1. Secondary Data, Literature Reviews, and Hypotheses
Chapter 3
Secondary Data, Literature Reviews, and Hypotheses
McGraw-Hill/Irwin
Copyright © 2013 by The McGraw-Hill Companies, Inc. All rights reserved.

2. 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

3. 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

4. Some Sources of Internal Secondary Data
Sales invoices
Sales activity reports
Online registrations
Warranty cards
Clickstream Data
Online Analytics (i.e. Google Analytics, Tweetdeck, Facebook Insights, etc.)
Customer letters/ comments / s
Social Media data (“found data”)
Past studies
Store Audits

5. 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.

6. 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)

7. Consumer Panels Benefits Risks 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

8. NPD Group

9. Neilsen Panels

10. 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)!

11. Google Scholar

12. 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

13. 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

14. Variable Construct Relationships Independent 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

15. 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

16. 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)

17. 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

18. 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

19. A Model of New Technology Adoption

20. 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! 

21. 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

22. 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

23. 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.

24. 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.

25. 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.

26. 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.

27. 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.

28. 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?