Sampling
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
- .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 :
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.
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.
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,
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).
- Multiphase random sampling
- 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.
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.
Volunteer sampling
- A volunteer sampling (voluntary sampling) is one in which the people self select themselves into the survey.
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.
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.
Exam Question
- Sampling error is classified as Alpha and Beta Error.
Simple random sampling:
- It is ideal for Homogeneous Population.
- In simple random sampling, the samples are taken completely at random from a given population.
- Every element in the population has an equal probability of being included.
- Haphazard collection of certain number for a sample
Stratified Sampling.
- The method in which the sample is taken from each predefined strata of society is called Stratified Sampling.
Systemic sampling:
- samples are taken according to a predetermined periodicity.
- the units are picked up at regular intervals from the universe in Systematic random sampling.
- People are arranged alphabetically by their names and then every 3rd person is chosen for study. The type of sampling is Systematic Random.
Random Sampling:
- In random sampling chance of being picked up is Same and known.
- If sample size is bigger in random sampling ,then it decreases standard error.
Types of random sampling are :
- Simple random sampling
- Systematic random sampling
- Stratified random sampling
- Cluster random sampling
- Multiphase random sampling
- Multistage random sampling.
Types of non-random sampling are.
- Quota sampling
- Volunteer sampling
- Convenience sampling
- Snowball sampling
In multistaged sampling,
- The researcher randomly selects elements from each cluster instead of using all the elements contained in the selected clusters.
- For Randomized Control Trial (RCT) to assess dating in adolescent, a study was done by selecting random schools, then random classes, then random sections and then random students.
Stratified Random Sampling.:
- For a survey a village is divided into 5 lanes then each lane is sampled randomly is an example of Stratified Random Sampling.
- In stratified random sampling, the population is divided into strata.
- Type of sampling, if random sample is taken from a characteristic population, eg. Hindus, Muslims, Christians etc
Cluster sampling:
- it is a two-stage sampling.
- it is cheaper than other methods of sampling
- it has the disadvantage of higher sampling error.
- Children surveyed in cluster sampling for coverage of national immunization programme is 30 cluster of 7 children.
- A region is divided into 50 villages for the purpose of a survey. 10 villages are then selected randomly for the purpose of a study. This type of sampling is termed as Cluster Sampling.
- Cluster sampling is cost effective.
- Is a Rapid and simple method.
- It is a type of probability sample.
- The sample size may vary according to study design.
- Estimation of percentage of children immunized in community as per WHO is to be done by Cluster Sampling.
- In the WHO recommended EPI Cluster sampling for assessing primary immunization coverage, the age group of children to be surveyed is 12-23 months.
- Design Effect’ is associated
- Snowball sampling is used for hidden population
Don’t Forget to Solve all the previous Year Question asked on Sampling
