Marketing Research Study Guide

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