It would normally be impractical to study a whole population, for example when doing a questionnaire survey. Sampling is a method that allows researchers to infer information about a population based on results from a subset of the population, without having to investigate every individual. Reducing the number of individuals in a study reduces the cost and workload, and may make it easier to obtain high quality information, but this has to be balanced against having a large enough sample size with enough power to detect a true association. (Calculation of sample size is addressed in section 1B (statistics) of the Part A syllabus.)
If a sample is to be used, by whatever method it is chosen, it is important that the individuals selected are representative of the whole population. This may involve specifically targeting hard to reach groups. For example, if the electoral roll for a town was used to identify participants, some people, such as the homeless, would not be registered and therefore excluded from the study by default.
There are several different sampling techniques available, and they can be subdivided into two groups: probability sampling and non-probability sampling. In probability (random) sampling, you start with a complete sampling frame of all eligible individuals from which you select your sample. In this way, all eligible individuals have a chance of being chosen for the sample, and you will be more able to generalise the results from your study. Probability sampling methods tend to be more time-consuming and expensive than non-probability sampling. In non-probability (non-random) sampling, you do not start with a complete sampling frame, so some individuals have no chance of being selected. Consequently, you cannot estimate the effect of sampling error and there is a significant risk of ending up with a non-representative sample which produces non-generalisable results. However, non-probability sampling methods tend to be cheaper and more convenient, and they are useful for exploratory research and hypothesis generation.
In this case each individual is chosen entirely by chance and each member of the population has an equal chance, or probability, of being selected. One way of obtaining a random sample is to give each individual in a population a number, and then use a table of random numbers to decide which individuals to include.1 For example, if you have a sampling frame of 1000 individuals, labelled 0 to 999, use groups of three digits from the random number table to pick your sample. So, if the first three numbers from the random number table were 094, select the individual labelled “94”, and so on.
As with all probability sampling methods, simple random sampling allows the sampling error to be calculated and reduces selection bias. A specific advantage is that it is the most straightforward method of probability sampling. A disadvantage of simple random sampling is that you may not select enough individuals with your characteristic of interest, especially if that characteristic is uncommon. It may also be difficult to define a complete sampling frame and inconvenient to contact them, especially if different forms of contact are required (email, phone, post) and your sample units are scattered over a wide geographical area.
Individuals are selected at regular intervals from the sampling frame. The intervals are chosen to ensure an adequate sample size. If you need a sample size n from a population of size x, you should select every x/nth individual for the sample. For example, if you wanted a sample size of 100 from a population of 1000, select every 1000/100 = 10th member of the sampling frame.
Systematic sampling is often more convenient than simple random sampling, and it is easy to administer. However, it may also lead to bias, for example if there are underlying patterns in the order of the individuals in the sampling frame, such that the sampling technique coincides with the periodicity of the underlying pattern. As a hypothetical example, if a group of students were being sampled to gain their opinions on college facilities, but the Student Record Department’s central list of all students was arranged such that the sex of students alternated between male and female, choosing an even interval (e.g. every 20th student) would result in a sample of all males or all females. Whilst in this example the bias is obvious and should be easily corrected, this may not always be the case.
In this method, the population is first divided into subgroups (or strata) who all share a similar characteristic. It is used when we might reasonably expect the measurement of interest to vary between the different subgroups, and we want to ensure representation from all the subgroups. For example, in a study of stroke outcomes, we may stratify the population by sex, to ensure equal representation of men and women. The study sample is then obtained by taking equal sample sizes from each stratum. In stratified sampling, it may also be appropriate to choose non-equal sample sizes from each stratum. For example, in a study of the health outcomes of nursing staff in a county, if there are three hospitals each with different numbers of nursing staff (hospital A has 500 nurses, hospital B has 1000 and hospital C has 2000), then it would be appropriate to choose the sample numbers from each hospital proportionally (e.g. 10 from hospital A, 20 from hospital B and 40 from hospital C). This ensures a more realistic and accurate estimation of the health outcomes of nurses across the county, whereas simple random sampling would over-represent nurses from hospitals A and B. The fact that the sample was stratified should be taken into account at the analysis stage.
In a clustered sample, subgroups of the population are used as the sampling unit, rather than individuals. The population is divided into subgroups, known as clusters, which are randomly selected to be included in the study. Clusters are usually already defined, for example individual GP practices or towns could be identified as clusters. In single-stage cluster sampling, all members of the chosen clusters are then included in the study. In two-stage cluster sampling, a selection of individuals from each cluster is then randomly selected for inclusion. Clustering should be taken into account in the analysis. The General Household survey, which is undertaken annually in England, is a good example of a (one-stage) cluster sample. All members of the selected households (clusters) are included in the survey.1
Cluster sampling can be more efficient that simple random sampling, especially where a study takes place over a wide geographical region. For instance, it is easier to contact lots of individuals in a few GP practices than a few individuals in many different GP practices. Disadvantages include an increased risk of bias, if the chosen clusters are not representative of the population, resulting in an increased sampling error.
Convenience sampling is perhaps the easiest method of sampling, because participants are selected based on availability and willingness to take part. Useful results can be obtained, but the results are prone to significant bias, because those who volunteer to take part may be different from those who choose not to (volunteer bias), and the sample may not be representative of other characteristics, such as age or sex. Note: volunteer bias is a risk of all non-probability sampling methods.
This method of sampling is often used by market researchers. Interviewers are given a quota of subjects of a specified type to attempt to recruit. For example, an interviewer might be told to go out and select 20 adult men, 20 adult women, 10 teenage girls and 10 teenage boys so that they could interview them about their television viewing. Ideally the quotas chosen would proportionally represent the characteristics of the underlying population.
