Sampling Methods-  Meaning, Types, Differences

The sampling methods or sampling strategies in statistics are the act of researching a population by acquiring information and evaluating that data. It serves as the data's foundation, and the sample space is large.

The sampling methods used depend on the kind of analysis being conducted, however, it might be basic sampling at random or systematic sampling.

There are various sampling procedures available, which may be classified into two classes. All of these sampling strategies may include particularly targeting difficult or difficult to reach populations.

What is Sampling?

In statistics, sampling is the process of picking a subset of persons or things from a larger population in order to make conclusions about the full population. This strategy is typically employed when it is not practicable or viable to research the whole population. Instead of evaluating every single member of the group, researchers pick a representative sample that ideally replicates the characteristics of the full community.

Types of Sampling Methods

In Statistics, there are many sampling methods used to gather meaningful data from the population. There are two types of sampling methods:

• Probability Sampling
• Non-probability Sampling

Probability Sampling Meaning

The probability sampling method involves random selection, where all eligible individuals have an opportunity to be chosen from the entire sample space. This approach assures that the chosen sample reflects the population, however, it is more time-consuming and expensive compared to non-probability sampling.

There are multiple types of probability sampling techniques, including simple random sampling, systematic sampling, stratified sampling, and clustered sampling.

Systematic Sampling:

Systematic sampling involves selecting items at regular intervals after choosing a random starting point. For example, if 300 students' names are sorted alphabetically, selecting every 15th student from a randomly selected starting number can create the sample.

Stratified Sampling:

Stratified sampling divides the population into smaller groups based on specific characteristics, and then samples are randomly chosen from these groups. If there are three bags of balls (A, B, and C) with varying quantities, proportionate samples, like 5 from bag A, 10 from bag B, and 20 from bag C, can be selected.

Clustered Sampling:

Clustered sampling forms groups with similar characteristics, and random sampling is used within these clusters. For instance, if an educational institution has ten branches, selecting a few branches as clusters for data collection purposes can be more feasible than visiting every unit.

Non- Probability Method Meaning

The non-probability sampling method is a strategy where the researcher selects the sample based on their subjective judgment rather than employing random selection. In contrast to probability sampling, not every person of the population has an equal chance to be part of the research.

There are several forms of non-probability sampling methods, including convenience sampling, sequential sampling, quota sampling, purposive (judgemental) sampling, and snowball sampling.

Convenience Sampling:

Convenience sampling involves selecting samples from the population because they are easily accessible to the researcher. It is an easy way to acquire data, but the sample may not reflect the total population. For instance, when examining customer support in a certain location, polling a few consumers who have just made a transaction is easy but may not be typical of all the customers in that area.

Consecutive Sampling:

Consecutive sampling is similar to convenience sampling but with a slight difference. The researcher selects one person or a group of people for sampling and then observes them for a period before moving on to another group if necessary.

Quota Sampling:

Quota sampling involves forming a sample to represent the population based on specific traits or qualities. The researcher handpicks sample subsets that yield a useful collection of data, allowing for some level of generalization to the entire population.

Purposive or Judgmental Sampling:

Purposive sampling relies solely on the researcher's knowledge and judgment. Since the researcher's expertise guides the selection process, this method has the potential to yield highly accurate results with minimal margin for error. It is also known as judgmental or authoritative sampling.

Snowball Sampling:

Snowball sampling, also referred to as chain-referral sampling, is employed when samples possess traits that are challenging to identify directly. In this approach, each identified member of a population is asked to help identify other sampling units from the same target population.

Probability Vs Non- Probability Sampling Methods

Below, we have provided a tabular representation that highlights the key differences between probability and non-probability sampling methods.