This blog contains information about Sampling Techniques. It starts with the definition of Sample and Sampling procedure. Then it explains the terms Sample Unit and Sample Design. It also mentions the Purposes of Sampling. Then it elaborates Probability Sampling along with its Types. It also contains Non-Probability Sampling with its Types. Then it tells the difference between Population and Sample. In the end, it mentions, what are the Considerations during sampling. This blog contains Population vs Sample, Sampling Techniques and Considerations.
What is a Sample?
A sample is a small unit that we collect from a large population. Sample should contain all the characteristics of the population. It should be a true representative of a population. Sample is an integral part of the research. Because it helps us to take a part of any population and analyze the whole population by analyzing its characteristics. This blog contains Sampling Techniques, Sample vs Population and considerations.
Sampling:
It is the process of collecting samples from a population in order to analyze it to know about its characteristics. Sampling is according to the population and research criteria to perform research. Sampling techniques are of many types so the detail about Sampling types is present in this blog with detail. Some important components of sampling includes Sample unit and Sample Design.
Sample Unit:
A sample unit is according to the population under observation. Sample unit can be of large quantity or a small quantity, as it is required or is suitable for the population. It is basically the amount of sample taken from a population for analysis. We can also call it as sample size. The sample size should be just appropriate for the population, if not it can be the cause of errors in the results.
Sample Design:
The term sample design is basically the plan or guiding light for the sampling application. It means that you study the population, you consider the sample unit and then you choose the correct and suitable sample design for proceeding in the sampling procedure. The selection of sample design is also a crucial step for the research. It determines the path which a Sampling procedure follows while taking samples. We will discuss the Types of Samples in detail.
Purpose of Sampling:
Following are the purpose of Sampling:
1.To get information:
Sampling helps us to get information of large and infinite populations with complete details. If the sample is with correct sample unit and appropriate sample design, we can get complete information about a large population with the help of few samples.
2.Estimate Parameter:
Sampling provides us great reliability about parameters so it helps us in estimating parameters of the whole population by analyzing a few samples and testing them. This helps to understand and get information about a population with detail.
3.Test Hypothesis:
Hypothesis are the bunch of thoughts in a researcher’s mind about any population or any research purpose. Sampling facilitates us in testing if hypothesis are right or wrong. It provides us proofs about the validation and rejection of Hypothesis.
These are the main purposes of Sampling. This blog contains Sampling Types, Population vs Sample and Considerations.
Probability Sampling:
It ensures that each member of the population has the same probability for being selected into your research. It introduces random sampling techniques. Probability sampling is a sampling method that includes randomly selecting a small group of people (a sample) from a larger population, and then predicting the probability that all their results put together will match those of the overall population. Its types are as follows:
Simple Random Sampling:
The name of this type itself tells that it is simple as well as random. So, in this type, you simply grab the samples from random locations of the populations and bring it for analysis.
Cluster Sampling:
This type of sampling introduces the sampling in a way that, the population is divided into random clusters of similar characteristics. And then you randomly select a cluster for sampling.
Systematic Sampling:
In this type of sampling, each individual in a population is assigned a number. Instead of randomly generating numbers, participants are chosen at regular intervals.
Stratified Sampling:
This types ensures that the population is divided into many strata according to similarities. In this, sample is collected from each strata and analysis is done.
Non-Probability Sampling:
Non-probability sampling includes sampling method in which the researcher selects samples according to the subjective judgment of the researcher rather than selection of samples randomly. It is a less stringent method. This sampling method depends heavily on the expertise of the researchers. In this type of sampling, the chances of each individual to add into the research are not equal and the sampling is not random. It is mostly applies in Qualitative and Exploratory research. It is much easier to conduct than random sampling.
Convenience Sampling:
This sampling is always according to the convenience of the researcher and it also includes the samples that are easily available for the researcher. It is easy for collecting initial information.
Judgmental and Purposive Sampling:
It is the sampling done by judgement of the researcher. The researcher decides which samples would be suitable for the type of research he has to perform. This relies on judgement.
Voluntary Response Sampling:
This sampling also apply according to the access of the researcher and accessibility of the samples. It is when people volunteer to participate in your research activity and taking samples.
Snowball Sampling:
It involves when the population whose samples are required is difficult to access. You need people to collaborate and get appropriate samples from that population. We call participants as snowballs.
Quota Sampling:
This sampling tells us to divide the population in certain categories and also need a particular number of males and females for the collection of samples. You decide a range within population for every category to identify it.
These were the types of Sampling. This blog contains Population vs Sample, Sampling Techniques and Considerations.
Population VS Sample:
Population:
A population is the number of all the units or individuals present in a population. It comprises with every single unit of population.
A population can be very large and it can also be infinite. Such as a river, ocean, country, etc.
The data of a population contains each and every unit and complete enumeration to elaborate it completely.
A population represents all the existing characteristics of a population as a whole.
The parameters of a population are always fixed, i.e. mean, variance etc.
The conclusions about a population are much harder as compared to the conclusions from a sample because there is a lot of data and the data is complex.
It is hard to study a whole population than studying or analyzing a sample.
Research criteria decides how to study a population.
Studying a population is scientifically impractical and much costly. There are risks of errors in a complete population analysis.
VS
Sample:
A sample is a small unit of any population that we garb for research purposes to make analysis about the population.
A sample is way smaller than the whole population, because it is easier to handle and use for analysis.
The sample is a single unit and most of the time smallest unit of a population.
Data within an appropriate sample or samples, contains true representativeness of the characteristics of a population.
The parameters of a sample are not fixed all the time.
The conclusions are very easy from a sample and its analysis is easily possible.
It is very easy to study a sample as compared to a population.
Sampling techniques decides how can you take samples for a given research target appropriately.
Studying a sample can be costly but it is way more cheaper than studying a population completely.
Considerations during Sampling:
How many samples to collect?
Is the equipment enough for collecting required number of samples?
Is the property owner okay with sampling on his property?
Why the pre-treatment of the sampling equipment is compulsory before sampling?
How will you store the sample after collection?
Are you following all the sampling protocols?
Is it the best time to collect the samples from given population?
Are you considering the fact that when the target problem to be is at worst?
Is the sample design according to the population and research criteria?
Do you select appropriate tools for collection of samples?
By considering all these things, we can perform appropriate sampling procedures for the population under consideration. If even one of these steps gets missing from your checklist, so it can have very bad impacts on your results and it can lead to extreme errors in the analysis and conclusions.
This blog contains Sampling Techniques, Population vs Sample and Considerations in sampling.
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