pricing) 200 300 - 500 Product tests 200 300 - 500 Test-market studies 200 300 - 500 TV/Radio/Print advertising (per commercial ad tested) 150 200 - 300 Test-market audits 10 stores 10 - 20 stores Focus groups 6 groups 10 - 15 groups
  • 218. MARGIN OF ERROR APPROACH TO DETERMINING SAMPLE SIZE 218
  • 219. MARGIN OF ERROR APPROACH TO DETERMINING SAMPLE SIZE 219 What is your primary daily media channel?
  • 220. MARGIN OF ERROR APPROACH TO DETERMINING SAMPLE SIZE 220 What is your primary daily media channel? How accurate is this statistic? What is the margin of error? The Margin of Error is the measure of accuracy of a survey. The smaller the margin of error, the more accurate are the estimates of a survey.
  • 221. MARGIN OF ERROR APPROACH TO DETERMINING SAMPLE SIZE 221 Means use this formula when evaluating estimatesof population means Proportions use this when evaluating estimates of proportions Means Proportions E = z σ n E = z π(1−π) n x = real population parameter x = sample statistic E = margin of error ^ x = ˆx ± E z = z-value for a given level of confidenceσ = standard deviation of a population parametern = sample size z = z-value for a given level of confidence π = estimate of the proportion in the population n = sample size
  • 222. MARGIN OF ERROR APPROACH TO DETERMINING SAMPLE SIZE 222 Means use this formula when evaluating estimatesof population means Proportions use this when evaluating estimates of proportions Means Proportions E = z σ n E = z π(1−π) n x = real population parameter x = sample statistic E = margin of error ^ x = ˆx ± E z = z-value for a given level of confidenceσ = standard deviation of a population parametern = sample size z = z-value for a given level of confidence π = estimate of the proportion in the population n = sample size unlikely to be known
  • 223. MARGIN OF ERROR APPROACH TO DETERMINING SAMPLE SIZE 223 Means use this formula when evaluating estimatesof population means Proportions use this when evaluating estimates of proportions Means Proportions E = z σ n E = z π(1−π) n x = real population parameter x = sample statistic E = margin of error ^ x = ˆx ± E z = z-value for a given level of confidenceσ = standard deviation of a population parametern = sample size z = z-value for a given level of confidence π = estimate of the proportion in the population n = sample size unlikely to be known has a maximum at π = .5
  • 224. MARGIN OF ERROR APPROACH TO DETERMINING SAMPLE SIZE 224 maximum margin of error for 95% level of confidence Proportions E = z π(1−π) n x = real population parameter x = sample statistic E = margin of error ^ x = ˆx ± E z-values z = 1.96 for 95% level of confidence z = 2.58 for 99% level of confidence =1.96 0.5(1− 0.5) n ≈ 1 n
  • 225. MARGIN OF ERROR APPROACH TO DETERMINING SAMPLE SIZE 225 What is your primary daily media channel? How accurate is this statistic? What is the margin of error? Margin of Error = 1/√n 48,804 people in sample √48,804 = 220.916 1/221 = 0.0045 *100 = 0.45% x = 61% ± 0.45% 60.55% to 61.45% x = ˆx ± E calculations are approximate values for 95% level of confidence
  • 226. MARGIN OF ERROR APPROACH TO DETERMINING SAMPLE SIZE 226 What is your primary daily media channel? How big should the sample be taking margin of error of ±1% into account? Sample Size n = (1/Margin of Error)^2 n±1%= (1/0.01)^2 = (100)^2 = 10,000 n±2%= (1/0.02)^2 = (50)^2 = 2,500 n±5%= (1/0.05)^2 = (20)^2 = 400 n±10%= (1/0.1)^2 = (10)^2 = 100 n ≈ 1 E " # $ % & ' 2 E ≈ 1 n calculations are approximate values for 95% level of confidence
  • 227. MARGIN OF ERROR APPROACH TO DETERMINING SAMPLE SIZE 227 What is your primary daily media channel? calculations are approximate values for 95% level of confidence Sample Size n = (1/Margin of Error)^2 Sample Size does not depend on population. n±1%= (1/0.01)^2 = (100)^2 = 10,000 What if the population under study consists of only 100 elements? (e.g., firms producing cars) Corrections needed, when sample size exceeds 10% of the population
  • 228. MARGIN OF ERROR APPROACH TO DETERMINING SAMPLE SIZE 228 What is your primary daily media channel? calculations are approximate values for 95% level of confidence Correction of the Sample Size ncorr = n (1+(n −1) / population) Corrections needed, when sample size exceeds 10% of the population
  • 229. MARGIN OF ERROR APPROACH TO DETERMINING SAMPLE SIZE 229 What is your primary daily media channel? calculations are approximate values for 95% level of confidence n±1%= (1/0.01)^2 = (100)^2 = 10,000 What if the population under study consists of only 100 elements? (e.g., firms producing cars) ncorr = n (1+(n −1) / population) ncorr = 10,000 (1+(10,000 −1) /100) = 10,000 (1+ 9,999 /100) = 10,000 (100.99) = 99.02 Corrections needed, when sample size exceeds 10% of the population
