It is difficult to locate the whole population everywhere and to have access to all the population. If we are conducting a study on patients with ischemic stroke, it will be difficult to include the whole population of ischemic stroke all over the world. This is used generally during the initial stages of a survey and is quick and easy to deliver results.In clinical research, we define the population as a group of people who share a common character or a condition, usually the disease. When the availability of samples is rare, convenience samples are selected. Here the samples are selected based on availability. This process continues until the cluster cannot be divided any further Alternatives to random samplingĬonvenience sampling refers to approaches where considerations of simplicity rather than randomness determine which observations are selected in a sample. One or more clusters can be randomly selected from each stratum. The population is divided into multiple clusters and then these clusters are further divided and grouped into various subgroups (strata) based on similarity. Multi-stage sampling is a combination of one or more of the techniques described above. This is achieved by defining clusters according to the ease of access (e.g., a suburb may be a cluster if door-to-door sampling or a household may be a cluster if phone interviewing). Typically, the purpose of cluster sampling is to reduce the costs of data collection. Then some of these subgroups are selected at random, and simple random samples are then collected within these subgroups. Similar to stratified random sampling, cluster sampling divides the sample into a large number of subgroups. We need to have prior information about the population to create subgroups. The main benefit of stratified sampling over simple random sampling is making sure that you have good sample sizes in key subgroups. The required sample size for each stratum will be designed either to match the known population proportions or to over-represent key subgroups of interest. Say you want to achieve a sample size of 200, then you can pick samples of 50 from each stratum. For example, males under 30, females under 30, males 30 or over, and females 30 or over. The elements are randomly selected from each of these strata. This technique divides the elements of the population into key subgroups or strata. For example, a random selection of 20 students from a class of 50 students gives a probability of selection being 1/50. It involves picking the desired sample size and selecting observations from a population in such a way that each observation has an equal chance of selection until the desired sample size is achieved. Simple random sampling is the most straightforward approach to getting a random sample. Non-random sampling techniques are often referred to as convenience sampling. The following random sampling techniques will be discussed: simple random sampling, stratified sampling, cluster sampling, and multi-stage sampling. For example, exit polls from voters that aim to predict the likely results of an election. It is typically assumed that statistical tests contain data that has been obtained through random sampling. In probability sampling, alternatively knows as random sampling, you start with a complete sample frame of all eligible individuals that have an equal chance to be part of the selected sample. The selection must occur in a 'random' way, meaning that they do not differ in any significant way from observations not sampled. The difference between the two techniques is whether the sample is selected based on randomization or not. There are several different sampling techniques available that can be grouped into two categories as probability sampling, and non-probability sampling. It is important to ensure that the individuals selected are representative of the whole population. Sampling is a method that allows researchers to infer information about a population based on results from a subset of the population. When carrying out a survey, it would be impractical to study a whole population. In this post, I'll explain what random sampling is and the different types of random sampling you might come across and an alternative to the random sampling that you may want to consider.
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