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Sampling Methods

Introduction to Sampling Methods

Sampling is the operation of picking a subset (or sample) from a larger population. Sampling is done in order to be able to make conclusions about a population using the features of the sample. In most situations, it will not be practical or even impossible to obtain data from the entire population, so sampling offers a convenient means of obtaining data and drawing statistical conclusions.

Methods of sampling can be generally divided into two major categories: probability sampling and non-probability sampling. The selection between the two sampling methods is based on the purpose of the study, the nature of data to be collected, and available resources.

 

Table of Content

 

What are Probability Sampling Methods

Probability sampling is a method where every member within a population is assigned a known, non-zero probability of being included. This type of sampling gives more accurate and statistically reliable results. The principal kinds of probability sampling include:

 

Simple Random Sampling

Simple Random Sampling (SRS) is among the simplest probability sampling methods which are widely used. Here, each member of the population has an equal likelihood of being chosen. For conducting SRS, we usually employ random number generators, lottery schemes, or random selection processes.

For instance, if we have a sample of 1000 individuals and wish to draw a sample of 100, we can number each person from 1 to 1000. We then draw 100 random numbers using a random number generator, and the respective individuals are selected.

1. Simple Random Sampling advantages:

  • Easy to comprehend and implement.

  • Eliminates bias since all the participants have an equal opportunity to be selected.

  • It is possible to generalize the results to the entire population.

2. Limitations of Simple Random Sampling:

  • May not always be feasible for very large populations.

  • May be inefficient if the population is highly scattered or difficult to access.

 

Systematic Sampling

Systematic Sampling entails the selection of every 'k-th' member from the roster of the population following the selection of an arbitrary starting point. Systematic sampling is more organized than the random sampling and is frequently applied when there is an available list of the population.

For instance, if we have a population of 1000 people and desire to take a sample of 100, then we would first determine the interval (k), which is 10 (1000 ÷ 100). Next, we randomly select a beginning point from 1 to 10 and pick every 10th person from there on.

1. Advantages of Systematic Sampling:

  • Simpler to conduct than simple random sampling, particularly in large populations.

  • Make sure that the sample is distributed evenly.

2. Disadvantages of Systematic Sampling:

  • May cause bias if there is an underlying structure in the list of population that aligns with the interval selected.

  • Not as flexible as simple random sampling.

 

Stratified Sampling

Stratified Sampling is applied when the population can be separated into subgroups, or strata, that possess a common trait. The sample is subsequently selected from every subgroup in such a manner that each subgroup is suitably represented in the sample.

For instance, if we are researching the levels of income among a population and we know that the population has people from varying levels of income, we may stratify the population according to income and draw samples from all the income groups. We aim to have each subgroup represented proportionally in the ultimate sample.

1. Advantages of Stratified Sampling

  • More precise and credible estimates, as it ensures that all subgroups are covered.

  • Has the potential to increase the precision of the sample with fewer subjects than simple random sampling.

2. Drawbacks of Stratified Sampling:

  • May be more complicated and time-consuming to carry out.

  • Assumes prior knowledge of the population structure.

 

Cluster Sampling

Cluster Sampling involves dividing the population into clusters and taking a random sample of these clusters. Everyone within the sampled clusters is surveyed. Cluster sampling is utilized whenever the population is dispersed over the geography or not easy to access.

For instance, if we are taking a survey of the whole country, we may classify the nation into zones or cities (clusters) and then take a few at random. Everyone in those zones would be questioned.

1. Advantages of Cluster Sampling:

  • Time- and cost-saving for large and geographically scattered populations.

  • More convenient to apply in large-scale studies.

2. Disadvantages of Cluster Sampling:

  • May lead to less accuracy than using other techniques such as stratified sampling.

  • May not be representative if the chosen clusters are not diverse.    

 

What are Non-Probability Sampling Methods

In non-probability sampling, the researcher is unaware of the probability of any individual being selected. These sampling procedures can be more subjective and tend to rely on the researcher's judgment. Although non-probability sampling can be faster and less expensive, it will introduce bias and the results cannot be generalized to the population at large. The principal types of non-probability sampling are:

 

Convenience Sampling

Convenience Sampling is when a sample is chosen based on convenience, or ease of accessibility. This type of sampling is commonly employed when there is a need for fast, low-cost data, but also has the potential to create a great deal of bias.

