Infographic: Sampling

Methods of Sampling

Elizabeth Barreto Liah Trinidad Valdez Emiliano Ibarra

Probability Sampling Methods

Simple Random Sampling & Systematic Sampling Stratified Sampling & Clustered Sampling

Simple Random Sampling

Systematic Sampling

• each individual is chosen entirely by chance
• each member of the population has an equal chance, or probability, of being selected
• simple random sampling allows the sampling error to be calculated and reduces selection bias
• the most straightforward method of probability sampling
• Disadvantages:

- you may not select enough individuals with your characteristic of interest difficult to define a complete sampling frame and inconvenient to contact them

• often more convenient than simple random sampling, and it is easy to administer
• however, it may lead to bias
• individuals are selected at regular intervals from the sampling frame
• the intervals are chosen to ensure an adequate sample size

Clustered Sampling

Stratified Sampling

• the population is first divided into subgroups (or strata) who all share a similar characteristic
• used when we might reasonably expect the measurement of interest to vary between the different subgroups
• improves the accuracy and representativeness of the results by reducing sampling bias
• however, requires knowledge of the appropriate characteristics of the sampling frame

• subgroups of the population are used as the sampling unit, rather than individuals
• population is divided into subgroups, known as clusters (randomly selected)
• can be more efficient that simple random sampling, especially where a study takes place over a wide geographical region
• however, it includes an increased risk of bias

Non-Probability Sampling Methods

Convinience Sampling & Quota Sampling Judgement (or Purposive) Sampling & Snowball Sampling

Quota Sampling

Convinience Sampling

Convenience sampling is the most effortless sampling approach as participants are chosen based on their readiness and willingness to participate. Although it can obtain valuable results, the possibility of significant bias cannot be ruled out. The individuals who choose to participate in such studies may differ from those who do not (volunteer bias). Moreover, the sample may not be a true representation of other key characteristics such as age or gender. It's worth noting that volunteer bias is a risk associated with all non-probability sampling methods.

Market researchers commonly utilize a sampling approach that involves interviewers given specific quotas of participants to recruit. For instance, an interviewer could be instructed to select 20 adult males, 20 adult females, 10 teenage girls, and 10 teenage boys to be interviewed about their television watching habits. The intent is to ensure that the chosen quotas reflect the underlying population characteristics in a proportionate manner.

Judgement (or Purposive) Sampling

Snowball Sampling

This method is commonly used in social sciences when investigating hard-to-reach groups. Snowball sampling is beneficial when it is difficult to define the sampling frame. However, by choosing friends and acquaintances of subjects that have already been studied, there is a significant possibility of selection bias (selecting a large number of people with similar characteristics or perspectives to the initial individual identified).

Also known as selective, or subjective, sampling, this technique relies on the judgement of the researcher when choosing who to ask to participate. Judgement sampling has the advantage of being time-and cost-effective to perform whilst resulting in a range of responses (particularly useful in qualitative research). However, in addition to volunteer bias, it is also prone to errors of judgement by the researcher and the findings, whilst being potentially broad, will not necessarily be representative.

• Any pre-agreed sampling rules are deviated from
• People in hard-to-reach groups are omitted
• Selected individuals are replaced with others, for example if they are difficult to contact
• There are low response rates
• An out-of-date list is used as the sample frame (for example, if it excludes people who have recently moved to an area)

Bias in Sampling

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Probablity Sampling

non-probability sampling

bias in sampling