Techniques of Collecting Data: || Stages in Sampling Techniques:
Introduction
In order to answer the study questions, it is doubtful that the researcher will be able to collect data from all circumstances. As a result, you'll need to pick a sample. Regardless of the method utilized, it is vital that the people chosen are representative of the overall population if a sample is to be used. This may imply concentrating on difficult-to-reach demographics.
Sampling can be used to make generalizations about a population or to make inferences about a theory. In essence, it is determined by the sample technique used.
In general, there are two types of sampling techniques:
What are your thoughts on this topic?
Probability Sampling
Probability sampling is a sampling approach in which a researcher chooses members of a population at random based on a set of criteria. With this selection criteria, all members have an equal chance of being included in the sample. A researcher could use a random number generating computer program to select a sample from the sampling frame after first constructing a sampling frame (Zikmund, 2002).
Here are the types of probability sampling:
You can use random number generators or other techniques that are totally based on chance to accomplish this type of sampling.
In this blog, I advocate using Python to produce appropriate sample/elements from the entire population. For example, If 10 samples are chosen from a population of 200 in the sampling frame, then type the following command:
population = 200
print (random.sample(sampledata,10)
[73, 94, 64, 79, 92, 26, 130, 97, 148, 152] - arbitrary outputs.
Example:
There are 600 female employees and 300 male employees at the company. Assume you need to hire 150 people from a population of which 90 are women and 60 are men. You select the population into two strata based on gender to ensure that the sample reflects the company's gender balance. Then you select 90 women and 60 men at random from each group, giving you a representative sample of 150 employees.
For a sample of size n, we divide our population of size N into k subgroups. The first of our k elements is picked at random from the first subgroup.
Example:
In a population of 4000 people, a researcher plans to take a systematic sample of 400 persons. He or she will number each individual in the population from 1-4000 and select every 10th person to be included in the sample (Total population/Sample Size = 4000/400 = 10).
Example:
Consider a scenario in which a company wants to assess the performance of cellphones across the Philippines. They can divide the population of the entire country into cities (clusters), select the most populous towns, and filter those who use mobile devices.
Example:
A country can be divided into regions, provinces, urban, and rural areas, with all regions with similar characteristics forming a stratum.
Non-probability Sampling
The non-probability approach is a sampling technique that involves gathering feedback based on a researcher's or statistician's sample selection abilities rather than a pre-determined selection process. In most cases, the results of a survey done with a non-probable sample are skewed and may not accurately reflect the intended population. However, there are times when non-probability sampling is far more valuable than the other type, such as during the basic stages of study or while performing research on a budget.
The following are a list of non-probability sampling:
Example:
You're conducting research into student support services at your university, so you invite your classmates to fill a survey on the subject after each of your classes. This is a convenient way to collect data, but the sample is not representative of all students at your university because you only questioned students who were taking the same classes as you at the same level.
It's frequently employed in qualitative research, especially when the researcher wishes to learn more about a certain phenomenon rather than making statistical inferences, or when the population is small and specific. In order to be effective, a purposive sample must have explicit inclusion criteria and justification.
Example:
You want to learn more about handicapped students' perspectives and experiences at your university, so you purposely choose a group of students with varying assistance requirements in order to collect a diverse set of data on their interactions with student services.
Example:
If we want to investigate a population with 40% females and 60% males, we should divide the population into two groups. We'll require a sample size of 100, and the selection won't stop until the target is reached.
Example:
Let's pretend there's a poll about covid patients. If we continue to question people about their covid optimism, we can expect that the majority of them will remain silent. The majority of them will be unable to discuss it freely. We contact their family, volunteers, doctors, or anybody else who can assist us gather information to find out the actual figures.
End Note:
We studied about several sampling strategies and how they are utilized in this blog. Finally, we must remember that sample strategies should be applied according to the case at hand; in this case, we must employ the appropriate sampling approaches.
Thank's for Reading
"I'd love to hear your ideas on the types of sampling techniques in the comment section below."
References:
Is this article useful to you?
1 Comments
Thank you for reading! Any thoughts about sampling techniques. I'd love to hear your comment.
ReplyDelete