7 Sampling Methods Explained Visually

In this article, we will cover 7 sampling methods, which we are going to divide into 2 types: probability sampling methods, and non-probability sampling methods.

Probability sampling methods involve random selection of participants, and therefore tend to produce unbiased samples; Non-probability methods do not involve random selection of participants, and therefore are cheaper to apply, but produce samples that are more prone to bias.

We will start by discussing 4 probability sampling methods:

  1. Simple random sampling
  2. Systematic sampling
  3. Stratified sampling
  4. Cluster sampling

And then 3 non-probability sampling methods:

  1. Convenience sampling
  2. Quota sampling
  3. Network sampling

1. Simple random sampling

animation representing how simple random sampling works

1.1. How it works:

Step 1: Make a list of all N individuals in the population.

Step 2: Randomly select n participants to be included in the study (selection can be with or without replacement).

1.2. Advantage:

  • It provides a simple and fair way of selecting the sample, since every person will have the same probability of being included in the study.
  • Since it is based on randomness, it will produce an unbiased sample that is representative of the population.

1.3. Limitation:

  • Randomly choosing a sample from a list of all individuals in the population requires us to have such a list, which is in general difficult to obtain.
  • It can be very expensive and time consuming, especially when participants are geographically dispersed.

2. Systematic sampling

animation representing how systematic sampling works

2.1. How it works:

Step 1: Select a random starting point i, and include the ith person in the sample.

Step 2: Pick an interval k, then include every kth person in the sample until you reach the sample size n. Those included will be the set: {i, i+k, i+2k, …, i + (n-1)k}.

2.2. Advantage:

  • The sample is evenly spread across the population..
  • Sometimes it is easier to apply than simple random sampling.

2.3. Limitation:

  • A patterns may be present in the population making the sample biased.

3. Stratified sampling

animation representing how stratified sampling works

3.1. How it works:

Step 1: Divide the population into subgroups/strata of individuals who share certain characteristics (like age, gender, income level, etc.).

Step 2: Calculate the number of individuals you want to sample from each subgroup/stratum (so that the proportion of each subgroup in your sample will match that of the population), and then apply simple random sampling within each stratum to build your sample.

3.2. Advantage:

  • It forces the sample to resemble the population regarding certain attributes that may influence the outcome, and therefore, increases the generalizability of our results.
  • It is mostly useful when we want to make sure that minority groups are represented in the sample.

3.3. Limitation:

  • Sometimes the population cannot be divided into different strata.
  • Creating stratified lists of individuals may be expensive.

4. Cluster sampling

animation representing how cluster sampling works

4.1. How it works:

Step 1: Divide the population into clusters (for example, according to their location on the map).

Step 2: Apply simple random sampling to select a subset of clusters.

Step 3: Include all individuals from the selected clusters in your sample.

4.2. Advantage:

  • Cheaper and more efficient to apply when dealing with a geographically dispersed population.

4.3. Limitation:

  • The sample will be biased if the clusters do not represent the population.

5. Convenience sampling

animation representing how convenience sampling works

5.1. How it works:

Select individuals that are the easiest to locate or contact.

5.2. Advantage:

  • Cheap, time saving, and simple to implement.

5.3. Limitation:

  • Often leads to selection bias and thus the study results will not be generalizable.

6. Quota sampling

animation representing how quota sampling works

6.1. How it works

Step 1: Divide the population into subgroups/strata of individuals who share certain characteristics (like age, gender, income level, etc.).

Step 2: Calculate the number of individuals you want to sample from each subgroup/stratum (so that the proportion of each subgroup in your sample will match that of the population), and then apply convenience sampling within each stratum to build your sample.

6.2. Advantage:

  • Compared to stratified sampling, quota sampling is cheaper, faster, and easier to apply because it does not require random sampling.
  • Compared to other non-probability sampling methods (convenience sampling and network sampling), this method ensures representativeness of all subgroups (especially minority groups).

6.3. Limitation:

  • Since this method is not based on random selection of participants, selection bias is possible and the sample representativeness is not guaranteed.

7. Network sampling

animation representing how network sampling works

7.1. How it works

Step 1: Identify few initial participants to be included in your study.

Step 2: Ask these participants to refer others to be included in the study. (Repeat this step many times until you reach the target sample size).

7.2. Advantage:

  • Useful when individuals in the population are difficult to identify (e.g. drug users).

7.3. Limitation:

  • Selection bias is possible since the sample is not selected at random.
  • May be slow because it relies on the referral of existing participants to build up the sample.

References

  • Berndt, A. E. (2020). Sampling Methods. Journal of Human Lactation, 36(2), 224–226. https://doi.org/10.1177/0890334420906850
  • Elliott, M. R., & Valliant, R. (2017). Inference for Nonprobability Samples. Statistical Science, 32(2), 249–264. https://doi.org/10.1214/16-STS598
  • Sharma, G. (2017). Pros and cons of different sampling techniques. International Journal of Applied Research, 3(7), 749–752.

Further reading