File Name: probability and non probability sampling .zip
Sampling can be a confusing concept for managers carrying out survey research projects. By knowing some basic information about survey sampling designs and how they differ, you can understand the advantages and disadvantages of various approaches. The big difference is that in probability sampling all persons have a chance of being selected, and results are more likely to accurately reflect the entire population.
A sample is a subset of a population and we survey the units from the sample with the aim to learn about the entire population. However, the sampling theory was basically developed for probability sampling , where all units in the population have known and positive probabilities of inclusion. This definition implicitly involves randomization , which is a process resembling lottery drawing, where the units are selected according to their inclusion probabilities. In probability sampling the randomized selection is used instead of arbitrary or purposive sample selection of the researcher, or, instead of various self-selection processes run by respondents. Within this context, the notion of non-probability Show page numbers Download PDF.
Non-probability sampling represents a group of sampling techniques that help researchers to select units from a population that they are interested in studying. Collectively, these units form the sample that the researcher studies [see our article, Sampling: The basics , to learn more about terms such as unit , sample and population ]. A core characteristic of non-probability sampling techniques is that samples are selected based on the subjective judgement of the researcher, rather than random selection i. Whilst some researchers may view non-probabilit y sampling techniques as inferior to probability sampling techniques, there are strong theoretical and practical reasons for their use. This article discusses the principles of non-probability sampling and briefly sets out the types of non-probability sampling technique discussed in detail in other articles within this site. The article is divided into two sections: principles of non-probability sampling and types of non-probability sampling :. There are theoretical and practical reasons for using non-probability sampling.
Sampling is the use of a subset of the population to represent the whole population or to inform about social processes that are meaningful beyond the particular cases, individuals or sites studied. Probability sampling, or random sampling , is a sampling technique in which the probability of getting any particular sample may be calculated. Nonprobability sampling does not meet this criterion. Nonprobability sampling techniques are not intended to be used to infer from the sample to the general population in statistical terms. Instead, for example, grounded theory can be produced through iterative nonprobability sampling until theoretical saturation is reached Strauss and Corbin, Thus, one cannot say the same on the basis of a nonprobability sample than on the basis of a probability sample. The grounds for drawing generalizations e.
Surveys of people's opinions are fraught with difficulties. It is easier to obtain information from those who respond to text messages or to emails than to attempt to obtain a representative sample. Samples of the population that are selected non-randomly in this way are termed convenience samples as they are easy to recruit. This introduces a sampling bias. Such non-probability samples have merit in many situations, but an epidemiological enquiry is of little value unless a random sample is obtained. If a sufficient number of those selected actually complete a survey, the results are likely to be representative of the population. This editorial describes probability and non-probability sampling methods and illustrates the difficulties and suggested solutions in performing accurate epidemiological research.
The difference between nonprobability and probability sampling is that nonprobability sampling does not involve random selection and probability sampling does. Not necessarily. But it does mean that nonprobability samples cannot depend upon the rationale of probability theory. At least with a probabilistic sample, we know the odds or probability that we have represented the population well. We are able to estimate confidence intervals for the statistic.
A sample is a subset, or smaller group, within a population. When designing studies, researchers must ensure that the sample replicates the larger population in all the characteristic ways that could be important to the study's research findings. Some samples so closely represent the larger population that it's easy to make inferences about the larger population from your observations of the sample group. In market research, there are two general approaches to sampling: probability sampling and nonprobability sampling. Generally, nonprobability sampling is a bit rough, with a biased and subjective process. This sampling is used to generate a hypothesis. Conversely, probability sampling is more precise, objective and unbiased, which makes it a good fit for testing a hypothesis.
Published on September 19, by Shona McCombes. Revised on February 25, Instead, you select a sample. The sample is the group of individuals who will actually participate in the research. To draw valid conclusions from your results, you have to carefully decide how you will select a sample that is representative of the group as a whole.
In non-probability sampling also known as non-random sampling not all members of the population has a chance of participating in the study. This is contrary to probability sampling , where each member of the population has a known, non-zero chance of being selected to participate in the study. In these cases, sample group members have to be selected on the basis of accessibility or personal judgment of the researcher. Therefore, the majority of non-probability sampling techniques include an element of subjective judgement. Non-probability sampling is the most helpful for exploratory stages of studies such as a pilot survey.
Home QuestionPro Products Audience. Definition: Non-probability sampling is defined as a sampling technique in which the researcher selects samples based on the subjective judgment of the researcher rather than random selection. It is a less stringent method. This sampling method depends heavily on the expertise of the researchers. It is carried out by observation, and researchers use it widely for qualitative research.
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