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What is considered secondary data?
1. Secondary Data - Data gathered and recorded by someone else prior to and for a purpose other than the current project.
2. Data conversion (a.k.a: data transformation) - The process of changing the original form of the data to a format suitable to achieve the research objective.
3. Cross-checks - The comparison of data from one source with data from another source to determine
the similarity of independent projects.
What are the advantages of secondary data?
1. Readily available
2. Faster and less expensive than acquiring primary data
3. Requires no access to subjects
4. Inexpensive—government data is often free
5. May provide information otherwise not accessible
What are the disadvantages of secondary data?
1. Uncertain accuracy
2. Data not consistent with needs
3. Inappropriate units of measurement
4. Time period inappropriate (outdated)
What are the 3 Typical Objectives for Secondary-Data Research Designs?
• Fact Finding
• Model Building
• Data Mining
What is fact finding?
1. Identification of consumer behavior for a product category
2. Trend Analysis
Market tracking—the observation and analysis of trends in industry volume and brand share over time.
3. Environmental Scanning
Information gathering and fact-finding that is designed to detect indications of environmental changes in their initial stages of development.
What is model building? Read textbook!
What is the formula for Index of retail saturation?
Index of retail saturation = Local Market Potential (Population x possible sales) / Local Market Retailing Space
What is data mining?
1. Data Mining - The use of powerful computers to dig through volumes of data to discover patterns
about an organization’s customers and products; applies to many different forms of analysis.
2. Neural Network - A form of artificial intelligence in which a computer is programmed to mimic the
way that human brains process information.
3. Market-Basket Analysis - A form of data mining that analyses anonymous point-of-sale transaction databases to identify coinciding purchases or relationships between products purchased and other retail shopping information.
4. Customer Discovery - Involves mining data to look for patterns identifying who is likely to be a valuable customer.