Economics: Census and Sampling

Census and Sampling

Census and sampling both provide data and information about a population. In the census, each and every unit of population is studied but only a few units of a populationare studied in sampling. Data obtained from both census and sampling are extremely important to a government for various purposes like planning developmental programs.

Census Method

Census method is the method where the information are collected from every individual related to its subject matter of enquiry. For example, the data about the population are obtained by a national census.

Merits:

• It helps government for the future plan.
• It gives complete information about the population.
• It gives more reliable and accurate information.
• It covers the wide range of the study.

Demerits:

• It is time-consuming and very expensive
• It needs a number of manpower.
• There may be a sometime chance of error information due to less efficiency of an enumerator.
• Sometimes we may lose the information while investing all the individual units during the census.

Sampling Method

The sample is a small segment considered for study which should represent the nature and standard of the entire population. By the study of a sample, the investor should be able to give a justifiable conclusion about the whole population.

Types of Sampling Methods

Generally, there are two methods of selecting samples from the population.

• Random or Probability Sampling
• Non-Random or Non-Probability Sampling

A. Random Sampling

Random sampling is a sampling method where the samples are gathered in a process that gives equal chance to all the individuals in the population of being selected.

Types of Random Sampling

1. Simple Random Sampling

Simple random sampling is the easiest form of random sampling. Researchers should assure that all the members of the population are included in the list and then randomly select the desired number of subjects.

2. Stratified Random Sampling

Stratified random sampling is also known as proportional random sampling. In this sampling technique, the subjects are initially grouped into a different group like age or gender. Then, the researcher randomly selects the final list of subjects from the different strata. But the strata are not to be overlapped.

Researchers usually use stratified random sampling if they want to study a particular subgroup within the population.

3. Systematic Random Sampling

Systematic random sampling is the random sampling method that requires selecting samples based on a system of intervals in a numbered population. For example, QFX can give a survey to every fourth customer that comes into the movie theater.

4. Cluster Random Sampling

Cluster random sampling is done when simple random sampling is almost due to the size of a population.

• In cluster sampling, the research first identifies boundaries.
• The researcher randomly selects a number of identified areas. It is important that all areas of the population be given equal chances of being selected.
• The researcher can either include all the individuals within the selected areas or he can randomly select subjects from the identified areas.

B. Non-Random Sampling

Non-random sampling is a sampling technique where the samples are gathered in a process that does not give all the individuals in the population equal chances of being selected.

Types of Non-Random Sampling

1. Convenience Sampling

Convenience sampling is probably the most common of all sampling techniques. With convenience sampling, the samples are selected because they are accessible to the researcher. Subjects are chosen simply because they are easy to recruit. This technique is considered easiest, cheapest and least time consuming. For example, telephone directory, industrial directory, a record of childbirth in a hospital etc., are the convenient use.

2. Consecutive Sampling

Consecutive sampling is very similar to convenience sampling except that it seeks to include All accessible subjects as part of the sample. This non-probability sampling technique can be considered as the best of all non-probability samples because it includes all subjects that are available that make the sample a better representation of the entire population.

3. Quota Sampling

Quota sampling is a non-probability sampling technique wherein the researcher ensures equal or proportionate representation of subjects depending on which trait is considered as a basis of the quota. This method consists of a large population that is not homogeneous. This was the challenge faced by the market and opinion researcher when they first started to conduct large-scale surveys. Their solution was the quota sample, which attempts to match the characteristics of the sample with those of the universe achieving a small replica of the universe.

4. Judgmental Sampling

Judgmental sampling is more commonly known as purposive sampling. In this type of sampling, subjects are chosen to be part of the sample with a specific purpose in mind. With judgmental sampling, the researcher believes that some subjects are more fit for the research compared to other individuals. This is the reason why they are purposively chosen as subjects.