PART 1

COURSE SYLLABUS/OUTLINE
Introduction to Marketing
Research
Dr. Doherty
Tobin College of Business
St. John’s University
MARKETING CASE REPORT
FORMAT
A.
Executive Summary: SelfContained Document, one to two
pages
• Statement of Purpose and Issues to be
Addressed
• Research Method Used to Address
Issues
• Salient Findings
(Appears before Table of Contents)
Table of Contents
B.
•
Subject and Page Numbers Including All
Exhibit References
Introduction
I.



II.
Background
Purpose and/or Problem Definition
Objectives of Report
Methodology


Specific Methodology – Why!!!
Data/Information to be Studied
III. Case Analysis



Application of Specific Methodology to Case
Discussion/Explanation of Analysis
Interpretation of Tables and Charts. (It is not
acceptable to merely refer to Tables, e.g., see
Table X)
IV. Findings and/or Conclusions
V. Appendices
VI. Other Requirements






Paragraph and Sub Paragraph headings
Identification of all exhibits which are to
be explained and referenced in text
No Misspellings!!!!
Proper Grammar
Interesting Style
On Time Delivery of Oral and Written
Report
Marketing Research and the Four Ps
1.
2.
3.
4.
Products




New Products
Evaluating Packaging and Brand Designs
Compassion Studies With Competitor’s Products
Consumer Evaluation of Current Products




Analysis of Different Storage or Transportation
Methods
Analysis of Alternative Sites
Determination Of Inventory Levels
Growth Rates of Different Channels




Testing Different Ad. Messages
Establishing Sales Territories
Selecting Media
Evaluating Ad. Effectiveness
Place (Distribution Channels)
Promotion
Pricing
Research on Markets
Forecasting Demand
Providing Information of General Trends
Providing Information For Segmenting
Markets
Developing Customer Profiles
Identifying New Markets For Existing
Products
Identifying New Product Needs
Foreign Markets
Elements Of The Marketing Mix That
Compose A Cohesive Marketing Program
Marketing
Manager
Product
Features
Brand name
Packaging
Service
Warranty
Place
Outlets
Channels
Coverage
Transportation
Stock level
Price
Promotion
Advertising
Personal selling
Sales promotion
Publicity
Place
Product
Promotion
Price
List price
Discounts
Allowances
Credit terms
Payment period
Marketing System Model
Independent Variables (Y)
Dependent Variables (X)
Controllable (XI)
Environmental (X2)
Etc
Behavorial (YI)
Sales
Demand
Psychological:
Preference
Intentions
Liking
Awareness
Performance Measures (Y2)
Market Share
Profits
Cash Flow
ROE
ROI
P/E
Brand Equity
Marketing Research
Definition:
A scientific approach to
(a) the collection; (b) analysis; and (c) presentation
of data/information to be used in the management
decision making process
Three Generic Approaches
I.
Exploratory
II.
Descriptive
III.
Causal/Experimental
Applications: See Tables 1 and 2
Exploratory Research

When:
• Problem Not Well Defined
• No Working Hypothesis
• Little to No Relevant Information

Purpose:
•
•
•
•
•
•
Identifying Information Sources
Identifying Potential Causes
Develop Hypothesis
Clarify Concepts
Familiarize Analyst with the Problem
Formulate the Problem for a More Precise
Investigation
The Exploratory Approach
Purpose: Identify Potential Relevant
Factors (Don’t try to solve the
problem!)
 Develop Hypothesis
 Establish priorities for further
research
 Identify information and data
sources
 Clarify concepts
The Exploratory Approach
Five Popular Exploratory Approaches:
1) Literature Search
2) Experience Survey
3) Analysis of Selected Cases
4) Focus Groups
5) “Small” Sample/Surveys/Interviews
The Descriptive Approach
Purpose: Test Hypothesis

Analyze Data

Develop Findings/Conclusions
Two Types (Depending on Type of Data)
A.
Longitudinal (Time Series)
 True Panel
 Omnibus Panel
B.
Cross Sectional
 Field Survey
 Field Study
True Panel Application
The Brand Switching Matrix or Turnover
Table (see your textbook!)
Time Period
Brand
T1
T2
A
200
250
B
300
270
C
350
330
D
150
150
Total 1000 1000
Brand
A
Time
(T1)
Time (T2)
B
C
25
0
B
A
175
(.875)
0
D
0
Total
200
C
0
225
(.750)
0
50
25
300
D
75
20
280
(.800)
0
70
350
55
(.367)
150
150
Total
250
270
330
1000
Applications of Turnover Table
Evaluating:
a)
b)
c)
d)
e)
Price Changes
Promotional Campaigns
New Packaging
New Products
Results can be integrated with other
databases to determine customer
profiles and media habits
Causal/Experimental
Research Design
1.
Scientific Criteria
• Concomitant Variation
• Time Sequence
• Elimination of Other Causes
2.
3.
Controlled Experiment
•
•
•
•
Reflects 1.
Lab vs. Field
Validation
Two Groups: Experimental and Control
•
•
•
•
•
Experiment : Process
Treatments : Alternatives
Test Units : Entities
Dependent Variables : Measures
Extraneous Variables
Basic Concepts Defined




Hold Constant
Randomize Assignment of Treatments
Specific Design
ANCOVA
Types of Evidence That Support a
Causal Inference



