• Deciding how to select a sample that is representative of the population as a
whole (Khan, 2020).
• The process of through which sample is selected from the population (Khan,
• It is a unit that is selected from population
• Represents the whole population
• Purpose to draw the inference.
• The actual list of population from which a sample is drawn from (Khan, 2020).
6. Probability sampling
• Minimal knowledge of population
• Easy to analyze data
• Involves the random selection,
• Allow a researcher to make a strong statistical inference about the whole population
• Every member of the population has a chance of being selected.
• It is mainly used in quantitative research (Mccombesa, 2023).
1. Simple random sampling
• All subsets of the frame are given an equal probability.
• Equal chance of being selected
• It can be done using a random number generators
• It provides unbiased results but can be time-consuming.
• Low frequency of use
• Does not use researchers’ expertise
• Larger risk of random error
7. 2. Systematic sampling
• Order all units in the sampling frame
• Then every nth number on the list is selected
• N= Sampling Interval
• After randomly selecting a starting point.
• It is straightforward to implement but may introduce bias if there is a pattern in the
• Moderate cost; moderate usage
• Simple to draw sample
• Easy to verify
• Periodic ordering required (Mccombesa, 2023)
8. Probability sampling cont’d
3. Stratified random sampling
• Population is divided into two or more groups called strata
• Based on the relevant characteristic (e.g., gender identity, age range, income
bracket, job role).
• Subsamples are randomly selected from each strata (Mccombesa, 2023).
9. Probability sampling cont’d
• Assures representation of all groups in sample population
• Characteristics of each stratum can be estimated and comparisons made
• Requires accurate information on proportions of each stratum
• Stratified lists costly to prepare Tam and Woo, 2020).
4. Cluster sampling:
• The population is divided into subgroups (clusters) like families.
• A simple random sample is taken from each cluster.
• A random selection of clusters is chosen.
• It is useful when the population is geographically dispersed,
• But it may lead to less precision compared to other techniques. (Peven et al., 2019)
10. Cluster sampling
• Can estimate characteristics of both cluster and population
• The cost to reach an element to sample is very high
• Each stage in cluster sampling introduces sampling error,
• The more stages there are, the more error there tends to be.
11. 5. Multistage sampling
• The population is divided into different stages, and a sample is selected at each stage.
• It starts with selecting larger clusters or groups from the population in the first stage.
• Then, within each selected cluster, smaller clusters or units are chosen in the second stage,
• This process may continue through several stages until the final sample units are selected.
• The final sample units can be individuals, households, or any other relevant units.
• Useful when the target population is large, geographically dispersed, or difficult to access.
• More Accurate
• More Effective
• Each stage in sampling introduces sampling error
• The more stages there are, the more error there tends to be
12. Non probability sampling
• Individuals are selected based on non-random criteria, and not every individual has a
chance of being included.
• Often used in exploratory and qualitative research (Mccombesa, 2023).
• Units of the sample are chosen on the basis of personal judgment or convenience.
1. Snowball sampling
• The research starts with a key person and introduce the next one to become a chain
• Low cost
• Useful in specific circumstances & for locating rare populations
• Not independent
• Projecting data beyond sample not justified, (Elfil and Negida, 2017).
13. 2. Convenience sampling
• The process of including whoever happens to be available at the time…called
“accidental” or “haphazard” sampling.
• But there is no way to tell if the sample is representative of the population
• Very low cost
• Extensively used/understood
• Variability and bias cannot be measured or controlled
• Projecting data beyond sample not justified
• It can’t produce generalizable results.
14. Purposive sampling
• Involves the researcher using their expertise to select a sample that is
most useful to the purposes of the research.
• Also called “judgmental” sampling.
• There is a assurance of Quality response
• Meet the specific objective.
• Bias selection of sample may occur
• Time consuming process. (Mccombesa, 2023)
15. Quota sampling
• The process whereby a researcher gathers data from individuals possessing
identified characteristics and quotas.
• You first divide the population into mutually exclusive subgroups (called strata)
and then recruit sample units until you reach your quota. (Omair, 2014).
• These units share specific characteristics, determined by you prior to forming your
• The aim of quota sampling is to control what or who makes up your sample.
• Used when research budget is limited
• Very extensively used/understood
• No need for list of population elements
• Variability and bias cannot be
• Time Consuming Projecting data beyond
sample not justified
16. Self-selection sampling
• It occurs when you allow each case usually individuals, to identify their desire to
take part in the research.
• Instead of the researcher choosing participants and directly contacting them,
people volunteer themselves (e.g. by responding to a public online survey).
• More accurate
• Useful in specific circumstances to serve the purpose.
• More costly due to Advertising
• Mass are left. (Mccombesa, 2023)
• Elfil, M., & Negida, A. (2017). Sampling methods in clinical research: An educational review. Emergency,
5 (1), Article e52, 1–3.
• Firchow, P., & MacGinty, R. (2020). Including hard-to-access pop- ulations using mobile phone surveys
and participatory indica- tors. Sociological Methods & Research, 49(1), 133–160. Magnani, R., Sabin, K.,
Saidel, T., & Heckathorn, D. (2005). Review of sampling hard-to-reach and hidden populations for HIV
surveillance. AIDS, 19 (Suppl. 2), S67–S72.
• Omair, A. (2014). Sample size estimation and sampling techniques for selecting a representative sample.
Journal of Health Specialties, 2(4), 142–147.
• Peven, K., Purssell, E., Taylor, C., Bick, D., and Lopez, V. K. (2019). Breastfeeding support in low and
middle-income countries: Secondary analysis of national survey data, Midwifery, 82.
https://doi.org/10.1016/j.midw.2019.102601 Shorten, A., & Moorley, C. (2014). Selecting the sample.
Evidence Based Nursing, 17(2), 32–33.
• Tam, W., Lo, K., and Woo, B. (2020). Reporting sample size cal- culations for randomized controlled trials
published in nurs- ing journals: A cross-sectional study. International Journal of Nursing Studies, 102.
• McCombes, S. (2023). Sampling Methods | Types, Techniques & Examples. Scribbr. Retrieved July 9,
2023, from https://www.scribbr.com/methodology/sampling-methods/
Hinweis der Redaktion
To produce a results that are representative of the whole population, probability sampling techniques are the most valid choice.