Sampling Methods
For any statistical analysis of large amounts of data, it is not always possible to evaluate every element. In such cases, several approaches are made to simplify data measurement to cope with limited resources. One of the most common methods used for analyzing and measuring data on a large scale is Sampling. There are various types of Sampling and Sampling methods used in statistical analysis.
What is Sampling?
Sampling is a type of method used in a statistical analysis where a selected number of elements are taken from a comparatively more extensive population. The idea and work process behind taking a sample from a more significant population depends on the type of statistical analysis being conducted.
In simple terms, it is a statistical process that concerns the predetermined elements of a specific data set that facilitates further analysis and inferences about that entire group.
For Example:
If any vaccine is made for the betterment of health conditions then it is important to test it first, to check its side effects and advantages. The test cannot be held on every single person hence what is possible is to take individuals from each state to test that vaccine so that effects according to place can be determined.
Why is Sampling Important?
In the case of a large population, gathering data about every single element can be time consuming and expensive. A population is defined as a whole or a mass, which involves all elements and their characteristics for studying a particular data set.
With the help of Sampling, an arbitrary section of a population is taken as a sample for analysis. It helps analysts to make inferences about an entire population quicker than the manual observation strategy.
So, for statistical analysis of a large population, it is a common practice to take a sample. Thus, Sampling makes the study much more efficient and cost-effective, thereby showcasing its importance in statistics.
There are different types of Sampling techniques, each applying a unique strategy to gain knowledge about a broad set of near homogeneous elements.
Different Types of Sampling Methods
Sampling methods can be broadly categorized into two types – random or probability Sampling methods and non-random or non-probability Sampling methods.
Random or probability Sampling methods can be further subdivided into 2 types, i.e. restricted or simple random Sampling and unrestricted random Sampling.
Restricted random Sampling can be further classified as systematic Sampling, stratified Sampling, and cluster Sampling.
Meanwhile, non-random or non-probability Sampling consists of 3 types : judgment Sampling, quota Sampling, and convenience Sampling. You can get a clear understanding of the various methods of Sampling and its types from the illustration below –
Restricted Random Sampling
Systematic Sampling
Stratified Sampling
Cluster Sampling
Non-Random Sampling
Judgment Sampling
Quota Sampling
Convenience Sampling
Random or Probability Sampling
Among the different types of Sampling in statistics, random or probability Sampling method deserves mention. In the case of random or probability Sampling methods, every individual element or observation has an equal chance to be selected as samples.
In this method, there should be no scope of bias or any pattern when drawing a selected group of elements for observation.
As per the law of statistical regularity, a random or probable sample of an adequate size which has been taken from a large population tends to have the same features and characteristics as those of the entire population as a whole.
In a population of 1000 people, each person has a one-in-a-thousand probability of being selected for a sample. Random Probability Sampling restricts population bias and ensures that all individuals of the population have an equal opportunity of being included in the sample.
Random or Probability Sampling can be broken down into 4 types, they are –
Unrestricted or Simple Random Sampling
Such type of Sampling is done with the random number generator technique. It is also termed as unrestricted random Sampling for its lack of predeterminants in picking a sample from a population.
It is considered the most reliable method as individuals are chosen randomly which is why there is a chance for everyone to get selected for the Sampling process. This works in a manner like suppose in an office if there is a team-building activity then the HR can conduct a chit selecting activity through which every employee will get a chance to take part in that activity.
Thus, simple random Sampling is also called unrestricted random Sampling. This method has two types of procedures, samples drawn with replacements and without replacements.
Systematic Sampling
Systematic Sampling falls under the category of restricted random Sampling, which means that it is not purely random. Samples are taken when elements meet certain criteria.
In the case of systematic Sampling, the entire population is arranged in a specific order. Then, every nth element of that population is selected as a sample.
This Sampling method is used by researchers to select samples of members of a selected community at regular periods. It is necessary for this method that the choice of sample and the sizing be done properly so that it can be used again when needed. This method has a predetermined range which is why it is the least time-consuming.
For example, for evaluating the marks in language subjects of all the students of standard 6, every 5th student’s mark sheet is selected as a sample. Here, n = 5.
Stratified Sampling
In this method of statistical analysis, the whole population is segregated into multiple homogenous groups or strata. From each stratum, samples are picked at random.
For example, if measuring the number of winter clothes with hoodies in a garment store, firstly all clothes might be separated as men’s, women’s, and kids’ and then random hoodies picked from each group act as samples for analysis.
Cluster Sampling
For cluster Sampling, the whole population is divided into clusters and then selected as samples. These samples are divided multiple times into smaller fractions until the sample size is reduced to a state that is reasonable for statistical analysis. That is why it is also known as multi-stage Sampling.
Based on demographic criteria such as age, sex, location, and so on, clusters are found and included in a sample. This makes it very easy for a survey developer to extract useful results from the research.
For example, departments of a business can be clusters as well as the number of roads within a city.
Non-Random or Non-Probability Sampling
In case of a non-probability sample, the elements and observations from a broader population are selected based on non-random criteria. So, each element of a population does not possess equal chances of being in a sample.
However, in the case of such a sample, it is not possible to make a valid judgment on the whole population. Researchers use this kind of Sampling method to develop an initial understanding of a small or semi-analysed population.