Concomitant Variation– evidence of the extent to
which X and Y occur together or vary together in
the way predicted by the hypothesis
Time order of occurrence of variables- evidence
that shows X occurs before Y
Elimination of other possible causal factorsevidence that allows the elimination of factors
other than X as the cause of Y
X– the presumed cause
Y– the presumed effect
Types of Experiments
Laboratory Experiment
Experiment
Scientific investigation in which an
investigator manipulates and
controls one or more independent
variables and observes the
dependent variable for variation
concomitant to the manipulation of
the independent variables
Research investigation in which investigator
creates a situation with exact conditions so
as to control some, and manipulate other,
variables.
Field Experiment
Research study in a realistic situation in
which one or more independent variables
are manipulated by the experimenter under
as carefully controlled conditions as the
situation will permit.
Types of Extraneous Factors That Can Contaminate
Research Results
History—Specific events external to an
experiment, but occurring at the same time,
which may affect the criterion or response
variable.
Maturation—Processes operating within the test
units in an experiment as a function of the
passage of time per se.
Testing—Contaminating effect in an experiment
due to the fact that the process of
experimentation itself affected the observed
response.
Main testing effect—The impact of a prior
observation on a later observation.
Interactive testing effect—The condition
when a prior measurement affects the test unit’s
response to the experimental variable.
Types of Extraneous Factors That Can
Contaminate Research Results
Instrument Variation —Any and all changes in
the measuring device used in an experiment that
might account for differences in two or more
measurements.
Statistical Regression —Tendency of extreme
cases of a phenomenon to move toward a more
central position during the course of an
experiment.
Selection Bias —Contaminating influence in an
experiment occurring when there is no way of
certifying that groups of test units were
equivalent at some prior time.
Experimental Mortality —Experimental condition
in which test units are lost during the course of
an experiment.
Causal/Experimental
Research Design
7.
Pre-Exp. Design (3)
a. After Only: X O
b. Before After: O X O
c. Static Group Comparisons: X O1
O2
Major Errors: H, SB
Causal/Experimental
Research Design
8. True Experimental Design
a. Before/After with Randomization (R)
and Control (C)
R Exp
O1
R
Control
O3
X
R
O1
O2
R
O3
O4
R
X
X = (O – O ) – (O – O3)
X
R
Control
O1
X
O5
O6
EXT = ?
ITE = ?
X=?
O2
X = O1 – O2
O2
O4
R
2
1
4
b. After Only
with
R and
C
R Exp
c. Solomon 4 Group
Problem
O1 = 100
O2 – 160
O3 = 106
O4 = 140
O5 = 150
O6 = 135
Causal/Experimental
Research Design
9.
Quasi Exp (3)
A. Single Time Series
O1 O2 O3 X O4 O5 O6
B. Multiple Time Series
O'1 O'2 O'3 X O'4 O'5 O'6
C. Separate Sample Before/After Design:
R:
R:
O1
X
X
O2
Main Problem of Quasi Approach: History
(Note: 9A is typical of consumer panel investigation data.)
Causal/Experimental
Research Design
10.
Advanced Statistical Design (4)
A.
B.
C.
D.
CRD
RBD
LSD
Factorial
Test Marketing
1.
2.
3.
Who?
Objectives
a. Forecasts: Sales, Market Share; CANNALBALISTIC
EFFECTS
b. Pretest Market Mix
c. Serendipity
Key Decisions
a. How Many Cities?

2 To 6

Importance of Regional Differences Degree of
Uncertainty
b. Which Cities?
Syracuse
Leonia DaytonDes Moines
c. Length Of Test?

2 Months to 2 Years

Average Repurchase Period

Competition Concern

First to Market Importance
Test Marketing Cont’d
d. What Data?





4.
Warehouse
Shipments
Store Audits
Consumer Panels
Buyer Surveys
Trade Attitudes
What Action?
Repurchase
Rate
Trial Rate
High
Low
High
Go!
More Adv.
Low
Product Flaw
Bust!
PART 2
Part 2A
Decision Making Under
Uncertainty
Criteria for Selecting the Best Option
•
MAX/MIN
•
MAX/MAX
MIN/MAX-REGRET
•
•
EXPECTED VALUE
Value of Information