For instance, a researcher can survey individuals at their place of work, or in a mall because they are accessible. Though this is fast and easy, the sample will not be representative of the population.

1. Advantages of Convenience Sampling:

  • Rapid, simple, and cheap to execute.

  • Ideal for exploratory research or where there are limited resources.

2. Disadvantages of Convenience Sampling:

  • Unbalanced risk of bias since the sample population cannot be taken as representative.

  • Unlikely to be generalizable.

 

Judgmental or Purposive Sampling

Judgmental or Purposive Sampling means the selection of participants based on the researcher's judgment or objective. The researcher consciously chooses people who are deemed representative of the population.

Suppose a researcher wishes to conduct research on professional athletes' experiences. They would purposely choose some few elite athletes and assume their experiences are representative of the broad athlete population.

1. Benefits of Judgmental Sampling:

  • Helpful when researching particular subgroups or individuals with certain traits.

  • Less costly and efficient in terms of time.

2. Drawbacks of Judgmental Sampling:

  • A high potential for researcher bias in participant selection.

  • Could be non-representative of the population.

 

Snowball Sampling

Snowball Sampling is a method whereby current study participants bring in subsequent participants from their acquaintance network. This approach is typically employed in hard-to-reach group studies or where the population can be hard to define.

For instance, in a research on a rare disease, researchers may identify a couple of people having the disease first and then ask them to refer to people they know who are affected. This is done repeatedly, leading to a 'snowball' effect.

1. Strengths of Snowball Sampling:

  • Convenient for researching populations that are difficult to define.

  • Best used when there is no complete list of the population.

2. Weaknesses of Snowball Sampling

  • Probable bias, as the participants refer to someone who is similar to them.

  • Not a representation of the whole population.

 

Fun Facts

  • Random Doesn't Equal Unplanned - Simple Random Sampling is random-sounding but utilizes formal methods such as random number generators.

  • Sampling is a Game of Chance - Probability sampling involves manipulating the chances, whereas a dice roll has equal chances of all outcomes.

  • Systematic Sampling is a Game of Hopscotch - You pick every k-th individual, in a systematic pattern, similar to hopping on a game board.

  • Stratified Sampling = Slices of Pizza - You split a population into groups (strata), like slicing pizza to ensure that all the flavors are included.

  • Cluster Sampling is a Scavenger Hunt - Rather than selecting individuals, you snatch entire clusters (groups) as a time and effort-saver.

 

Conclusion

Sampling is an important component of research and data collection. By employing suitable sampling techniques, researchers are able to make inferences about a population based on a small sample size, conserving time and resources and still arriving at authentic results. The secret lies in learning the strengths, limitations, and proper use of each sampling technique so that the findings remain both valid and generalizable.

 

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Frequently Asked Questions on Sampling Methods

1. What are the 4 sampling methods?

The 4 sampling methods are:

  1. Simple Random Sampling

  2. Systematic Sampling

  3. Stratified Sampling

  4. Cluster Sampling

 

2. What are the 5 primary sampling types?

The 5 primary sampling types are:

  1. Probability Sampling

Random choice with a known probability for each person.

  1. Non-Probability Sampling

Chosen based on judgment or convenience, with no known probability of selection.

  1. Simple Random Sampling

An elementary probability method wherein each person has a known chance of selection.

  1. Stratified Sampling

Dividing the population into groups (strata) and sampling from every group.

  1. Cluster Sampling

Splitting the population into clusters and sampling from some randomly selected clusters.

 

3. What is sampling and its types?

Sampling is a procedure for selecting a part (or sample) of a larger population in order to make conclusions about the overall population.

There are two basic types of sampling:

Probability Sampling: Where each member of the population has a known, non-zero probability of being selected. This includes:

  • Simple Random Sampling

  • Systematic Sampling

  • Stratified Sampling

  • Cluster Sampling

Non-Probability Sampling: Where the selection is not made purely by random chance and tends to involve the use of subjective judgment. This includes:

  • Convenience Sampling

  • Judgmental or Purposive Sampling

  • Snowball Sampling

 

4. What are the 4 sampling strategies?

The 4 sampling strategies are:

  1. Simple Random Sampling

  2. Systematic Sampling

  3. Stratified Sampling

  4. Cluster Sampling

 

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