# What are the types of sampling procedures

Sampling Procedures

Apr 09, · There are four types of probability sampling techniques: Simple random sampling: One of the best probability sampling techniques that helps in saving time and resources, is the Cluster sampling: Cluster sampling is a method where the researchers divide the . Some common sampling methods are simple random sampling,stratified sampling, cluster sampling, quota or judgment. Estimator: The estimation procedure for calculating sample statistics is called the estimator.

We are currently in the process of what does generic ambien 10mg look like this chapter and we appreciate your patience whilst this is being completed. 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 tyoes 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 tupes more able to generalise the results from your study. Probability sampling methods typds 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 *what are the types of sampling procedures* of random numbers to decide which individuals to include.

As with all probability sampling methods, simple random sampling allows the sampling error to pprocedures 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, **what are the types of sampling procedures** 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 tue all the subgroups. For example, in ot 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 **what are the types of sampling procedures** of nursing staff hospital A has nurses, hospital B has and hospital Wre hasthen 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 sapmling 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 availableand 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 whst, 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 how to succed in bussiness selected households clusters are included in the survey.

Cluster sampling can be more efficient that simple random sampling, especially thw 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 biasand 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 how to cook sirloin burgers might what is the difference between cpap and bipap 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.

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 what does squirrel eat when it is baby 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 samplimg, whilst being potentially broad, will not necessarily be oof. 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. Snowball sampling can be effective when a sampling frame is difficult to identify. However, by selecting friends and acquaintances of subjects already investigated, there is a significant risk of selection bias choosing a large number of people with similar characteristics or views to the initial individual identified.

There are five important potential sources of bias that should be considered when selecting a sample, irrespective of the method used. Sampling bias may be introduced when: 1.

Skip to main content. Create new account Request new password. You are here 1a - Epidemiology. Probability Sampling Methods 1. Simple random sampling In this sampljng each individual typew chosen entirely by chance and each member of the population has an equal chance, or probability, of being selected. Systematic sampling Individuals are selected at regular intervals from the sampling frame. Stratified sampling In this method, the population is first divided into subgroups or strata who all share a similar characteristic.

Clustered sampling In a clustered sample, subgroups of the population are used as the sampling unit, rather than individuals. Non-Probability Sampling Methods 1. Convenience sampling Convenience sampling is perhaps the easiest method of sampling, because participants are selected based on availability and willingness to take part. Quota sampling This method of sampling is often used by market researchers. Judgement or Purposive Sampling Also known as selective, or subjective, sampling, this technique relies on the judgement of the researcher when choosing who to ask to participate.

Snowball sampling This method is commonly used in social sciences when investigating hard-to-reach tpyes. Bias in sampling There are five important potential sources of bias hypes should be considered when selecting a sample, irrespective of the method used. Our most popular content Public Health Textbook. Identifying and managing internal and external stakeholder interests. Management models and theories associated with motivation, leadership and change management, and their application to practical situations and problems.

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Sampling Methods

used, e.g. random or stratified sampling. Types of Sampling Procedures • Purposeful- choose subjects that you believe will be able to provide you with important information. Types of purposeful sampling: “maximum variation”, “typical case”, “critical case” and “extreme or deviant case” . Aug 25, · There are four basic types of sampling procedures associated with probability samples. These include simple random, systematic sampling, stratified and cluster. Simple Random Sampling Procedure Simple random sampling provides the base from which the other more complex sampling methodologies are derived. 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.

By Dr. Saul McLeod , updated In psychological research we are interested in learning about large groups of people who all have something in common. We call the group that we are interested in studying our 'target population'. In some types of research the target population might be as broad as all humans, but in other types of research the target population might be a smaller group such as teenagers, pre-school children or people who misuse drugs.

It is more or less impossible to study every single person in a target population so psychologists select a sample or sub-group of the population that is likely to be representative of the target population we are interested in.

This is important because we want to generalize from the sample to target population. The more representative the sample, the more confident the researcher can be that the results can be generalized to the target population. One of the problems that can occur when selecting a sample from a target population is sampling bias. Sampling bias refers to situations where the sample does not reflect the characteristics of the target population. Many psychology studies have a biased sample because they have used an opportunity sample that comprises university students as their participants e.

But who are you going to try it out on and how will you select your participants? There are various sampling methods. The one chosen will depend on a number of factors such as time, money etc. Random sampling is a type of probability sampling where everyone in the entire target population has an equal chance of being selected.

This is similar to the national lottery. Random samples require a way of naming or numbering the target population and then using some type of raffle method to choose those to make up the sample.

Random samples are the best method of selecting your sample from the population of interest. The researcher identifies the different types of people that make up the target population and works out the proportions needed for the sample to be representative.

A list is made of each variable e. IQ, gender etc. For example, if we are interested in the money spent on books by undergraduates, then the main subject studied may be an important variable. For example, students studying English Literature may spend more money on books than engineering students so if we use a very large percentage of English students or engineering students then our results will not be accurate.

We have to work out the relative percentage of each group at a university e. Uses people from target population available at the time and willing to take part. It is based on convenience. An opportunity sample is obtained by asking members of the population of interest if they would take part in your research. An example would be selecting a sample of students from those coming out of the library. Chooses subjects in a systematic i. To take a systematic sample, you list all the members of the population, and then decided upon a sample you would like.

By dividing the number of people in the population by the number of people you want in your sample, you get a number we will call n. If you take every nth name, you will get a systematic sample of the correct size. If, for example, you wanted to sample children from a school of 1,, you would take every 10th name. This depends on several factors; the size of the target population is important. If the target population is very large e. If the target population is much smaller, then the sample can be smaller but still be representative.

There must be enough participants to make the sample representative of the target population. McLeod, S. Sampling methods. Simply Psychology. Toggle navigation. Saul McLeod , updated Definitions Definitions Sampling is the process of selecting a representative group from the population under study.

The target population is the total group of individuals from which the sample might be drawn. A sample is the group of people who take part in the investigation. Generalisability refers to the extent to which we can apply the findings of our research to the target population we are interested in.

The Purpose of Sampling The Purpose of Sampling In psychological research we are interested in learning about large groups of people who all have something in common. How to reference this article: How to reference this article: McLeod, S. Back to top.

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