Business Studies
Marketing Research
Primary Data
Collected for specific research activity. Usually used if there is insufficient
secondary data available.
ADVANTAGES
Collected for specific research
Data is current
Methodology known & controlled
Findings not known to competitors
No conflicting data
DISADVANTAGES
Time consuming
Expensive
Information unavailable
Perspective may be limited
Secondary Data
This is data that has been gathered for some other purpose. It can be internal
or external to the company . It has its limitations in as much as it may not
completely satisfy the research objective.
ADVANTAGES
Cheap
Readily available
Gives other perspectives
Offers potentially higher quality data
than would otherwise be used
Independent source adds to credibility
DISADVANTAGES
May not be current or complete
Too generalised
Unknown research methodology
Conflicting results
Not all results made public giving
misleading views
Sampling
Random sampling
Based on the statistical probability that each member of the population as an
equal chance of being selected in the sample. It is possible to calculate the level
of confidence of the sample, ie., how accurate it will be.
Deductive reasoning
This means that the logic that exists in a general situation is applied to a
specific situation.
Inductive reasoning
This means that we know everything about any given population and use this
information to study the characteristics of a given sample and use this to
compare with the known population.
Elementary units
The population possessing the characteristics we want to investigate.
Business Studies
Sampling frame
The source of elementary units
Sample
Collection of results
Quota sampling
This is non random as the researcher has a set number of people to interview.
All respondents are selected from a given criteria.
Multi stage sampling
There are stages to the sampling, in as much as a sample is taken from the
original trawl. This is known as a cluster sample.
Quantitative and Qualitative Methods
Characteristics of populations may be distinguished in two ways, either
numerical or by attributes.
Nominal data
This is a form of coding and does not affect the research itself. It is a form of
classification.
Ordinal data
Used to rank the importance of data. Examples of ordinal scales are:
Likert scale- a list of statements with five possible choices for example
Strongly agree
agree
disagree
strongly agree
Semantic scale - measure differences between words they are usual bipolar,
for example,
Expensive
Poor value
Inexpensive
Good value
Interval data
This data scale provides rankings with specific calculations. For example the
degrees Celsius on a thermometer.
Ratio data
This uses basic arithmetic to develop relationships. For example gross
profit to sales.