Thursday, 26 August 2021

Probability sampling and Types of Sampling

 

Probability sampling and Types of Sampling 

Probability sampling: It is a sampling technique, in which the subjects of the population get an equal opportunity to be selected as a representative sample. Probability sampling: It is a sampling technique where a researcher sets a selection of a few criteria and chooses members of a population randomly.

 We may remember Probability Sampling like S3C

Ø Simple random sampling: Each individual has the same probability of being chosen to be a part of a sample. It is a reliable method of obtaining information where every single member of a population is chosen randomly, merely by chance. It is one of the best probability sampling techniques for example, in an organization of 200 employees, if the sports team decides on conducting team building activities, it is highly likely that they would prefer picking chits out of a bowl. In this case, each of the 200 employees has an equal opportunity of being selected.

Ø  Systematic sampling: Researchers use this sampling to choose the sample members of a population at regular intervals. This type of sampling method has a predefined range, and hence this sampling technique is the least time-consuming.
For example, a researcher intends to collect a systematic sample of 200 people in a population of 2000. He/she numbers each element of the population from 1-2000 and will choose every 10th individual to be a part of the sample (Total population/ Sample Size = 2000/200 = 10).

Ø Stratified random sampling: It  is a method in which the researcher divides the population into smaller groups that don’t overlap but represent the entire population. While sampling, these groups can be organized and then draw a sample from each group separately. For example, a researcher looking to analyze the characteristics of people belonging to different annual income divisions will create strata (groups) according to the annual family income. For example  less than 10,000,  10,000 – 20,000; 20,000 - 30,000; 30,000 -40,000, etc. By doing this, the researcher concludes the characteristics of people belonging to different income groups. Marketers can analyze which income groups to target and which ones to eliminate to create a roadmap that would bear fruitful results.

Ø Cluster sampling: It is a method where the researchers divide the entire population into sections or clusters that represent a population. Clusters are identified and included in a sample based on demographic parameters like age, sex, location, etc. For example, if the Indian government wishes to evaluate the number of minorities living in the North east, they can divide it into clusters based on states. This way of conducting a survey will be more effective as the results will be organized into states and provide insightful minorities data.

 

Nonprobability sampling: It is a method of sampling wherein; it is not known that which individual from the population will be selected as a sample.   In this sampling, the researcher chooses members for research at random. This sampling method is not a fixed or predefined selection process. This makes it difficult for all elements of a population to have equal opportunities to be included in a sample.

 

                  We may remember Non- Probability Sampling like PIQS

Ø Purposive/ Judgmental sampling:   It is formed by the discretion of the researcher. Researchers purely consider the purpose of the study, along with the understanding of the target audience. For instance, when researchers want to understand the thought process of people interested in studying for their B.Ed. degree. The selection criteria will be: “Are we interested in doing our B.Ed. degree?” and those who respond with a “No” are excluded from the sample.

Ø  Incidental/ Convenience sampling: This method is dependent on the ease of access to subjects such as surveying customers at a mall or passers-by on a busy street. It is usually termed as Convenience sampling because of the researcher’s ease of carrying it out and getting in touch with the subjects. Researchers have nearly no authority to select the sample elements, and it’s purely done based on proximity and not representativeness. For example, start-ups and NGOs usually conduct convenience sampling at a mall to distribute leaflets of upcoming events or promotion of a cause – they do that by standing at the mall entrance and giving out pamphlets randomly.

Ø Quota sampling: In this sampling technique happens based on a pre-set standard. In this case, as a sample is formed based on specific attributes, the created sample will have the same qualities found in the total population. It is a rapid method of collecting samples.

Ø Snowball sampling: It is a sampling method that researchers apply when the subjects are difficult to trace. For example, it will be extremely challenging to survey shelter less people or illegal immigrants. In such cases, using the snowball theory, researchers can track a few categories to interview and derive results. Researchers also implement this sampling method in situations where the topic is highly sensitive and not openly discussed—for example, surveys to gather information about Covid-19. Not many victims will readily respond to the questions. Still, researchers can contact people they might know or volunteers associated with the cause to get in touch with the victims and collect information.

Important questions

1.    Approaches to sampling commonly used in qualitative research design are given in Set - I and their characteristics in Set - II. Match Set - I and Set - II and select appropriate code.

 

 Set - I                                               Set – II

 (Approaches to sampling            (Characteristics in qualitative research)

 (a) Extreme case sampling          (i) Seeks cases that are typical

 (b) Purposive sampling                 (ii) Seeks cases that are highly similar to  

                                                           each other

(c) Snowball sampling                    (iii) Seeks cases that are unusual

                                                            (iv) Seeks help from participants to

                                                                   identify additional participants

                                                            (v) Seeks cases according to his/her

                                                             judgement about the appropriateness

 Code : (a) (b) (c)

    (1) (i) (iv) (iii)

    (2) (ii) (iv) (i)

   (3) (iii) (v) (iv)

   (4) (iv) (ii) (iii)

2.    When the population is heterogeneous which of the following methods will be efficient for a choice of sampling procedure? (1) Random sampling (2) Systematic sampling (3) Stratified sampling (4) Convenience sampling 

No comments:

Post a Comment