Sampling

• Sample: 

In research terms a sample is a group of people, objects, or items that are taken from a larger population for measurement. The sample should be representative of the population to ensure that we can generalize the findings from the research sample to the population as a whole.

• Sampling Frame: 

Sampling Frame is a list of elements belonging to the population from which the sample will be drawn.

• What is Sampling?

Sampling means selecting a given number of subjects from a defined population as representative of that population. The main objective of sampling is to get a representative sample of the population by minimizing time, cost and human resources, and help to estimate and test the validity of the estimated population parameter.

• Sampling Error:

Sampling error is the difference between the survey result and population value due to the random selection of individuals or households to include in the sample. Sampling error is the error in a statistical analysis arising from the unrepresentativeness of the sample taken. Sampling error can make a sample unrepresentative of its population.

• Sampling Bias: 

Sampling bias is a bias in which a sample is collected in such a way that some members of the intended population are less likely to be included than others.

• Types of Sampling Methods

1. Probability Sampling: Probability sampling is also known as ‘random sampling’ or ‘chance sampling’. Under this sampling design, every item/subject of the sample frame has an equal chance of inclusion in the sample.

                Types of Probability Sampling

                a. Simple Random Sampling

                b. Systematic Sampling

                c. Stratified Sampling

                d. Cluster Sampling

                e. Multi-stage Sampling

2. Non-probability Sampling: Non-probability 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-probability Sampling

             a. Purposive sampling

             b. Quota sampling

             c. Snowball sampling

             d. Convenience sampling

• Differences between Probability and Non-probability Sampling

• Simple Random Sampling (SRS)

In this method, the samples are drawn in such a way that each unit of the population has equal and independent chance of being selected in the sample. The samples can be drawn with or without replacement. In this method, sampling can be done by two ways: Lottery methods and random number table.

Advantage of SRS: It eliminates personal bias, the results are more accurate as sample size increases, and the method is very simple.

Disadvantage of SRS: It requires complete sampling frame, the method is not suitable to isolate members from a group.

• Systematic Sampling

It is a random sampling and the sampling process is carried out on systematic manner/ rule, i.e. the samples are selected at regular interval (of time, items or observations) from the sampling frame.

Sample Interval (K) = Total Population

                                 Sample Size

Advantages: The sampling method is simple and easy, it gives more precise result than SRS for homogenous population, time cost and labor is relatively small.

Disadvantages: It needs complete sampling frame, the system may interact with same hidden pattern in the population.

• Stratified Sampling

When the populations are heterogeneously distributed throughout the region, then divide the target population into number of subgroups (called strata). The stratification is done in such a way that population within each strata are homogenous and various strata are non-overlapping i.e. each and every unit of the population belongs to one and only one stratum. The samples are drawn from each stratum using simple random sampling.

The stratification is done in such a way that population within each strata are homogenous and various strata are non-overlapping i.e. each and every unit of the population belongs to one and only one stratum. The samples are drawn from each stratum using simple random sampling.

Advantages: Same sampling fraction can be used for all strata to ensure the proportional representation in the sample characteristic being stratified. Each unit in the strata has equal chance of being selected in the sample.

Disadvantages: The sample frame has to be prepared separately for each stratum. It can be complex and time-consuming.

• Cluster Sampling

When the population is densely distributed throughout the region, the population is classified into different subgroups known as cluster in such a way that within the cluster the population are heterogeneous and between the clusters are homogenous. The simple random sampling technique is used to select the relevant clusters.

Advantage: Cluster sampling method cut down the cost and time of preparing sampling frame and greater speed. The sampling frame is required only for the selected clusters and individuals in selected clusters.

Disadvantages: It is less accurate and errors of estimates are high.

• Multi-stage Sampling

It is a complex form of cluster sampling and involves several stages in which the sampling process is carried out. In the first stage, large group or clusters are selected. These clusters are designed to contain more population units than are required for the final sample. In the second stage, population units are chosen from selected clusters to derive a final sample. If more than two stages are used, the process of choosing population units within clusters continues until the final sample is achieved.

Advantage: It is flexible and efficient.

Disadvantages: Sampling error is increased compared with simple random sampling of the same size.

• Purposive sampling

In this method, the choice of sample units is selected deliberately or purposively depending upon the object of investigation. It entirely depends upon the personal convenience, beliefs and prejudices of the investigator. The advantage of purposive sampling is that it is very cheap and if selection is done carefully, gives relevant results. The major drawback of this sampling method is that it is highly subjective in nature since the selection of the sample entirely depends upon the personal convenience, beliefs and prejudices of the investigator.

• Convenience sampling

In this method, the sample items from the population are selected which are convenient way to the researcher. It is no randomness sampling and likelihood of bias is high. It is fast, easy and less expensive to collect the information, but the result obtained by this method, hardly be representative of the population. This method is useful for making a pilot study and pretesting of questionnaire. To study the mass behavior this sampling can be used.

• Quota sampling

Quota sampling resembles like special form of stratified sampling. The specified sub-groups/reserved items called quota from the population are collected according to the desire of enumerator or researcher. In this method, the interviewer is told in advance the number of sampling units he/she is to enumerate from specified sub-groups (quota) of the population assigned. The selection of the sample is non-random and purposive thus induces bias.

• Snowball sampling

This method can be used to access too hard to reach or hidden populations like drug addicts, homeless people, individuals with HIV/AIDS, prostitutes and so on. Snowball sampling has chain type links to the sampling units. There are two steps to create snowball sampling. First, try and identify one or more units from the population. Secondly, use these units to find further units and so on until the required number of sample size is fulfilled.