Also known as selective, or subjective, sampling, this technique relies on the judgement of the researcher when choosing who to ask to participate. Researchers may implicitly thus choose a “representative” sample to suit their needs, or specifically approach individuals with certain characteristics. This approach is often used by the media when canvassing the public for opinions and in qualitative research.
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.
This method is commonly used in social sciences when investigating hard-to-reach groups. Existing subjects are asked to nominate further subjects known to them, so the sample increases in size like a rolling snowball. For example, when carrying out a survey of risk behaviours amongst intravenous drug users, participants may be asked to nominate other users to be interviewed.
Types of Sampling Methods (4.1)
What are the types of sampling?
All types of sampling fall into one of these two fundamental categories:
What is sampling?
Sampling is the selection of subjects in a statistical study to represent a larger population. Because testing every member of a given population isn’t always feasible, researchers select samples to make testing more efficient and cost-effective.
How researchers develop samples can have a significant impact on the quality of the studys results. The following elements determine a samples efficacy:
5 types of probability sampling
Here are the five types of probability sampling that researchers use:
1. Simple random sampling
Simple random sampling, or SRS, occurs when each sample participant has the same probability of being chosen for the study. Consider a lottery method. You can place all possible respondents in a pool and randomly, or blindly, select participants. Every person in the pool has the same likelihood that you will choose them. Researchers may also use computer programs that generate random numbers from a set.
Random sampling provides less opportunity for bias and influence by researchers in participant selection. However, true random sampling can be challenging because it requires a list of every potential participant.
2. Stratified sampling
Stratified sampling is a variation of random sampling that involves dividing the population into distinct groups, or strata. This method aims to make samples more representative of the population. One study may incorporate several groups. To create a representative sample, researchers take a simple random sample from each stratum.
For example, if a population consists of 650 females and 350 males, researchers may divide the population into males and females. Then, you can choose 65 female respondents and 35 male respondents via random sampling to get a representative sample of 100 participants. Professionals may divide strata into categories, including:
3. Systematic random sampling
Systematic sampling occurs when researchers reference a list and choose a certain subgroup as study participants. For example, you can compile a list of 250 individuals in a population and use every fifth person as a study participant.
Systematic sampling aims to eliminate bias and can be easier to achieve than random sampling. However, systematic sampling differs from simple random sampling because the systematic method doesn’t offer the same probability of being chosen for every member of a population.
4. Cluster sampling
Cluster sampling involves dividing a certain population into groups, or clusters. Often, clusters correlate to different geographic areas. Researchers choose clusters to use in their study randomly, and every member of each cluster takes part in the study.
For example, you could examine the dining habits of residents in a certain state. You can divide these. residents into clusters based on the county they live in and then use a random sampling method to select eight counties for the study. Cluster sampling differs from strata sampling because some clusters are unrepresented in the final sample, whereas researchers use members from every stratum in stratified sampling.
5. Multistage sampling
Multistage sampling occurs when you use different sampling methods at different stages of the same study. This method is helpful for large population sizes. For instance, consider determining how much support a new government initiative has across the country. Its not practical to list every person in the country, so you may start by creating clusters in stage one for each state or geographic region, like southwest, southeast, northeast and northwest. In the next stage, you may further divide these clusters into strata and choose random samples from each stratum.
4 types of nonprobability sampling
Here are four examples of nonprobability sampling:
1. Convenience sampling
In this type of sampling, researchers use random people as testing subjects. For example, a researcher may sample a group of people walking by on a street. In this case, the researcher has no control of the sample group itself. This type of sampling is both cost and time efficient as researchers can gather people to sample relatively quickly.
2. Quota sampling
Quota sampling involves researchers creating a sample based on predefined traits. For example, the researcher might gather a group of people who are all aged 65 or older. This allows researchers to easily gather data from a specific demographic.
3. Judgemental sampling
In judgemental sampling, the chosen research subjects are solely at the discretion of the researcher. In this case, the researcher is responsible for picking individuals who they feel would be a positive addition to the study. To create their sample, researchers may ask prospective individuals a few questions relating to the study and then decide based on their answers.
4. Snowball sampling
Researchers use snowball sampling when the study involves sampling groups of people who are more difficult to gather. To gather people to survey, researchers may ask the test subjects they do have to contact and nominate others to take part in the study. While this can be an effective way to gather participants, it makes the factors of the test group more difficult to control.
What to avoid when creating a sample
Sampling for research should aim to be unbiased and representative. The best way to create these types of samples is through probability sampling. Nonprobability sampling can lead to inaccuracies, bias and misrepresentation in the sample. Here are a few types of sampling that you may want to avoid:
Convenience sampling occurs when researchers choose respondents based on elements of convenience, such as being near respondents or being close friends with respondents. For instance, a survey conductor may poll people at a nearby park. Convenience sampling is easier and cheaper than random sampling, but you cannot generalize the results, which makes it less reliable.
Voluntary response sampling
Voluntary response sampling refers to soliciting responses from volunteers. Unlike other studies, participants select themselves rather than being selected by those carrying out the research. For instance, a teaching assistant may send an evaluation survey via email asking for feedback on their performance. Voluntary response sampling is typically unrepresentative and not random, as only respondents with strong opinions are likely to participate.
What are the 5 types of samples?
- Simple random sampling. In a simple random sample, every member of the population has an equal chance of being selected. …
- Systematic sampling. …
- Stratified sampling. …
- Cluster sampling.
What are the different type of sampling?
What are the two main types of sampling?