Chapter 3
Secondary Data,
Literature Reviews, and Hypotheses
McGraw-Hill/Irwin
Copyright © 2013 by The McGraw-Hill Companies, Inc. All rights reserved.
Nature, Scope, and Role of Secondary Data
• Secondary data: Data not gathered for the
immediate study at hand but for some other
purpose
– Internal secondary data: Data collected by the
individual company for accounting purposes or
marketing activity reports
– External secondary data: Data collected by
outside agencies such as the federal government,
web properties, trade associations, or periodicals
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Nature, Scope, and Role of Secondary Data
• Secondary data research has gained
substantial importance in marketing research
with:
– Increased emphasis on business and competitive
intelligence
– Ever-increasing availability of information from
online / electronic sources
• Used to examine marketing problems because
of the relative speed and cost-effectiveness of
obtaining the data
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Some Sources of Internal Secondary Data
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Sales invoices
Sales activity reports
Online registrations
Warranty cards
Clickstream Data
Online Analytics (i.e.
Google Analytics,
Tweetdeck, Facebook
Insights, etc.)
• Customer letters/
comments / e-mails
• Social Media data
(“found data”)
• Past studies
• Store Audits
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Store Audits
• Examination of how much of a particular
product or brand has sold at retail
– Product sales in relation to competition
– Effectiveness of shelf space/POP displays
– Sales at various price points
– Effectiveness of POS and non-POS offers
– Sales by store type, location, channel, etc.
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External Secondary Data Sources
• Government Studies & Reports (i.e. Census
Data)
• Business sources
– Editors and Publishers Market Guide
– Source Book of Demographics and Buying Power
for Every Zip Code in the USA
– IDS and Gartner Group (technology)
– Neilsen/IRI (consumer products & media)
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Consumer Panels
• Benefits
– Lower cost than other
methods
– Rapid availability and
timeliness
– Can track trends over
time with same (similar)
sample
– High level of specificity
• Risks
– Sampling error (low
minority representation)
– Response bias –
“inbreeding” effects
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NPD Group
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Neilsen Panels
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What is a Literature Review?
• A comprehensive examination of available
information related to your research topic
– Can help clarify and define the research problem
and research questions
– Can suggest research hypotheses to investigate
– Can identify scales and research methodologies to
use in your study
– May provide an answer to your research
question(s)!
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Google Scholar
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Criteria Used to Evaluate Secondary
Data Sources
• Purpose (i.e. why collected?)
• Accuracy / Timeliness
• Consistency over time and across units
studied
• Credibility
• Methodology used
• Bias in findings
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Developing a Conceptual Model
• Literature reviews can help you conceptualize
a useful and empirically accurate model of
reality
• Elements required to conceptualize and test a
model:
– Variables
– Constructs
– Relationships
– Hypotheses
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Variable
• An observable item that is able to be directly measured
EXAMPLES: Gender, Age, Location, Intention to Purchase
Construct
• An unobservable concept that is measured by a group of related
variables EXAMPLES: Brand Loyalty, Intelligence, Satisfaction
Relationships
• Associations between two or more variables or constructs
Independent Variable
• The variable or construct that predicts or explains the outcome
(dependent) variable of interest
Dependent Variable
• The outcome variable or construct researchers are seeking to explain
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Hypotheses and Models
• Hypotheses: Theoretical statements about
relationships between variables or constructs
• Two hypotheses formally stated:
– Hypothesis 1: Higher spending on advertising
leads to higher sales.
– Hypothesis 2: Higher prices lead to lower sales.
• A set of related hypotheses form a Conceptual
Model
• Here we are trying to formulate a model of sales
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Relationship Types
• Positive relationship: An association between
two variables in which they both increase or
decrease together
• Negative relationship: An association
between two variables in which one increases
while the other decreases (and vice-versa)
• Non-Directional relationship: Does not
specify a covariance pattern (i.e. no
directional association specified)
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Characteristics of Good Hypotheses
• Follow from research questions
• Clearly and simply stated
• Must be testable (i.e. falsifiable)
• Must lend themselves to variable identification
and “operationalization” of variables
• Data sources must be available to populate the
operationalized model
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Model Conceptualization
• Development of a model that outlines all relevant
variables/constructs and the hypothesized
relationships between them
• Involves:
• Identifying the independent and dependent variables for
your research
• Specifying relationships between the variables
– Preparing a diagram or other conceptual model that
represents the relationships you will study
– Developing or identifying theories that justify those
relationships
– Specifying “Boundary Conditions” for the relationships, if
any
– Identifying any control variables (pre-hoc) or confounding
variables (post-hoc) that could affect results
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A Model of New Technology Adoption
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Boundary Conditions, Control Variables
and Confounding Variables
• Boundary Conditions: Conditions under which your
hypothesized relationships and research conclusions
will hold / not hold
• Control Variables: Independent Variables (IVs) that
affect the Dependent Variable (DV) but which are
often not the main focus of the study
• Confounding Variables: IVs that affect the DV which
are not part of the conceptual model, not “controlled
for” and not observable – BIG TROUBLE! 
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Hypothesis Testing
• Hypothesis: An empirically testable though
yet unproven statement developed in order to
explain phenomena
– Null hypothesis: No relationship between two
variables/constructs.
– Alternative hypothesis: Suggests that two
variables/constructs are somehow related
– Non-directional vs. Directional
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Hypothesis Testing
• A null or alternate hypothesis refers to a
population parameter, not a sample statistic
– Parameter: The true value of a variable
– Sample statistic: The value of a variable that is
estimated from a sample
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Examples - Null Hypotheses
• There is no significant difference between the
preferences toward specific banking method
exhibited by white-collar customers and blue-collar
customers.
• No significant differences exist in requests for
specific medical treatments from emergency walk-in
clinics between users and nonusers of annual
preventive maintenance health care programs.
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Examples – Alternate Hypotheses, Non-directional
• There is a significant difference in
satisfaction levels reported by Safeway and
Lucky shoppers.
• Significant differences exist between males
and females in the number of hours spent
online.
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Examples – Alternate Hypotheses,
Directional
• We expect higher satisfaction levels to be
reported by Safeway shoppers than Lucky
shoppers.
• We expect to find that males spend
significantly more hours online than
females.
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Examples – Alternate Hypotheses
(positive relationship)
• More studying is related to higher GPAs.
• Friendlier salespeople generate higher sales
revenues.
• More frequent advertising is associated with
higher sales.
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Examples – Alternate Hypotheses
(negative relationship)
• Students with higher GPAs consume less
alcohol than those with lower GPAs.
• The more pressure to close sales perceived by
salespeople, the fewer the number of follow
up, “relationship-building” sales calls made.
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ACTIVITY: Formulating Research Objectives and Hypotheses
• Develop a simple research question (example: What
factors drive the adoption of a new technology?)
• Formulate a simple model for your research
question. Specify the following:
– Independent Variables (several) vs. Dependent Variables
(one)
– The + or – relationship between each IV and the DV.
– Theory behind the relationships
– Any boundary conditions for the model
– Any control variables/factors needed
• Follow up: How to measure your IVs/DVs?
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