But, 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.
In qualitative research which is related to exploring, non-probability Sampling methods are widely used. The goal of this form of research is to get a thorough understanding of a tiny or not researched community, rather than to test a sample of a large population that has been researched many times.
Such methods are mainly of 3 types based on the choice of element selection, which are judgment Sampling, quota Sampling, and convenience Sampling.
Sampling Errors
Sampling error is a type of statistical error, which differentiates the analysis of samples with the actual value of the investigated elements and observation of a population. There are different types of Sampling errors, among them the important ones being biased and unbiased errors.
The magnitude of both types of Sampling errors can be reduced by drawing a bigger sample.
How to control Sampling Error?
Statistical theories assist researchers in calculating the intuition of Sampling errors based on sample size and population.
The amount of the Sampling error is mostly determined by the size of the sample taken from the population. Larger sample sizes are related to reduced error rates.
To understand and analyze the amount of error, researchers use a statistic known as the margin of error. A confidence level of 95 per cent is usually considered to be the normal level of confidence.
Ways to Reduce Sampling Errors?
Sampling errors are simple to spot. To reduce sample error, one should:
Increase the Size of the Sample: A larger sample size has a more accurate conclusion because the study is more related to the actual population.
Instead of a random sample, divide the population into groups and test groups based on their size in the population. For example, if a given place makes up 20% of the population, make sure this fact is included in the study.
Know the Basics: Examine your population and learn about its population. Know who uses the product or service and make sure to only target the right people.
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FAQs on Types of Sampling Methods in Research and Applications
1. What are the 4 types of sampling methods?
There are four main types of sampling methods commonly used in research to select participants or items for study. These methods help ensure that samples are representative and results are reliable. The primary sampling methods are:
- Simple random sampling: Every member has an equal chance of being selected.
- Systematic sampling: Selection occurs at regular intervals from a list.
- Stratified sampling: The population is divided into subgroups, and samples are taken from each stratum.
- Cluster sampling: Entire groups or clusters are chosen randomly, then all or some members are selected from each cluster.
2. What are the 4 sampling techniques in qualitative research?
Qualitative research uses specific sampling techniques to gather detailed, in-depth data instead of aiming for statistical representation. The four common techniques include:
- Purposive sampling: Participants are chosen for their unique perspectives or experiences.
- Snowball sampling: Existing participants refer others with relevant experiences.
- Quota sampling: The researcher selects participants to meet pre-set characteristics or proportions.
- Theoretical sampling: Data is collected based on emerging theory, selecting new cases as understanding develops.
3. What are the three types of systematic sampling?
Systematic sampling is a structured way to select samples from a larger population. It involves picking every $k$th element from a list after a random start. The three common types are:
- Linear systematic sampling: Selects every $k$th member throughout the entire population, with no repeats.
- Circular systematic sampling: After reaching the end of the list, selection continues by cycling back to the beginning until the sample size is reached.
- Random systematic sampling: The starting point is randomly chosen, and then every $k$th unit is selected from there.
4. What is the best sampling method to use?
The best sampling method depends on your research goals, population characteristics, and available resources. Simple random sampling is often ideal when every individual should have an equal chance of selection, which minimizes bias. However, for large or naturally grouped populations, cluster or stratified sampling might be more efficient and representative. Ultimately, the chosen sampling technique should match the study objectives and ensure reliability and validity. Balancing practicality, cost, and the need for representativeness will guide the selection of the most appropriate method for your research.
5. What is the difference between probability and non-probability sampling methods?
Probability and non-probability sampling methods differ mainly in how participants are selected. Probability sampling gives every member of the population a known, equal chance of being chosen, leading to results that can be generalized to the whole population. Examples include simple random, stratified, and systematic sampling. Non-probability sampling, such as purposive or convenience sampling, selects participants based on researcher judgment or availability, rather than random chance. This makes non-probability samples less representative and limits the generalizability of findings. Researchers choose between these sampling strategies based on study goals, resources, and the need for statistical reliability.
6. When should you use stratified sampling?
Stratified sampling is ideal when your population has distinct subgroups, or strata, that could influence the research results. In this method, the population is divided according to key characteristics, and samples are drawn from each stratum proportionally. Stratified sampling is used when researchers want to ensure all subgroups are fairly represented in the study, which increases accuracy and reduces bias. For instance, it's common in national surveys where age, income, or region might affect responses. Stratified sampling enhances the reliability of comparisons between groups and the overall validity of findings.
7. How does cluster sampling differ from stratified sampling?
While both cluster and stratified sampling divide populations into groups, their approach and purpose differ. In stratified sampling, subgroups are formed based on shared characteristics, and samples are drawn from every subgroup. In cluster sampling, groups or clusters (often geographic or institutional) are randomly selected, and then all or some members within these clusters are studied. Cluster sampling is useful when populations are large and widely dispersed, as it reduces costs and logistical effort. Choosing between these sampling methods depends on research scope and the nature of the population.
8. What are the advantages of using systematic sampling?
Systematic sampling offers several benefits for selecting a sample from an ordered list or population. Its main advantages include:
- Simplicity: Easy to implement compared to random selection.
- Time efficiency: Faster to conduct, especially with large lists.
- Reduced bias: If the list is randomly ordered, selection bias is minimized.