Payoff (Decision) Table
Management
Options
AI
Ej
Eij
Pj
EVENTS (States of Nature)
E1
Ez
En
...
A1
X11
X12
X1n
A2
Xz1
X22
Xzn
An
Xn1
Xnz
Xnn
Prior
Probabilities
(P1)
(Pz)
(Pn)
:
:
:
:
Decision Acts
Events (or Sj = States of Nature)
Payoff or Consequences
Prob. Associated with Ej
ILLUSTRATION
E1
E2
E3
E4
A1
80M
40
-10
-50
A2
30
40
30
10
A3
20
30
40
15
A4
5
10
30
20
Regret Table
E1
E2
E3
E4
MAX
A1
0
0
50
70
70
A2
50
0
10
10
50
A3
60
10
0
5
60
A4
75
30
10
0
75
Part 2B
Marketing Research Case
Study
Bayesian Analysis
Value of Information
Payoff (Decision)
Table
Events (States of Nature)
Manageme
nt Options
E1
E2
…
En
A1
X11
X12
X1n
A2
Xz1
X22
Xzn
An
Xn1
Xnz
Xnn
Prior
Probabilitie
s
(P1)
(Pz)
(Pn)
AI
:
Decision Acts
Ej
:
Events (or Sj = States of Nature)
Xij
:
Payoff or Consequence
Pj
:
Prob associated with Ej
ILLUSTRATION
E1
E2
E3
E4
A1
80M
40
-10
-50
A2
30
40
30
10
A3
20
30
40
15
A4
5
10
30
20
Regret Table
E1
E2
E3
E4
MAX
A1
0
0
50
70
70
A2
50
0
10
10
50
A3
60
10
0
5
60
A4
75
30
10
0
75
Bayesian Case
Objective: Determine Value of Research
Problem
S1
S2
S3
A1
100
50
-50
A2
50
100
-25
A3
-50
0
90
Prior Probs.
0.6
0.3
0.1
P(Sj)
EV(A1)=$70M
EV(A2)=$57.5
M
EV(A3)=-$22M
EV(L1)=$70M
EV(C)=$98M
EV(PI)=$28M= EV(C) – EV(U)
EV(C)= .6(100) + .3(100)+ .1(80) = $98M
EV(PI)= EV(C) – EV(U) = $98M - $70M =
$28M
Conditional Prob. Matrix
Actual Results
Test MKT
Results
S1
S2
S3
Light D
Z1
0.7
0.2
0.1
Mod. D
Z2
0.2
0.6
0.3
Heavy
D
Z3
0.1
0.2
0.6
1.00
1.00
1.00
Must sum to
one
Should,
but not
necessary
to, sum to
one.
Bayesian Work Table
State of
Nature
(Sj or Ej)
Z1 :
S1
S2
S3
Z2 :
S1
S2
S3
Z3 :
S1
S2
S3
Prior
Prob.
P(Sj)
Cond’l
Prob.
P(Zk/Sj)
Joint
Prob.
P(ZkSj)
Posterior
Prob.
P(Sj/Zk)
0.6
0.3
0.1
0.7
0.2
0.1
0.42
0.06
0.01
0.49
0.857
0.122
0.020
1.000
0.6
0.3
0.1
0.2
0.6
0.3
0.12
0.18
0.03
0.33
0.364
0.545
0.091
1.000
0.6
0.3
0.1
0.1
0.2
0.6
0.06
0.06
0.06
0.18
0.333
0.333
0.333
1.000
Computation of Expected Values
from BAYESIAN Work Table
Given:
Z1 (Test MKT. Results show Light D)
EV(A1) = 100(.858) + 50(.122) + -50(.02)
= $90.9M
EV(A2) = 50(.858) + 100(.122) + -25(.02)
= $54.6M
EV(A3) = -50(.858) + 0(.122) + 80(.02) = $-41.3M
Z2 (Test MKT. Results show Moderate D)
EV(A1) = 100(.364) + 50(.545) + -50(.091)
= $59.1M
EV(A2) = 50(.364) + 100(.545) + -25(.091)
= $70.4M
EV(A3) = -50(.364) + 0(.545) + 80(.091) = $-10.9M
Z3 (Test MKT. Results show Heavy D)
EV(A1) = 100(.333) + 50(.333) + -50(.333)
= $33.3M
EV(A2) = 50(.333) + 100(.333) + -25(.333)
= $41.6M
EV(A3) = -50(.333) + 0(.333) + 80(.333) = $10.0M
Probability of Obtaining Each Test
MKT. Result
k
P(Zk)
=
P(S )P(Z /S )
j
k
j
j 1
P(Z1) = P(S1)P(Z1/S1) + P(S2)P(Z1/S2) + P(S3)P(Z1/S3)
= (.6)(.7) + (.3)(.2) + (.1)(.1)
= 0.49
P(Z2) = P(S1)P(Z2/S1) + P(S2)P(Z2/S2) + P(S3)P(Z2/S3)
= (.6)(.2) + (.3)(.6) + (.1)(.3)
= 0.33
P(Z3) = P(S1)P(Z3/S1) + P(S2)P(Z3/S2) + P(S3)P(Z3/S3)
= (.6)(.1) + (.3)(.2) + (.1)(.6)
= 0.18
Probability of Obtaining Each Test
MKT. Result (cont’d)
FORECASTS
Decision
Acts
Opt. Ev
Prob.
Z1
A1
90.9
0.49
Z2
A2
70.4
0.33
Z3
A3
41.6
0.18
EV(Research)
= 90.0(.40) + 70.4(.33) + 41.6(.18)
= $75.26M
EV(U)
= 70.0M
Max Price For Res. = EV(R) – EV(U)
= 75.26 – 70.0
=$5.26M
Case Description
Newco is a manufacturer of natural soft drink
beverages. It has recently experienced a decline
in market share. To reverse this decline,
management is considering a new promotional
program that will cost $1 million. Management
believes that the program may have three
possible effects:
1. Very Favorable: 10% increase in market
share; $4 million increase in profits.
2. Favorable: 5% increase in market share; $1
million increase in profits.
3. Unfavorable: (No Effect on Sales) –
incremental loss of $1 million, the cost of the
program.
Abbey Normal, Director of Marketing Research,
estimates the probability of the three events as
follows:
S1: Very Favorable Consumer Reaction = 0.30
S2: Favorable Consumer Reaction = 0.40
S3: Unfavorable Consumer Reaction = 0.30
Newco is considering a proposal made by
Marketing Testing Experts (MTE), a private
consulting firm, to asses the potential effects of
the program.
MTE has advised Newco that based on its past
experience of assessing promotional programs
that the following results on average have been
obtained:
Customer
Reaction
MTE Experience
Very
Favorable
Favorable
Unfavorable
Strongly
Positive
0.7
0.2
0.0
Moderately
Positive
0.3
0.6
0.2
Slightly Positive
0.0
0.2
0.8
MTE proposes a charge of $250,000 for conducting
the research.
Questions:
1.
Construct the relevant payoff table.
2.
What are the maximin and maximax solutions?
3.
What is the solution according to the expected value
criterion?
4.
What is the value of perfect research information?
5.
Should Newco except MTE’s proposal? Why?
6.
What price would Newco be willing to pay for the study?
7.
What probabilities are critical to the outcome of the
study?
8.
How could the various probabilities that are needed for
such a study be obtained in practice?
Note: There are many computer software packages, that can
be run on a PC, mainframe and microcomputer that can
be used to solve this problem. See, for example, D.A.
Schellinck and R.N. Maddox, Marketing Research: A
Computer Assisted Approach, The Dryden Press, 1987.
PART 3
SECONDARY SOURCES OF
DATA
FIVEFOLD (5)
CLASSIFICATION
1.
INTERNAL
•
•
•
•
•
•
•
P&L
Balance Sheet
Sales Figure
Sales-Call Reports
Invoices
Inventory Records
Prior Research Studies
2.
PERIODICALS & BOOKS
•
•
•
•
•
•
•
Business Periodicals Index (Monthly Publications
that provide a list of business articles appearing
in a wide variety of business publications).
Standard & Poor’s Industry surveys (provides
updated statistics and analyses of industries).
Moody’s Manuals (financial data and names of
executives in major corporations).
Encyclopedia of Associations (provides
information on every major trade and
professional association in the U.S.
Marketing Journals
Trade Magazines (Advertising Age, Chain Store
Age progressive Grocer, Sales and MKT. MGT,
Stores).
Business Magazines (Fortune, Business Week,
Forbes, Barrons, Harvard Business Review, etc.)
3.
COMMERCIAL DATA
•
A.C. Nielsen Co.
1)
2)
Retail Index Service (data on products and brands
sold through retail outlets)
Scan track (Supermarket scanner data)
Electronic Test MKT
a. Scanner Cards for Panel Members
b. Demographics
c. TV Viewers Habit of Panel Members
Media Research Services (Television
Audience)
4) Neodata Service Inc. (Magazine Circ.)
5) Home Services – National Purchase Diary
Panel
• MRCA – National Purchase Diary Panel
National Menu Census (data on home food
consumption)
3)
COMMERCIAL DATA (CONTINUED)
•
Claritas – buying habits of 250,000 U.S. neighborhoods
•
Information Resources Inc. – provide supermarket scanner
data
1.
(InfoScan); also
2.
Promotio Scan – IMPACT of supermarket promotions
•
SAMI/BURKE
Provides reports on warehouse withdrawals to food stores in
selected market areas (SAMI reports) and supermarket
scanner data (SAMSCAN)
•
SIMMONS Market Research Bureau (MRB Group)
Provides annual reports covering television market, sporting
goods, proprietary drugs.
Giving demographic data by sex; income; age and
brand preference (selective market and media reaching
them)
•
Other
Audit Bureau of Circulation Arbitron
Audit and Surveys
Dunn and Bradstreet
National Family Opinion
Standard Rate and Data Service
Stard
GOVERNMENT PUBLICATION
4.
•
Statistical Abstract of MKT Sources (updated
annually)
Provides summary data on: demographic, economy,
social and other aspects of the U.S. economy and
society.
•
County and City Data Book (updated every three
years)
-Presented statistical information for counties, cities
and other geographical units regarding:
- population, education, employment
- aggr. And med. Income – housing
- bank deposit, retail sales, etc.
•
U.S. Industrial Outlook
-Projections of industrial activity by industry and
includes data on:
production
sales
• Marketing Information Guide
Provides a monthly annotated bibliography of
marketing information.
• Other
- Annual Survey of Manufacturers
- Business Statistics
- Census of Manufacturers
- Census of Retail Trade, Wholesale Trade and
Selected Service Industries
- Census of Transportation
- Federal Reserve Bulleting
- Monthly Labor Review
- Survey of Current Business
- Vital Statistics Report
5.
COMPUTERIZED DATA BASE
Definition: A collection of numeric data and/or
textual information that is available on
computer readable form.
e.g.: Bibliographic
ABI/INFORM
Predicast
Numeric
1. 2000/2010 Census Data
Donnelly MKT
DRI
2. Nielsen Retail Product Movement
SAMI
3. SPI (Strategic Planning Institute) 250 Companies
PIMS
Work Index:
Sponsored by Cornell University’s School of Industrial Labor
Relations and Human Resource Executive magazine, this
site provides links to resources on labor relations,
benefits, training, technology, staffing, recruiting,
leadership, legal issues and related topics.
Marketing
Advertising World Links to resources in selected areas of
marketing and advertising.
American Association of Advertising Agencies Provides
membership information, recent bulletins, and links to
related resources.
American Marketing Association Provides information on
membership, publications, and conferences.
Guerrilla Marketing Online Provides access to recent articles
in marketing and links to relevant sites.
Marketing Cont’d
Institute for the Study of Business Markets (ISBM)
Features current information about seminars and
research projects. Includes marketing links.
John W. Hartman Center for Sales, Advertising &
Marketing History (Duke University Libraries) Center
promotes study of sales, marketing, and advertising
history. Features “Ad*Access,” an image database of
over 7,000 advertisements printed in U.S. and
Canadian newspapers between 1911 and 1955.
Database allows keyword searching.
Yahoo – Business and Economy: Marketing Provides
links to marketing web sites
Marketing Information: A Bibliography
Statistical Sources
Business Resources on the Web: Economic Statistics,
Government Statistics, and Business Law Maintained
by Boise State University’s Albertsons Library,
contains extensive links to statistics sources for the
economy, population, international trade, statistics by
state, etc. Primarily dedicated to statistics sources,
but also contains a business law component
Fisher College of Business Financial Data Finder Links to
financial and economic data on the web and
elsewhere.
Profiling Customers
Dr. Doherty
Tobin College of Business
St. John’s University
Industrial

