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Gathering Data
Gathering Data
Primary Data: This is data which you collect yourself. It is directly relevant to your
enquiries.
Secondary Data: This is data which was collected by someone else. Some of it may not be
relevant to your enquiry.
There are two methods of gathering information about a population (the group which is of
interest),
1. By taking a census, i.e. measuring every individual in the population. This is slow
and expensive and unless the population is small, is usually only carried out by the
state.
2. By taking a sample. A small representative group is taken out of the population and
measured. The results for the population are then drawn from the sample results.
However to be accurate the sample must be free from bias, that is, no group in the
population must have a greater probability of being chosen in the sample.
How is a sample chosen?
A sample can be picked using probability sampling methods, such as simple random
sampling, where the probability of any one individual being chosen is known, and nonprobability sampling methods, such as convenience sampling, where this probability is not
known.
Simple Random Sample
For this method you must have a list of all the members of the population, a sampling frame.
Each member is given a unique number and a selection of these numbers is taken using
random methods e.g. Random number tables, computer programs etc. While it is un-biased
and the best way of picking a sample it has a number of practical problems:
a) It may be difficult to get a sampling frame
b) It may be difficult to interview the chosen individuals
c) It is possible that in populations with sub-groups (strata) these may be underrepresented or even left out e.g. male, female, urban, rural, age groups, etc.
Stratified Sampling
To overcome point c) above, this method divides up the sample into the same sub-groups
(strata) as the population, e.g. if 50% of the population are female then 50% of the sample is
female. However it is necessary to know the percentages of these strata in the population.
This can often found from the National Census.
Systematic Sampling
This is an alternative to simple random sampling. The first item is chosen at random and then
every nth item is chosen. The value of n is got by dividing the size of the population by the
sample size. For example to select a sample of 20 from a population of 800, every 40th (800
÷ 20) item after a random start in the first 40 items should be selected: e.g. if the first item
was say the 23rd then the items chosen would be 23rd ,63rd ,103rd …etc
Care must be taken that no regular pattern in the population coincides with that of the sample.
Clustered Sampling
If the population is clustered into groups, e.g. factories, streets, schools, etc. a sample of the
clusters can be taken and the individuals in the chosen clusters are measured. For example, if
Walter Fleming
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Gathering Data
the inhabitants of a city are to be interviewed, a random sample of streets (using stratified
sampling) might be chosen and the inhabitants of these streets interviewed.
Multi-stage Sampling
This is used when there is a large geographical area to be surveyed, e.g. the USA. The area is
divided up smaller areas and a sample of these is taken. These smaller areas are then subdivided again and another sample is taken. This process can be repeated until there is a
sample of small areas from which a sample of individuals is taken.
Quota Sampling
This is used when bias is not of major importance. Surveyors are given a quota, based on
stratified sampling, to measure and it is left to them to find the individuals. It is very open to
bias but is a quick and easy method in certain situations.
Questionnaires
Information is usually collected by means of questionnaires, which are filled in by the
individuals being interviewed or by the surveyors who ask the questions, face to face or over
the phone. These questionnaires must be carefully designed. The following are some of the
aspects that must be taken into consideration:
1) Keep it as short as possible
2) Make sure that the questions get the information required
3) Questions should be short and unambiguous avoiding biased or emotive words
4) Avoid double-barrelled questions (where two questions are combined in one)
5) Questions should not be phrased so as to suggest a particular answer (leading
questions)
6) Where possible questions should be a yes/no type
7) No calculations should be required
8) Questions should follow a logical sequence
9) Personal questions should be kept to the end
10) If necessary, explain the purpose of the questionnaire and assure the respondent of the
confidentiality of the answers.
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