A suitable sample that meets the inclusion and exclusion criteria of a research design must be chosen from a given population to carry out studies. Sampling is a technique aiming to reduce the number of respondents in a piece of research, whilst retaining – as accurately as possible – the characteristics of the whole group.
The purpose of taking a sample is to investigate features of the population in greater detail than could be done if the total population was used, and to draw inferences about this population. The population on which the researcher is interested in carrying out his or her research may be too large, therefore a suitable sample which can represent the population in correct proportion must be chosen. Restraints such as limitation of time, resources and many other factors necessitate the selection of a sample for research purpose so that better quality data is obtained from it and that the researcher can make statement about it.
Sampling Carrying out a survey of every single potential consumer (known as population) of a firm’s product would be impractical, time-consuming and costly. Businesses still, however, need to collect enough primary data to have a clear idea of the views of consumers. This can be done by taking a sample of the population. This sample group should be made up of consumers that are representative of all potential buyers of the product. There are a number of ways in which… Below are some of the sampling techniques that can be used by a researcher to come up with a report on contrition of small scale businesses in fighting poverty.
- Simple random sampling. The simple random sampling is one of the most widely-used random sampling method. The term “random” here does not mean a haphazard selection as many people think. The “random” in this method means each member of the population has equal opportunities being chosen be subject and no one in the identified population who could not be selected in this method. For example, the teacher wants to choose 5 people in QTB class to stand up and introduce themselves. In order to perform random sampling, each member has to have a specific number as an ID, and those number are put in a random
- Stratified Random Sampling Stratified random sampling is commonly done in quantitative researches. When the samples reflect the characteristics of the target population in the same proportion; assumptions can be made on generalizing the data acquired from these samples provided it has been done correctly, since it is statistically representative (Sim,J and wright,C.,2000) there fore this samling method provides a stratified sample that gives greater precision than the simple random sampling of the same population, it also which make stratified random sampling be better to use a small group to save time and money. Another advantage of stratified random sampling is highly representative of the population being studied
- Cluster sampling Technique. McDaniel and Gates (2008:343) as well as Malhotra (2004:328) agree that cluster sampling is when one selects a sub-group of a targeted population that is divided into mutually exclusive and collectively exhaustive groups called clusters. After the clusters have been identified the clusters are selected in two ways. The samples can either be selected probabilistically which means that not all of the clusters will be included in the survey process or all of the clusters will feature in the survey procedure
- Multi stage sampling Technique. Multi-stage sampling (also known as multi-stage cluster sampling is a more complex form of cluster sampling which contains two or more stages in sample selection. In simple terms, in multi-stage sampling large clusters of population are divided into smaller clusters in several stages in order to make primary data collection more manageable. It has to be acknowledged that multi-stage sampling is not as effective as true random sampling; however, it addresses certain disadvantages associated with true random sampling such as being overly expensive and time-consuming application Choose households from each district using simple random or systematic sampling methods. This will result in 120 households to be included in your sample group.
- Systematic Sampling Technique. A method of choosing a random sample from among a larger population. The process of systematic sampling typically involves first selecting a fixed starting point in the larger population and then obtaining subsequent observations by using a constant interval between samples taken. Hence, if the total population was 1,000, a random systematic sampling of 100 data points within that population would involve observing every 10th data point.