Dun’s Market Identifiers (DMI)
• D&B’s market information service. A
record of over 7 million establishments
updated monthly

Enhanced DMI extends 4 digit S/C
codes to 6 and 8 digits to allow
clients to target specific customer
groups
Consumer

Geodemographers
• R.L. Pole
Product for Retailers: Vehicle Origin Survey
Samples cars parked in retailer parking lots and
identifies (from the Vehicle Registration Database)
their home location. Can also match location with
Census data and via their TIGER files provide a
demographic profile of customers
• Claritas
Uses 500+ demographic variables in its Prigm
(Potential Ratings for Zip markets) database to
classify 250,000 neighborhoods
40 types based on consumer behavior and lifestyle
(shotguns, pickups, patios and pools, etc.)
Consumer

Diary Panels
• NPD (13,000 HHs)
30 Product Categories
• 29 Miniature Panels
• Quota Sampling
• Applications
 Brand Shares
 Brand Switching Behavior
 Frequency of Purchase and Amounts
 Evaluation of Price and Promotions
 Changes in Channels and Distribution
 Size of Market
Consumer

Store Audits
• Nielsen Retail Index
(Drug stores, Mass media indexes and liquor stores)
• Now Use Scanners
Beginning Inventory and Net purchase (from
wholesalers and manufactures) – Ending Inventory
= Sales
 Audit Includes
•
•
•
•
•
•
•
•
Sales
Purchases by retailers
Inventories
Number of Days of Supplies
Out-of-stock stores
Prices (retail and wholesale)
Special factory packs
Promotions and Advertising
Consumer
• Disaggregate data by



Competitors
Geographic area
Store type
• Nielsen’s Scantrack supplements its
Retail index (since 1970’s)


11 digit WPC code
Evaluates
•
•
•
•

Promotions
Price changes
Channel trends
Product trends
40,000 HHs using scanner wands
Consumer

Behavior Scan (provided by
Information Resources)
• 3,000 HHs provided scanner cards
• Supermarkets and Drugstores provided
with scanner
• With coorperation from Cable TV
Companies It links view habits with
purchase (Black Boxes)
• Distinguishes Users from nonusers of
products WRT …/promotions
Consumer

Television
• Nielsen TV Index



Radio
Audimeters attached to TV sets and tied into a central computer.
Replaced by People Meters in 1988.
Aggregate ratings by 10 socioeconomic groups and demographic
characteristics, including territory, ed. Of head of H.H., age of
woman in house, etc.
• Arbitron


Panel of HHs are randomly selected who have agreed to complete
diaries. Radio marketing are rate 1-4 times age during the
“Sweeps” period (April/May). Focus on age, sex, and individual
(USHH) behavior
Print Media
• Starch Readership Service
• Evals. 50,000 ads in 1000 print media (mag., bus.
Publications, newspapers); u=75,000 person interview
• Recognition method: 3 degrees
1.
2.
3.
Noted. Remembers any part of ad
Associated (1) plus recalls brand or advertise
Read Most recalls 50% or more of the written material
Multimedia Services

Simmons Media/Mkt Service
•
•
•
Prob. Sample of 19,000+
Cross references product usage and media exposure
4 different interviews with each respondent

•
•
•
•
•

Results disaggregated by sex
Self –administered questions covering 500 product categories
TV view behavior gathered by means of a personal diary; Radio via
both personal and telephone interviews
Demographics collected
Application Segmentation and targeting by firms
Mediamark
•
•

Magazine, TV, Newspaper, Radio
Similar service, problem sample of 20,000
Tends to establish audience rate 10% higher than Simmons (see p
252)
Mail Panels
•
NFO Research



•
Quota Sample of 400,000 HHs
Rebuilt every two years
Self-adm q
Market Facts, Inc,


Quota Sample of 275,000
Cross Tabulation of Aug. Criterion Variable (Adv. Sales, etc) with anyone or
number of demographic variables (Age, sex, automobile,…, pets ordered,
PART 3B
Determining Market
Potential
Dr. Doherty
Tobin College of Business
St. John’s University
Determining Market Potential

