What is Sampling?
It is not possible, nor it is necessary, to collect information from the total population. Instead, a smaller subgroup of the target population or a sample is selected for the purpose of study. Sampling is the strategy of selecting a smaller section of the population that will accurately represent the patterns of the target population at large.
Why Take a Sample?
The main purposes of sampling are :
What is Sampling Frame?
First, define your sampling frame i.e. what group of persons or households or farms are relevant for you and will be eligible to be drawn for the sample? Is it only the farmers who have land holdings or the larger population of all farmers including landless farmers? Is it only the persons who watch TV regularly or all those including those who watch occasionally? The results would be different in each case.
Different procedures are used for selecting a sample for the purpose of data collection. Broadly, these are of two major types:-
In this, sample is taken in such a manner that each and every unit of the population has an equal and positive chance of being selected. In this way, it is ensured that the sample would truly represent the overall population.
Probability sampling can be achieved by random selection of the sample among all the units of the population.
If you intend to establish baseline data or want to assess the changes, effects or the impact that has taken place after the project has been in operation for some time, you go in for probability sample design. This design is generally used in quantitative studies. Random selection of the sample units enables you to confidently generalize results from the small sample to the larger population.
Major random sampling procedures are :
A brief discussion about each is given below.
Simple Random Sample
For this, each member of the population is numbered. Then, a given size of the sample is drawn with the help of a random number chart. Random number charts of the type given below are easily available in the market.
The other way is to do a lottery. Write all the numbers on small, uniform pieces of paper, fold the papers, put them in a container and take out the required lot in a random manner from the container as is done in the kitty parties.
It is relatively simple to implement but the final sample may miss out small sub groups.
Systematic Random Sample
It also requires numbering the entire population. Then every nth number (say every 5th or 10th number, as the case may be) is selected to constitute the sample. It is easier and more likely to represent different subgroups.
Stratified Random Sample
It is sometimes advisable to first introduce an element of structure
into the population before taking the random selection. This is
applicable when the population has a large variability in some characteristics such
as level of education or socio-economic status, which could have a significant
bearing on the results of the study. For example, only very few persons in the
sample frame may be educated and a large majority may be semi-illiterate and
illiterate. It is then possible to omit this small minority from the sample.
This way, it is possible that different categories in the population are fairly represented in the sample, which
could have been left out otherwise in simple random sample.
In this case also, the entire population is first divided into homogeneous strata with respect to the given characteristic. Then, you meet just a specified number of people from each strata as you come across them rather than selecting them through random procedure. The resulting samples are called quota samples
In some cases, the selection of units may pass through various stages, before you finally reach your sample of study. For this, a State, for example, may be divided into districts, districts into blocks, blocks into villages, and villages into identifiable groups of people, and then taking the random or quota sample from each group. For example, taking a random selection of 3 out of 15 districts of a State, 6 blocks from each selected district, 10 villages from each selected block and 20 households from each selected village, totaling 3600 respondents. This design is used for large-scale surveys spread over large areas. The advantage is that it needs detailed sampling frame for selected clusters only rather than for the entire target area. There are savings in travel costs and time as well. However, there is a risk of missing on important sub-groups and not having complete representation of the target population.
Probability sampling is generally used in quantitative studies. Random selection of the sample enables you to confidently generalize results from a small sample to a larger population
However, sometimes it is desirable as in the audience research
to purposively choose the region and the respondents for a
specific purpose. For example, to study the life style of the
young children of the construction workers near the
The power of purposive sampling lies in selecting information rich-cases for in-depth analysis related to the central issues being studied. Purposive sampling can be used with both quantitative and qualitative studies.
Purposive sampling can be done in addition to probability sampling also. For example, after completing
your baseline study based on a random sample, you may recognize that certain sections of the project area
are quite different from other areas due to variations in landscape, geography, culture etc. You may then
purposively select those areas to get representative information about how the variations have influenced
the behavior of the people.
Purposive sampling is particularly relevant when you are concerned with exploring the universe and understanding the audience. This means, using your common sense and the best judgment in choosing the right habitations, and meeting the right number of right people for the purpose of your study.
Some purposive sampling strategies that can be used in qualitative studies are given below. Each strategy serves a particular data gathering and analysis purpose.
In short, purposive sampling is best used with
small numbers of individuals/groups which
may well be sufficient for understanding human
perceptions, problems, needs, behaviors and
contexts, which are the main justification for a
qualitative audience research.
Sampling having been done, and data collected though appropriate research methods, the next step is writing the report. The next chapter deals with this.