13     SAMPLING

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 :  Economies on the resources required for collecting and managing the data from a smaller sub-group  Improve quality of data by focusing on a smaller group

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. 

Sampling Procedures 

Different procedures are used for selecting a sample for the purpose of data collection. Broadly, these are of two major types:- 

• Probability Sampling 
• Purposive Sampling 

Probability Sampling 

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 : 

• Simple Random Sample 
• Systematic Random Sample 
• Stratified Random Sample 
• Quota Sample, and 
• Cluster Sample 

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.  In such cases, the population is first divided into groups or strata each of which is homogeneous with respect to the given characteristic feature. From each strata, then, samples are drawn at random. This is called stratified random sampling. For example, with respect to the level of socio-economic status, the population may first be grouped in such strata as high, middle, low and very low socio-economic levels as per pre-determined criteria, and random sample drawn from each group. The sample size for each sub-group can be fixed to get representative 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.

Quota 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 

Cluster Sample 

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. 

Purposive Sampling 

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 construction site.   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. 

• Extreme Case Sampling: It focuses on cases that are rich in information because they are unusual or special in some way. e.g. the only community in a region that prohibits felling of trees. 
  
• Maximum Variation Sampling: Aims at capturing the central themes that cut across participant variations. e.g. persons of different age, gender, religion and marital status in an area protesting against child marriage.
  
• Homogeneous Sampling: Picks up a small sample with similar characteristics to describe some particular sub-group in depth. e.g. firewood cutters or snake charmers or bonded labourers. 
  
• Typical Case Sampling: Uses one or more typical cases (individuals, families / households) to provide a local profile. The typical cases are carefully selected with the co-operation of the local people/ extension workers. 
  
• Critical Case Sampling: Looks for critical cases that can make a point quite dramatically. e.g. farmers who have set up an unusually high yield record of a crop. 
  
• Snowball or Chain Sampling: Begins by asking people, “who knows a lot about ________”. By asking a number of people, you can identify specific kinds of cases e.g. critical, typical, extreme etc. 
  
• Criterion Sampling: Reviews and studies cases that meet some pre-set criterion of importance e.g. farming households where women take the decisions. 

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.