Multiple-Factor Index Method
(“Annual Survey of Buying Power” published
by Sales and Marketing Management )
Purpose: Measure the relative consumer
buying power in different region, state,
and metropolitan areas.
Determining Market Potential
Bi = 0.5yi + 0.3ri + 0.2pi
where
Bi : % of total national buying power found in area i
yi: % of national DI in area i
ri: % of nat’l retail sales in area i
pi: % of nat’l population in area i
Example 1: drug sales
Suppose N.Y. State has: yi
= 5.0%, ri = 10.0%, pi = 8.0%
Bi = 0.5(5.0) + 0.3 (10.0) + 0.2(8.0) = 7.1
Thus, 7.1% of the nation’s drug sales would be expected to
occur in NY. If the total drug sales are $50 Billion, sales in
the NY market should be
$50B x .071 = $3.55B
Determining Market Potential
Bi = 0.5yi + 0.3ri + 0.2pi
where
Bi : % of total national buying power found in area i
yi: % of national DI in area i
ri: % of nat’l retail sales in area i
pi: % of nat’l population in area i
Example 2: Actual 1992 Values for NY
yi = 8.0%, ri = 6.7%, pi = 7.2%
Bi = 0.5(8.0) + 0.3 (6.7) + 0.2(7.2) = 7.45
Thus, 7.45% of the nation’s drug sales would be expected to
occur in NY. If the total drug sales are $50 Billion, sales in
the NY market should be
$50B x .0745 = $3.725B
U.S. Population, effective buying income,
and retail sails for selected states, 1991
1991 Regional State Summaries of …
Population
1991 Total
Percentage
Population
of U.S.
(thousands)
Region State
Middle Atlantic 37,947.9
New Jersey 7,813.5
New York 18,166.3
Pennsylvania 11,968.1
14.9621
3.0807
7.1626
4.7188
Effective Buying Income
Retail Sales
1991 Total Percentage 1991 Total Percentage
EBI ($000)
of U.S.
Retail Sales of U.S.
632,218,683
155,172,906
298,926,889
178,118,888
16.9542
4.1613
8.0163
4.7766
266,597,624 14.6370
63,209,987 3.4704
122,445,952 6.7227
80,941,685 4.4439
Source: Adapted from “1992 Survey of Buying Power,” Part I. Sales and Marketing Management (August 24, 1992), pp. B-2, B-3, B-4.
PART 4
Measuring Attitude: Five
Approaches
Dr. Doherty
Measuring Attitude: Five
Approaches
1.
Self Reports
• Most Common Procedure
2.
3.
Observation of Behavior
Indirect Techniques
•
•
•
•
4.
5.
Word Association
Sentence Completion
Storytelling
Graphics Interpretation
Performance of Objective Tasks
Physiological Reactions
• Galvanic Skin Response Technique
• Pupilometer
Qualitative Research Techniques
1. Focus Group
Skilled moderator leads a small group (6-12) of
participants in an unstructured discussion of a
particular topic.
A.
Advantages
1)
2)
3)
4)
Flexibility
Controllable
Group Interaction
Openness (encourages participants to
be honest and direct)
5) Opportunity for quick execution
Qualitative Research Techniques
1. Focus Group
Skilled moderator leads a small group (6-12) of
participants in an unstructured discussion of a
particular topic.
B.
Disadvantages
1) Lack of scientific validity
2) Prone to bias (moderator)
3) Offers false sense of security (Results
should be considered inconclusive)
4) Measurement difficulties
5) Subject to “Squeaky Wheel Syndrome”
Qualitative Research Techniques
2. Depth Interviews
Structured or Unstructured, one-on-one
interview.
A.
Advantages
1) Offers greater comfortability for
sensitive topics
2) More detailed and revealing
3) Easier to schedule
4) Can handle more complex topics (e.g.
Interviewing financial experts)
Qualitative Research Techniques
2. Depth Interviews
Structured or Unstructured, one-on-one
interview.
B.
Disadvantages
1) No interaction effects
2) Expensive
3) Inconsistency among interviewers and
levels of energy (Diminishing Returns)
4) Interpretational errors produce
inconsistency and unreliability
5) Lack statistical validity
Qualitative Research Techniques
3. Projective Techniques
Based on the theory that people may
not be aware of their innermost
attitudes and/or may not wish to
express certain attitudes.
Qualitative Research Techniques
3. Projective Techniques
A.
Techniques
1) Word Association Ex. Detergents
Stimulus Words
Washday
Fresh
Pure
Scrub
Filth
Bubbles
Family
Towels
Respondents
B
A
Ironing
Everyday
Clean
and sweet
Soiled
Air
Clean
Don’t
This neighborhood Dirt
Soap and Water
Bath
Children
Squabbles
Wash
Dirty
Qualitative Research Techniques
3. Projective Techniques
A.
Techniques
2) Picture Interpretation

Thematic Apperception Test (TAT)
Respondent is shown abstract visual stimuli
and describes what is going on in the
pictures and what will happen
Qualitative Research Techniques
3. Projective Techniques
A.
Techniques
3) Sentence Completion
Ex. Toothpaste

I brush my teeth because _________.

I use my brand of toothpaste because
_________.

My toothpaste tastes like _________.

When I brush my teeth, I _________.
Qualitative Research Techniques
3. Projective Techniques
A.
Techniques
4) Third-person technique and role
playing
5) Cartoons


Blank bubbles appear above the cartoon
characters
Ex. New car models
Qualitative Research Techniques
3. Projective Techniques
B.
Disadvantages of Projective
Techniques
1) Subjectivity of scoring procedures low
reliability
2) Low validity
3) Absence of substantial evidence of
“Basic Assumption,” namely, that
respondents project their true feelings
on ambiguous stimuli
4) Small samples and unstructured
formats limit generalization
Basic Measurement/Scale
Concepts
Measure:
Assignment of numbers to characteristics of objects
Object:
A material or physical configuration. Can be seen and/or
touched
Characteristics:
Qualities associated with objects that give such objects
identifying traits
Measurement Scale:
A plan that is used to assign numbers to characteristics of
objects
Construct:
The “something” that is being measured
Scales of Measurement
Scale
Basic
Comparisons
Typical Examples
Measures of
Average
Nominal
Identity
Male-female
User-nonuser
Occupations
Uniform numbers
Mode
Ordinal
Order
Preference for brands
Social class
Hardness of minerals
Graded quality of lumber
Median
Interval
Comparison of
intervals
Temperature scale
Grade point average
Attitude toward brands
Awareness of advertising
Mean
Ratio
Comparison of
absolute
magnitudes
Units sold
Number of purchasers
Probability of purchase
Weight
Geometric mean
Harmonic mean
Equal-Appearing Interval Sort of the Statement into Categories
Statement
f
1
p
cp
f
2
p
cp
f
3
p
cp
f
4
p
cp
A
1
B
2
0
0.00
0.00
0
0.00
0.00
0
0.00
0.00
0
0.00
0.00
8
0.04
0.04
0
0.00
0.00
0
0.00
0.00
0
0.00
0.00
C
3
10
0.05
0.09
0
0.00
0.00
0
0.00
0.00
8
0.04
0.04
D
4
30
0.15
0.24
0
0.00
0.00
0
0.00
0.00
16
0.08
0.12
Sorting Categories
E
F
G
H
5
6
7
8
60 60 14 12
0.30 0.30 0.07 0.06
0.54 0.84 0.91 0.97
0
6 16 28
0.00 0.03 0.08 0.14
0.00 0.03 0.11 0.25
10 10 14 32
0.05 0.05 0.07 0.16
0.05 0.10 0.17 0.33
36 58 48 24
0.18 0.29 0.24 0.12
0.30 0.59 0.83 0.95
I
9
60
0.03
1.00
44
0.22
0.47
84
0.42
0.75
10
0.05
1.00
J
10
K
11
0
0.00
1.00
66
0.33
0.80
34
0.17
0.92
0
0.00
1.00
0
0.00
1.00
4
0.20
1.00
16
0.08
1.00
0
0.00
1.00
Scale
Q
Value Value
5.4
1.7
9.6
1.8
8.9
1.5
6.2
2.0
Centile Formula
 c   pb 
Vc  L  
i
P
w


