Sample 

• Population represents the entire group of individuals in whom we are interested. 
• Sample (n) is a “Finite” part of a statistical population whose properties are studied to gain information about the “WHOLE”.
• Sampling can be either purposive or non-purposive. 

Types of Sampling 

• Purposive (non-probability/deliberate) sample is one which is selected by the researcher subjectively.  
• Non-purposive (probability) sample is one in which every unit in the population has a chance (greater than zero) of being selected in the sample, and this probability can be accurately determined. 

Convenience sampling 

• The criterion adopted in this sampling is that of convenience to the investigator.  
• Selection is made from an available source like that from a nearby college students to study the awareness regarding COVID19 in college students. 
• Generally biased and unsatisfactory, but time, cost and resource required will be considerably less.  

Quota sampling 

• Specified numbers (quotas) are obtained from each specified population subgroup (e.g. households or persons classified by relevant characteristics), but with essentially no randomization of unit selection within the subgroups. 

• Advantage: Quicker and cheaper than starting from full population lists 
• Limitation: Difficulty to locate sufficient respondents of particular characteristics 

Snowball samples 

• Also called the “chain method,” is efficient and cost- effective to access people who would otherwise be very difficult to find. 
• Researcher asks the first few samples, who are usually selected via convenience sampling, if they know anyone with similar views or situations to take part in the research. 

Simple random sampling 

• Also known as chance sampling where each and every subject in the population has an equal chance of inclusion in the sample and each one of the possible samples has the same probability of being selected. 
• Methods of simple random sampling include coin toss, random selection of numbers, lottery, and computer generation of numbers. 

Systematic sampling 

• This method of sampling uses a systematic method where the selection process starts by picking some random points in the list and then every nth element is selected until the desired number is secured. E.g. 5, 10, 15, 20 and so on.
• Systematic sampling technique is applicable when the population is large and scattered but the population list available. 

Stratified sampling 

• In this technique, the population is stratified into a number of non-overlapping subpopulations or strata and sample items are selected from each stratum. 
• Stratified sampling technique is applicable when: (i) the population is large, and (ii) the population is not homogeneous.  

Cluster sampling  

• Cluster sampling is applicable when preparing the sampling frame is difficult. 
• In it, geographical area is divided into small area called cluster. 
• This technique allows only small number of target population to be sampled.  
• Error will be more in the result but the cost and time will be reduced remarkably.  
• Usually this technique is used for evaluation of the immunisation programme.  

Multi-stage sampling 

• This is an extension to the idea of cluster sampling. 
• This technique is meant for larger data extending to a considerably large geographical area like an entire country.  
• In multi-stage sampling the first stage may be to select large primary sampling units such as states, districts, towns, and finally certain families within towns. 

Article by Dr. Siri P. B.