Systematic Random Sampling
Simple Random Sampling
Cluster Random Sampling
Ans:A.)Systematic Random Sampling
- When a large population of individuals have to be studied, it is easier and more economic to study the sample than whole population.
- So we require sampling.
- It is important to ensure that the group of the people included in the sample are representative of the whole population to be studied.
Methods of sampling
1.Random sampling (Probability sampling)
- A random sample is one chosen by a method involving an unpredictable component.
- It is also called probability sample because each item has a known probability of being in the sample.
Types of random sampling are : ‑
i) Simple random sampling
- A simple random sample is a subset of a statistical population in which each member of the subset has an equal probability of being chosen. An example of a simple random sample would be the names of 25 employees being chosen out of a hat from a company of 250 employees. In this case, the population is all 250 employees, and the sample is random because each employee has an equal chance of being chosen.
ii) Systematic random sampling
- It consists of the following steps
- Defining the population (p);
- Deciding on the sample size (s);
- Listing the population and assigning numbers to cases (sampling frame);
- Calculating the ‘sampling interval’ (p/s = K); Total Population size/desired sample size = Sampling interval (K).
- The first unit is selected randomly and then every Kth unit is selected.
iii) Stratified random sampling
- Separation of people in groups followed by random sampling from those groups is stratified random sampling.
- Stratified random sampling is particularly useful where one is interested in analysing the data by a certain characteristic of the population, viz Hindus, Muslims, Christians, age group etc,
iv) Cluster random sampling
- Here the target population (i.e. entire region) is divided into naturally occuring separate subpopulations or clusters (50 villages) and this division is not based on any particular population characteristic (like religion or age group etc).
v) Multiphase random sampling
vi) Multistage random sampling
- First three, i.e. simple random sampling, systemic random sampling and stratified random sampling are used most commonly for community health survey.
2. Non-random sampling (non-probability sampling)
- Non-random sampling technique is used when it cannot be ensured that each item has an equal chance of being selected, or when selection is based on expert knowledge of the population.
Types of non-random sampling are.
i) Quota sampling
- It is a two stage sampling, in first stage population is segmented into quotas and in second stage sample elements are selected by convenience sampling.
ii) Volunteer sampling
- A volunteer sampling (voluntary sampling) is one in which the people self select themselves into the survey.
iii) Convenience sampling
- Participants are selected at the convenience of the researcher, i.e., the sample comprises the subjects who are simply available in a convenient way to the researcher.
iv)Snow ball sampling
- Data is collected from a small group of people with special characteristics, who are then asked to identify other people like them.