Marketing Research Study Guide

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secondary data
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information that has already been gathered; might be helpful in saving the problem at hand; the researcher must decide its relevance
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primary data
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information you go out and collect (through surveys, experiments, or observation) in order to solve the problem at hand
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advantages of secondary data
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-it can help to specifically define the research problem -sometimes it will actually solve the problem at hand -it may provide insight on how to structure the primary research
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disadvantages of secondary data
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-lack of relevance -lack of availability -inaccurate data
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internal secondary data
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information collected by a company for accounting or marketing purposes
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external secondary data
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collected by outside agencies like the government, trade associations, marketing research companies, and academic researchers
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examples external secondary data
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popular press sources, scholarly sources, government sources, NAICS, guidebooks, commercial sources
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syndicates sources
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commercial vendors who collect information and sell the reports
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store audits
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formal examination of how much of a particular product or brand has sold at the retail level
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NAICS
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standard used by Federal statistical agencies in classifying business establishments for the purpose of collecting, analyzing, and publishing statistical data related to the U.S. business economy
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examples of internal secondary data
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internal database management systems, customer knowledge information
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databased marketing
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creation of large computerized file of customers’ and potential customers’ profiles and purchase patters; done through the use of a database management system
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data mining
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the use of statistical software to uncover patterns in your database
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literature reviews
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comprehensive examination of available information that is related to your research topic
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reasons to conduct a literature review
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-clarify the research problem and questions -uncover existing studies -suggest research hypothesis -identify scales to measure variable and methods
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possibly info to include in LRs
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-demographic dimensions -employment characteristics -economic data -competitive characteristics -regulations -international market characteristics
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conceptualization
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refers to the development of a model the shows variable and hypothesize or proposed relationships between variables
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variable
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an observable item that is used as a measure on a questionnaire
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construct
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an unobservable concept that is measured by a group of related variables
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relationships
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associations between tow or more variables
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independent variables
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variables or constructs that predict or explain the outcome of interest
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dependent variables
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variables or constructs that researchers seek to explain
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hypothesis
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an empirically testable though yet unproven statement developed in order to explain phenomena
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types of hypotheses
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-inverse (negative) directional -direct (positive) directional
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parameter
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the true value of a variable
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sample statistic
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the value of a variable based on estimates from a sample

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