What kind of sample

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. 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. 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.

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 nurses, hospital B has and hospital C has , then it would be appropriate to choose the sample numbers from each hospital proportionally e.

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. Stratified sampling improves the accuracy and representativeness of the results by reducing sampling bias.

However, it requires knowledge of the appropriate characteristics of the sampling frame the details of which are not always available , and it can be difficult to decide which characteristic s to stratify by. 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. Systematic sampling is similar to simple random sampling, but it is usually slightly easier to conduct. Every member of the population is listed with a number, but instead of randomly generating numbers, individuals are chosen at regular intervals.

All employees of the company are listed in alphabetical order. From the first 10 numbers, you randomly select a starting point: number 6. From number 6 onwards, every 10th person on the list is selected 6, 16, 26, 36, and so on , and you end up with a sample of people. If you use this technique, it is important to make sure that there is no hidden pattern in the list that might skew the sample.

For example, if the HR database groups employees by team, and team members are listed in order of seniority, there is a risk that your interval might skip over people in junior roles, resulting in a sample that is skewed towards senior employees. Stratified sampling involves dividing the population into subpopulations that may differ in important ways. It allows you draw more precise conclusions by ensuring that every subgroup is properly represented in the sample.

To use this sampling method, you divide the population into subgroups called strata based on the relevant characteristic e. Based on the overall proportions of the population, you calculate how many people should be sampled from each subgroup.

Then you use random or systematic sampling to select a sample from each subgroup. The company has female employees and male employees. You want to ensure that the sample reflects the gender balance of the company, so you sort the population into two strata based on gender.

Then you use random sampling on each group, selecting 80 women and 20 men, which gives you a representative sample of people. Cluster sampling also involves dividing the population into subgroups, but each subgroup should have similar characteristics to the whole sample.

Instead of sampling individuals from each subgroup, you randomly select entire subgroups. If it is practically possible, you might include every individual from each sampled cluster. If the clusters themselves are large, you can also sample individuals from within each cluster using one of the techniques above. This is called multistage sampling. This method is good for dealing with large and dispersed populations, but there is more risk of error in the sample, as there could be substantial differences between clusters.

The company has offices in 10 cities across the country all with roughly the same number of employees in similar roles. See an example. In a non-probability sample, individuals are selected based on non-random criteria, and not every individual has a chance of being included.

This type of sample is easier and cheaper to access, but it has a higher risk of sampling bias. That means the inferences you can make about the population are weaker than with probability samples, and your conclusions may be more limited. If you use a non-probability sample, you should still aim to make it as representative of the population as possible. Non-probability sampling techniques are often used in exploratory and qualitative research.

In these types of research, the aim is not to test a hypothesis about a broad population, but to develop an initial understanding of a small or under-researched population. A convenience sample simply includes the individuals who happen to be most accessible to the researcher. You are researching opinions about student support services in your university, so after each of your classes, you ask your fellow students to complete a survey on the topic.

This is a convenient way to gather data, but as you only surveyed students taking the same classes as you at the same level, the sample is not representative of all the students at your university. Similar to a convenience sample, a voluntary response sample is mainly based on ease of access. Instead of the researcher choosing participants and directly contacting them, people volunteer themselves e. Voluntary response samples are always at least somewhat biased, as some people will inherently be more likely to volunteer than others.

You send out the survey to all students at your university and a lot of students decide to complete it. This type of sampling, also known as judgement sampling, involves the researcher using their expertise to select a sample that is most useful to the purposes of the research.

It is often used in qualitative research , where the researcher wants to gain detailed knowledge about a specific phenomenon rather than make statistical inferences, or where the population is very small and specific. An effective purposive sample must have clear criteria and rationale for inclusion. And continually iterate and improve them. Meet the operating system for experience management. Experience iD is a connected, intelligent system for ALL your employee and customer experience profile data.

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Deliver breakthrough contact center experiences that reduce churn and drive unwavering loyalty from your customers. In survey research, sampling is the process of using a subset of a population to represent the whole population. To ask every person would be almost impossible.

Sampling allows large-scale research to be carried out with a more realistic cost and time-frame because it uses a smaller number of individuals in the population with representative characteristics to stand in for the whole.

However, when you decide to sample, you take on a new task. You have to decide who is part of your sample list and how to choose the people who will best represent the whole population. How you go about that is what the practice of sampling is all about.

Although the idea of sampling is easiest to understand when you think about a very large population, it makes sense to use sampling methods in research studies of all types and sizes. And because sampling allows you to research larger target populations using the same resources as you would smaller ones, it dramatically opens up the possibilities for research.

Sampling is a little like having gears on a car or bicycle. It allows us to do things like carrying out exit polls during elections, map the spread and effects rates of epidemics across geographical areas, and carry out nationwide census research that provides a snapshot of society and culture. Sampling strategies in research vary widely across different disciplines and research areas, and from study to study. Here are some of the best-known options.