Semantic Differential Scale
1.
2.
Origin: Research designed to
investigate the underlying structure
of words used to describe objects,
events, processes, attitude, etc.
Rational: Three independent
(orthogonal) dimensions can be
used to describe an object using a
bipolar adjective scale.
Semantic Differential Scale
3.
Three Uncorrelated Dimensions
1)
Potency
2)
Evaluation
3)
Activity:
Strong - Weak
Shallow - Deep
Powerful - Powerless
Good – Bad
Sour – Sweet
Informative – Uninformative
Helpful – Unhelpful
Useless – Useful
Dynamic – Static
Orderly – Chaotic
Aggressive – Non aggressive
Dead – Alive
Slow - Fast
Semantic Differential Scale
4.
Marketing Application
• Develop profiles for products, firms,
markets or whatever is being
measured
• Studies often use adjective that are
not anonyms or single words and use
phrases to anchor scales
• 7-Point Scale is common
Semantic Differential Scale
5.
Marketing Application
• Purification Stage (often times
skipped)
• Item Analysis. Product Moment
Formula is used to compare score of
each item with total score. Or,
• T-test of significance between mean
scores of “low” and “high” total scores
groups on an item-by-item basis.
Example of Semantic Differential
Scale
Not Trustworthy ____:____:____:____:____:____:____
Trustworthy
Attractive
____:____:____:____:____:____:____
Unattractive
Not Expert
____:____:____:____:____:____:____
Expert
Knowledgeable ____:____:____:____:____:____:____
Not
Knowledgeable
Likert Scale


Allows an expression of intensity of
feeling
Purification Stage (same as SD scale)
• Representative Sample of Target
Population

Final Selection of Questions
• Same as SD Scale


Generally a 5-Point Scale
Mixes Statements as to Positive or
Negative Expression
Example of Likert Scale
1.
The celebrity endorser is
trustworthy.
____
____
____
____
____
2.
The celebrity endorser is
attractive.
____
____
____
____
____
3.
The celebrity endorser is an
expert on the product.
____
____
____
____
____
____
____
____
____
____
The celebrity endorser is
4. knowledgeable about the
product.
Stapel Scale



Adjectives or descriptive phrases are
tested rather than bipolar adjective
pairs.
Generally, a 10 point scale is used.
Points, on scale are identified by
number.
Results my differ according to the
manner in which statement is
phrased.
Example of the Stapel Scale
-5
-4
-3
-2
-1 +1 +2 +3 +4 +5
1.
The celebrity endorser is
trustworthy.
         
2.
The celebrity endorser is
attractive.
         
3.
The celebrity endorser is an
expert on the product.
         
The celebrity endorser is
4. knowledgeable about the
product.
         
Basic Rating Scales (3)
1.
Itemized Rating Scale:
Most commonly used. Attitudes are measured by
the choice of positions on a continuum.
2.
Graphics Rating Scale:
Attitudes are expressed along a line or graphic
continuum running from one extreme to the
next.
3.
Comparative Rating Scale:
Uses an explicit reference point for comparison.


Rank order
Pairwise comparison
Examples of the Rating Scales:
Itemized Rating Scale
Please evaluate each of the following attributes of compact disc players
according to how important the attribute is to you personally by placing an
“X” in the appropriate box.
Somewhat Fairly
Exremely
Not Important Important Important Important
1.
Quality of sound
reproduction




2.
Physical size of CD
Unit












3. Brand name
4. Durability of unit
Examples of the Rating Scales:
Graphic Rating Scale
Please evaluate each of the following attributes of compact disc players
according to how important the attribute is to you personally by placing an
“X” at the position on the horizontal line that most accurately reflects your
feelings.
Attribute
1.
Quality of sound
reproduction
2. Physical size of CD
Unit
3. Brand name
4. Durability of unit
Not
Important
Extremely
Important
Examples of the Rating Scales:
Comparative Rating Scale
Please divide 100 points between the following attributes of compact disc
players according to the relative importance of each attribute to you.
Quality of sound
reproduction
Physical size of CD Unit
Brand name
Durability of unit
______
______
______
______
100%
Q-Sort Technique




