What Is Sample And Sampling Techniques Pdf
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- Types of Sampling: Sampling Methods with Examples
- An introduction to sampling methods
- Sampling Techniques
Posted on 18th November by Mohamed Khalifa. This tutorial will introduce sampling methods and potential sampling errors to avoid when conducting medical research.
Home QuestionPro Products Audience. Sampling definition: Sampling is a technique of selecting individual members or a subset of the population to make statistical inferences from them and estimate characteristics of the whole population. Different sampling methods are widely used by researchers in market research so that they do not need to research the entire population to collect actionable insights. It is also a time-convenient and a cost-effective method and hence forms the basis of any research design.
Types of Sampling: Sampling Methods with Examples
Sampling is a process or technique of choosing a sub-group from a population to participate in the study; it is the process of selecting a number of individuals for a study in such a way that the individuals selected represent the large group from which they were selected Ogula, There are two major sampling procedures in research.
These include probability and non probability sampling. In probability sampling, everyone has an equal chance of being selected. This scheme is one in which every unit in the population has a chance greater than zero of being selected in the sample.
There are four basic types of sampling procedures associated with probability samples. These include simple random, systematic sampling, stratified and cluster. Simple random sampling provides the base from which the other more complex sampling methodologies are derived. To conduct a simple random sample, the researcher must first prepare an exhaustive list sampling frame of all members of the population of interest.
From this list, the sample is drawn so that each person or item has an equal chance of being drawn during each selection round Kanupriya, To draw a simple random sample without introducing researcher bias, computerized sampling programs and random number tables are used to impartially select the members of the population to be sampled. Subjects in the population are sampled by a random process, using either a random number generator or a random number table, so that each person remaining in the population has the same probability of being selected for the sample Friedrichs, Systematic sampling procedure often used in place of simple random sampling.
In systematic sampling, the researcher selects every nth member after randomly selecting the first through nth element as the starting point. For example, if the researcher decides to sample 20 repondents from a sample of , every 5th member of the population will systematically be selected. A researcher may choose to conduct a systematic sample instead of a simple random sample for several reasons.
Firstly, systematic samples tend to be easier to draw and execute, secondly, the researcher does not have to go back and forth through the sampling frame to draw the members to be sampled, thirdly, a systematic sample may spread the members selected for measurement more evenly across the entire population than simple random sampling. Therefore, in some cases, systematic sampling may be more representative of the population and more precise Groves et al.
Stratified sampling procedure is the most effective method of sampling when a researcher wants to get a representative sample of a population. An independent simple random sample is then drawn from each group. Stratified sampling techniques can provide more precise estimates if the population being surveyed is more heterogeneous than the categorized groups. This technique can enable the researcher to determine desired levels of sampling precision for each group, and can provide administrative efficiency.
In cluster sampling, a cluster a group of population elements , constitutes the sampling unit, instead of a single element of the population. The sampling in this technique is mainly geographically driven. The sampling frame is also often readily available at cluster level and takes short time for listing and implementation.
The technique is also suitable for survey of institutions Ahmed, or households within a given geographical area. Non probability sampling is used in some situations, where the population may not be well defined. In other situations, there may not be great interest in drawing inferences from the sample to the population. The most common reason for using non probability sampling procedure is that it is less expensive than probability sampling procedure and can often be implemented more quickly Michael, It includes purposive, convenience and quota sampling procedures.
The selection of a purposive sample is often accomplished by applying expert knowledge of the target population to select in a non random manner a sample that represent a cross-section of the population Henry, A major disadvantage of this method is subjectivity since another researcher is likely to come up with a different sample when identifying important characteristics and picking typical elements to be in the sample.
Given the subjectivity of the selection mechanism, purposive sampling is generally considered most appropriate for the selection of small samples often from a limited geographic area or from a restricted population definition. Key informants are also selected using this procedure.
Convenience sampling is sometimes known as opportunity, accidental or haphazard sampling sampling. It is a type of nonprobability sampling which involves the sample being drawn from that part of the population which is close to hand, that is, a population which is readily available and convenient.
The researcher using such a sample cannot scientifically make generalizations about the total population from this sample because it would not be representative enough Michael, This type of sampling is most useful for pilot testing.
Convenience sampling differs from purposive sampling in that expert judgment is not used to select a representative sample. The primary selection criterion relates to the ease of obtaining a sample.
Ahmed, S. Methods in Sample Surveys: Cluster Sampling. Friedrichs, R. Rapid Surveys unpublished. Groves, R. N onresponse in Household Interview Surveys. New York: John Wiley. Responsive design for household surveys: Tools for actively controlling survey errors and costs. Journal of Royal Statististics Soceiety , p.
Kanupriya, C. Sampling methods. Prevention and treatment of item nonresponse. Journal of Official Statistics , 19, — Michael, P. Non probability Sampling. Encyclopedia of survey research Methods. Kenya Projects Organization is a membership organization founded and registered in Kenya in the year The main objective of the organization is to strengthen human and institutional capacities through applying the best practices in project management, research and information technologies.
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Probability Sampling Procedures In probability sampling, everyone has an equal chance of being selected. Simple Random Sampling Procedure Simple random sampling provides the base from which the other more complex sampling methodologies are derived. Systematic Sampling Procedure Systematic sampling procedure often used in place of simple random sampling. Stratified Sampling Procedure Stratified sampling procedure is the most effective method of sampling when a researcher wants to get a representative sample of a population.
Cluster Sampling Procedure In cluster sampling, a cluster a group of population elements , constitutes the sampling unit, instead of a single element of the population. Non Probability Sampling Procedures Non probability sampling is used in some situations, where the population may not be well defined. Anthony M. Hunt, N. Stratified Sampling.
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An introduction to sampling methods
Skip to content Your Produce, Our Responsibility. Snowball b. Those who are available to be surveyed in public places are unlikely to be a representative sample. It is possible to specify the probability of selecting any particular sample of a given size. The number of elements to be included in the sample set can be pre-specified.
some elements are selected from a population, we refer to that as a sample. Types of sampling. 1) Probability sampling methods. 2) Non-.
Sampling is a process or technique of choosing a sub-group from a population to participate in the study; it is the process of selecting a number of individuals for a study in such a way that the individuals selected represent the large group from which they were selected Ogula, There are two major sampling procedures in research. These include probability and non probability sampling. In probability sampling, everyone has an equal chance of being selected.
Quantitative researchers are often interested in being able to make generalizations about groups larger than their study samples. While there are certainly instances when quantitative researchers rely on nonprobability samples e. The goals and techniques associated with probability samples differ from those of nonprobability samples. The reason is that, in most cases, researchers who use probability sampling techniques are aiming to identify a representative sample A sample that resembles the population from which it was drawn in all the ways that are important for the research being conducted. A representative sample is one that resembles the population from which it was drawn in all the ways that are important for the research being conducted.