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.

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

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

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 / e-mails Social Media data (“found data”) Past studies Store Audits

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.

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)

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

NPD Group

Neilsen Panels

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)!

Google Scholar

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

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

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

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

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)

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

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

A Model of New Technology Adoption

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! 

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

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

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.

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.

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.

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.

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.

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?