  • 230. MARGIN OF ERROR APPROACH TO DETERMINING SAMPLE SIZE 230 What is your primary daily media channel? calculations are approximate values for 95% level of confidence n±5%= (1/0.05)^2 = (20)^2 = 400 What if the population under study consists of only 100 elements? (e.g., firms producing cars) ncorr = n (1+(n −1) / population) ncorr = 400 (1+(400 −1) /100) = 400 (1+399 /100) = 400 (4.99) = 80.16 Corrections needed, when sample size exceeds 10% of the population
  • 231. MARGIN OF ERROR APPROACH TO DETERMINING SAMPLE SIZE 231 What is your primary daily media channel? calculations are approximate values for 95% level of confidence Corrections needed, when sample size exceeds 10% of the population n±10%= (1/0.1)^2 = (10)^2 = 100 What if the population under study consists of only 100 elements? (e.g., firms producing cars) ncorr = n (1+(n −1) / population) ncorr = 100 (1+(100 −1) /100) = 100 (1+ 99 /100) = 100 (1.99) = 50.25
  • 232. A NOTE ON CONFIDENCE INTERVAL 232 A confidence interval estimate is an interval of numbers, along with a measure of the likelihood that the interval contains the unknown parameter. The level of confidence is the expected proportion of intervals that will contain the parameter if a large number of samples is maintained. Confidence Interval & Level of Confidence Suppose we're wondering what the average number of hours that people at Siemens spend working. We might take a sample of 30 individuals and find a sample mean of 7.5 hours. If we say that we're 95% confident that the real mean is somewhere between 7.2 and 7.8, we're saying that if we were to repeat this with new samples, and gave a margin of ±0.3 hours every time, our interval would contain the actual mean 95% of the time.
  • 233. The higher the confidence we need, the wider the confidence interval and the greater the margin of error will be CONFIDENCE INTERVAL, MARGIN OF ERROR, AND SAMPLE SIZE 233 maximum margin of error for 99% level of confidence E = z π(1−π) n z-values z = 1.96 for 95% level of confidence z = 2.58 for 99% level of confidence = 2.58 0.5(1− 0.5) n = 1.29 n
  • 234. The higher the confidence we need, the wider the confidence interval and the greater the margin of error will be CONFIDENCE INTERVAL, MARGIN OF ERROR, AND SAMPLE SIZE 234 maximum margin of error for 99% level of confidence E = z π(1−π) n z-values z = 1.96 for 95% level of confidence z = 2.58 for 99% level of confidence = 2.58 0.5(1− 0.5) n = 1.29 n To reduce the margin of error we have to increase the sample size higher levels of confidence require larger samples smaller margins of error require larger samples
  • 235. 235 7.Data Analysis: A Concise Overview of Statistical Techniques 7.1. Descriptive Statistics: Some Popular Displays of Data 7.1.1. Organizing Qualitative Data 7.1.2. Organizing Quantitative Data 7.1.3. Summarizing Data Numerically 7.1.4. Cross-Tabulations 7.2. Inferential Statistics: Can the results be generalized to population? 7.2.1. Hypothesis Testing 7.2.2. Strength of a Relationship in Cross-Tabulation 7.2.3. Describing the Relationship Between Two (Ratio Scaled) Variables
  • 236. TYPES OF STATISTICAL DATA ANALYSIS 236 Inferential Inferential statistics are techniques that allow making generalizations about a population based on random samples drawn from the population. Allow assessing causality and quantifying relationships between variables. Descriptive Descriptive statistics provide simple summaries about the sample and about the observations that have been made. Include the numbers, tables, charts, and graphs used to describe, organize, summarize, and present raw data.
  • 237. 237 7.Data Analysis: A Concise Overview of Statistical Techniques 7.1. Descriptive Statistics: Some Popular Displays of Data 7.1.1. Organizing Qualitative Data 7.1.2. Organizing Quantitative Data 7.1.3. Summarizing Data Numerically 7.1.4. Cross-Tabulations 7.2. Inferential Statistics: Can the results be generalized to population?
  • 238. FREQUENCY AND RELATIVE FREQUENCY TABLES 238 Original Data A frequency distribution lists each category of data and the number of occurrences for each category The relative frequency is the proportion (or percent) of observations within a category A relative frequency distribution lists each category of data together with the relative frequency of each category. relative frequency = frequency sumof all frequencies Excel how to video: http://faculty.elgin.edu/dkernler/statistics/videos/excel2007/freq-table-1.mov
  • 239. BAR GRAPHS 239 Original Data Bar Graphs / Bar Charts 1. heights can be frequency or relative frequency 2. bars must not touch Excel how to video: http://faculty.elgin.edu/dkernler/statistics/videos/excel2007/bar-graph.mov
  • 240. PIE CHARTS 240 Pie Charts 1. should always include the relative frequency 2. also should include labels, either directly or as a legend Excel how to video: http://faculty.elgin.edu/dkernler/statistics/videos/excel2007/pie-chart.mov