Similar to Thurstone approach.
Respondents place questions into
different piles to form a known
probability distribution, e.g., normal
or log normal
Subjects reflect their attitude toward
an object
Focus is on individuals and not the
object(s)
Used for cluster and segmentation
applications
Consumer Decision Making Models
Attribute Analysis of Valence and Salience Properties
1. Product Examples
Product
Computer
Hotel
Mouthwash
Lipstick
Attributes
Memory, Software, Price, etc.
?
?
?
2. Illustration: PC
Brand
A
B
C
D
Memory
Capacity
10
8
6
4
Graphics
Capacity
8
9
8
3
Software
Diversity
6
8
10
7
Price
4
3
5
8
3. Decision Models
A.
Ideal Brand Model
N
Ajk  Wik Pijk
i 1
B.
Constrained Brand Model
N
D jk  Wik Pijk  Cik
i 1
C.
Conjunctive Model
Minimum attribute levels screen out
competition brands to yield reduced
set. Ex. PC brands equals or exceeds
(7,6,7,2)
Constrained Brand Model
Ex.: (6,10,10,5)
D(a)  .4 | 10 - 6 |  .3 | 8 - 10 |  .2 | 6 - 10 |  .1 | 4 - 5 |  3.1
D(b)  .4 | 8 - 6 |  .3 | 9 - 10 |  .2 | 8 - 10 |  .1 | 3 - 5 |  1.7
D(c)  .4 | 6 - 6 |  .3 | 8 - 10 |  .2 | 10 - 10 |  .1 | 5 - 5 |  0.6
D(d)  .4 | 4 - 6 |  .3 | 3 - 10 |  .2 | 7 - 10 |  .1 | 7 - 5 |  3.8
PART 5
Questionnaire: Anatomy
Dr. Doherty
Tobin College of Business
St. John’s University
Questionnaire: Anatomy
Definition: A formalized schedule
(document) that is designed to
achieve three purposes:
1.
2.
3.
Obtain Relevant Information;
Direct the Questioning Process; and
Set the format for recording and
evaluating data.
Eight Step Process
Step 1: Define Marketing Problem
1)
2)
3)
4)
5)
6)
Write a paragraph
List data to be collected
Anticipate use of data
State objectives
Develop a Plan of Analysis
Client “Sign Off”
Eight Step Process
Step 2: Interviewing Process
1) Personal
–
–
Structured vs. Unstructured
Interviewer Administered vs. Self
Administered
2) Telephone
3) Mail
4) Internet
Eight Step Process
Step 3: Evaluate Question Content
Four Rules:
1) Will the Respondent understand the
question?
2) Will the Respondent have the
information?
3) Will the Respondent provide
information?
4) Will the Analyst understand the
Respondent’s response?
Eight Step Process
Step 4: Q/A Format
1) Open Ended
a.
b.
c.
Free Response
Probing
Projective (e.g. association, construction,
sentence completion)
2) Close Ended
a.
b.
c.
d.
e.
Dichotomous
Multichotomous
Scales
Ranking
Check List
Eight Step Process
Step 5: Determine Wording of
Question
Three Rules:
1) Unambiguous
2) Simple and Familiar Words
3) Specific Words or Options
Ex.) Why did you fly to Chicago on
U.S. Airlines?
Eight Step Process
Step 6: Sequence of Questions
1)
2)
3)
4)
5)
6)
Screening (if necessary)
Gain Confidence and Interest
Groups Like Topics Together
Funneling
Demographics at End
Thank You!
Eight Step Process
Step 7: Physical Characteristics of
Questionnaire (especially by mail)
Step 8: Pretest - Revise - Formalize Finalize
1) Personal
2) Planned Method of Administration
Guidelines for Question Wording







Use simple words and questions
Avoid ambiguous words and
questions
Avoid leading questions
Avoid implicit alternatives
Avoid implicit assumptions
Avoid generalizations and estimates
Avoid double-barreled questions
Characteristics: Form:
Characteristics: Form:
DISGUISED
UNDISGUISED
Communication Methods
STRUCTURED
Standardized questions
Standardized responses
e.g. fixed alternative
questions
Simple Administration
Simple Analysis
Suitable for facts or
clear-cut opinions due to
forced alternatives
Standardized questions
Standardized responses

Simple administration
Simple analysis
Difficult interpretation
Least used method
UNSTRUCTURED
Non standardized
questions
Nonstandardized
responses.
e.g. depth interviews
Flexible
Difficult interpretation
Interviewer influenced
Better for exploratory
research
Standardized stimuli
Non standard responses
e.g. projective
techniques
Difficult analysis
Subjective
interpretation
Suited to exploratory
Comparison of mail, telephone, and
personal interview surveys
PERSONAL
MAIL SURVEYS
INTERVIEW
SURVEYS
Usually the
Moderately
Most
least
expensive,
expensive
Cost per
expensive,
assuming
because of
completed survey assuming
reasonable
interviewer’s
adequate
completion
time and travel
return rate
rate
expenses
Little, since
Some, since
Much, since
selfinterviewer
interviewers
Ability to probe
can probe and can show
and ask complex administered
format must
elaborate on
visuals, probe,
questions
be short and
questions
establish
simple
rapport
None, since
Some, because Significant,
form is
of voice
because of
Opportunity for
completed
inflection of
voice and
interviewer to
without
interviewer
facial
bias results
interviewer
expressions of
interviewer
Complete,
Some, because Little, because
BASIS OF
COMPARISON
TELEPHONE
SURVEYS
Comparison of Three Communications Media on
Ten Factors
FACTOR
Bias freedom (from interviewer)
Control over collection
Depth of questioning
Economy
Follow-up ability
Hard-to-recall data obtainable
Rapport with respondent
Sampling completeness
Speed of obtaining reponses
Versatility to use variety of
methods
MEDIUM
MAIL PERSONAL TELEPHONE
1
3
2
3
2
1
3
1
2
2
3
1
3
2
1
1
2
3
3
1
2
3
1
2
3
2
1
2
1
3
© 1987 by Prentice-Hall, Inc.
A division of Simon & Schuster
Englewood Cliffs, NJ 07632
PART 6
STATISTICAL ANALYSIS
 From A, B, and C
Z
x
or
x
Major Principles
x
t
ˆ x
(1)
 Rewriting (1)
x  Z x
or
x  Zˆ x
Z

where
E  x
 Solving for Sample Size
Z 
n
E2
2
B)
x 
C)
CLT
(3)
n
=100, Z=2, and E=10
n=(22 x 1002)  102 = 400
Let 
2

Examples:
Let 
n
E (x )  
(2)
 Also, from (1)
E
A)
=100, Z=2, and E=5
n=(22 x 1002)  52 = 1600
Determinants of Sample Size (3)



Variance of Population
Error Allowance
Probability of Realizing Error
Allowance
2
Z 2
n  
E
 From A, B, and C: Binomial
A)
B)
E (P)  
P 
P(1  P)
N
 Similar to (3), for Binomial
Z 2 P(1  P)
n
2
E
P(1  P)
PZ
n
(4)
note:
 x  P(1  P)
2
2
 x  P(1  P)  
P x
Example:
Let P
=0.2, Z=2, and E=0.02
22  0.2(0.8)
n
 1600
2
0.02
Suppose that P
(5)
=0.3 from (5)
0.3(0.7)
.3  2
1600
 .3  2(.0115)
 .3  .0229
 From A, B, and C: Binomial
A)
B)
E (P)  
P 
P(1  P)
N
 Similar to (3), for Binomial
Z 2 P(1  P)
n
2
E
P(1  P)
PZ
n
(4)
note:
 x  P(1  P)
2
2
 x  P(1  P)  
P x
Example:
Let P
=0.2, Z=2, and E=0.02
22  0.2(0.8)
n
 1600
2
0.02
Suppose that Pfound
(5)
=0.3 from (5)
0.3(0.7)
.3  2
1600
 .3  2(.0115)
 .3  .0229
Six-Step Procedure for Drawing a Sample






Step
Step
Step
Step
Step
Step
1: Define the Population
2: Identifying the Sampling Frame
3: Select a Sampling Procedure
4: Determine the Sample Size
5: Select the Sample Elements
6: Collect the Data from the
Designated Elements
Sampling Plans
Non Probability
Convenience
Judgment
Snowball
Quota
Probability
Simple Random Sampling
Systematic Random Sampling
Stratified Random Sampling
•Proportionate
•Disproportionate
Cluster Random Sampling
•One Stage
•Two Stage
Area
•One Stage
•Two Stage

Stratified Sampling
1) Proportionate
2
Ni
Where: Wi 
N
 Ni 
Allocation: ni   n
N
W 
i
2
i
Nk 2 
Z  N1 2 N 2 2
n  2  1 
 2  ...   k 
E N
N
N

2
Note:
x 
Z
n 2
E
2
W

 i i
n
Stratified Sampling
2) Disproportionate
Allocation: ni 
Z
n 2
E
N i i
N
i
W  
2
i
i
n
k
i 1
i
Nk 
Z  N1
N2
n  2  1 
 2  ...  k 
E N
N
N

2
Note:
 W  
2
x 
2
i
n
i
2
Stratified Sampling Illustration
N=1250
Industry
E=8.00
90% Confidence Level:
N1
Z=1.64
Ni
N2
750
500/
1250
i
Wi
N i i
20 0.60
15000
15,000/
30 0.40
30,000
1) Proportionate

(1.64) 2
2
2
n
.
6
(
20
)

.
4
(
30
)
82
2.6896
.6(400)  .4(900)

64
 .042025(240 360)

n  25.215
n1  .6(25)  15
n2  .4(25)  10
Ni i
 Ni i
0.50
0.50
Stratified Sampling Illustration
N=1250
Industry
E=8.00
90% Confidence Level:
N1
Z=1.64
N2
i
Ni
750
500/
1250
Wi
20 0.60
N i i
15000
15,000/
30 0.40
30,000
Ni i
 Ni i
0.50
0.50
2) Disproportionate
(1.64) 2
2


n
.
6
(
20
)

.
4
(
30
)
82
2.6896
12  122

64
 .042025(576)
n  24


750(20)
n1  
(24)  12

 750(20)  500(30) 
n2  n  n1  24  12  12
PART 7
STATISTICAL
DISTRIBUTIONS
Sales Performance of REPS under Three
Different Sales Training Programs
I
II
III
86
90
82
79
76
68
81
88
73
70
82
71
84
89
81
425
375
85
75
Total 400
x
x
80
80
SUMMARY (Anova: Single Factor)
Groups
Count Sum
Average
Column 1
5
400
80
Column 2
5
425
85
Column 3
5
375
75
ANOVA
Source of
Variation
Between Groups
Within Groups
SS
250
448
df
2
12
Total
698
14
3.348
Accept H0
Variance
38.5
35
38.5
MS
125
37.3333
3.885
Reject H0
F
P-value
3.348214 0.0699094
F crit
3.88529
SSB /( K  1)
250/(3  1)
F

SSE /( N  K ) 448/(15  3)
125

 3.348214
37.3333
Step I: SST
(x
ij
 x)
2
N
 (86  80) 2  (79  80) 2  ...  (81  80) 2
 698
Step II: SSB
K
2
n
(
x

x
)
 j j
j 1
 5[(80  80)  (85  80)  (75  80) ]
2
 250
2
2
Step III: SSE
 (x
ij
 xj)
2
N
(86  80)  (79  80)  (81 80)  (70  80)  (84  80)  154
2
2
2
2
2
(90  85) 2  (76  85) 2  (88  85) 2  (82  85) 2  (89  85) 2  140
(82  75)  (68  75)  (73  75)  (71 75)  (81 75)  154
154 140 154  448
2
2
2
Note: SST = SSB + SSE
698 = 250 + 448
2
2
Step IV: Fcalc. Value
 SSB   250

 

 K-1    2   125  3.35
 SSE   448 37.3

 

 N-K   12 
T able Value of F2i 12 j .05  3.88
Accept H0. No significant difference
among samples at 5% level
Chi-Square
1.
Definition
2 
2.
r ,le
(Oij  Eij ) 2
i 1, j 1
Eij

Applications
A. Contingency Table (r by le)
H 0 : P11  P12  ...  P1k   1
B. Goodness of Fit Test
H 0 : 01  E 1
P21  P22  ...  P2k   2
02  E2
Pr1  Pr2  ...  Prk   r
0r  Er
Chi-Square
3. Illustration
Problem: Children's Commercials:
Does the level of Understanding (Levels I, II, and
III) vary with a child's age (5-7 vs 8-10 vs. 11-12)
H 0 : P11  P12  P13   1
P21  P22  P23   2
P31  P32  P3k   3
Level of
Sample Test:
Understanding
I
II
III
TOTAL
5-7
55
35
10
100
AGE
8-10 11-12 Total
37
15 107
50
60 145
13
25 48
100
100 300
Chi-Square
4. Solution
H 0 : P11  P12  P13  107 / 300  357
P21  P22  P23  145 / 300  483
P31  P32  P3k  48 / 300  160
1,000
Level of
Understanding
I
II
III
TOTAL
5-7
55
(35.7)
35
(48.3)
10
(16)
100
AGE
8-10 11-12
37
15
(35.7) (35.7)
50
60
(48.3) (48.3)
13
25
(16) (16)
100
100
Total
107
145
48
300
Chi-Square
4. Solution (continued)
2
2
(
55

35
.
7
)
(
37

35
.
7
)
2
 calc 


35.7
35.7
(25  16) 2
... 
 36.9
16
 .205, 4  9.488
Since 
2
calc

2
critical
, reject H0
Dependent Samples: t-Test
H0: Consumers are Indifferent Between
Alternatives, that is, D  0
Test Statistic: t 
d

where: d 
d 
d D
d
n
ˆ d
n
n = number of sample (retail outlets)
ˆ d 
2
(
d

d
)

n 1
Dependent Samples: t-Test
Illustration:
Store
1
2
3
4
5
6
TOTAL
TUMS I TUMS II d
130
82
64
111
50
56
493
111
76
58
103
48
61
457
19
6
6
8
2
-5
36
(d  d ) 2
169
0
0
4
16
121
310
Dependent Samples: t-Test
Illustration (continued)
d 36

d

6
n
ˆ d 
6
 (d  d )
n 1
ˆ d
H0 : D  0
2

310
 7.87
5
7.87
d 

 3.21
n 2.45
 : .05 tn1,0.5  2.015
d D
6
t

 1.87
ˆ d
3.21
Since tcalc  tcritical, cannot reject H 0
END