There are two types of probability samples: The term random has a very precise meaning. Each individual in the population of interest has an equal likelihood of selection. This is a very strict meaning -- you can't just collect responses on the street and have a random sample. The assumption of an equal chance of selection means that sources such as a telephone book or voter registration lists are not adequate for providing a random sample of a community.
In both these cases there will be a number of residents whose names are not listed. Telephone surveys get around this problem by random-digit dialing -- but that assumes that everyone in the population has a telephone. The key to random selection is that there is no bias involved in the selection of the sample. Any variation between the sample characteristics and the population characteristics is only a matter of chance.
A stratified sample is a mini-reproduction of the population. Before sampling, the population is divided into characteristics of importance for the research. For example, by gender, social class, education level, religion, etc.
Then the population is randomly sampled within each category or stratum. How to Construct a probability representative sample. As they are not truly representative, non-probability samples are less desirable than probability samples. However, a researcher may not be able to obtain a random or stratified sample, or it may be too expensive.
A researcher may not care about generalizing to a larger population. The validity of non-probability samples can be increased by trying to approximate random selection, and by eliminating as many sources of bias as possible. A researcher is interested in the attitudes of members of different religions towards the death penalty.
In Iowa a random sample might miss Muslims because there are not many in that state. However, the sample will no longer be representative of the actual proportions in the population. This may limit generalizing to the state population. But the quota will guarantee that the views of Muslims are represented in the survey. A subset of a purposive sample is a snowball sample -- so named because one picks up the sample along the way, analogous to a snowball accumulating snow. This is in stark contrast to probability sampling techniques , where the selection of units is made randomly.
In our example of the 10, university students, we were only interested in achieving a sample size of students who would take part in our research. As such, we would continue to invite students to take part in the research until our sample size was reached. Since the aim of convenience sampling is easy access, we may simply choose to stand at one of the main entrances to campus of the University of Bath where it would be easy to invite the many students that pass by to take part in the research.
Convenience sampling is vey easy to carry out with few rules governing how the sample should be collected. The relative cost and time required to carry out a convenience sample are small in comparison to probability sampling techniques. This enables you to achieve the sample size you want in a relatively fast and inexpensive way. The convenience sample may help you gathering useful data and information that would not have been possible using probability sampling techniques, which require more formal access to lists of populations [see, for example, the article on simple random sampling ].
For example, imagine you were interested in understand more about employee satisfaction in a single, large organisation in the US. You intended to collect your data using a survey. The manager who has kindly given you access to conduct your research is unable to get permission to get a list of all employees in the organisation, which you would need to use a probability sampling technique such as simple random sampling or systematic random sampling.
However, the manager has managed to secure permission for you to spend two days in the organisation to collect as many survey responses as possible. You decide to spend the two days at the entrance of the organisation where all employees have to pass through to get to their desks.
Whilst a probability sampling technique would have been preferred, the convenience sample was the only sampling technique that you could use to collect data. Irrespective of the disadvantages limitations of convenience sampling, discussed below, without the use of this sampling technique, you may not have been able to get access to any data on employee satisfaction in the organisation.
The convenience sample often suffers from biases from a number of biases. This can be seen in both of our examples, whether the 10, students we were studying, or the employees at the large organisation. In both cases, a convenience sample can lead to the under-representation or over-representation of particular groups within the sample. If we take the large organisation: It may be that the organisation has multiple sites, with employee satisfaction varying considerably between these sites.
By conducting the survey at the headquarters of the organisation, we may have missed the differences in employee satisfaction amongst non-office workers.
Convenience sampling (also known as availability sampling) is a specific type of non-probability sampling method that relies on data collection from population members who are conveniently available to participate in study. Facebook polls or questions can be mentioned as .
Researchers use convenience sampling not just because it is easy to use, but because it also has other research advantages. In pilot studies, convenience sample is usually used because it allows the researcher to obtain basic data and trends regarding his study without the complications of using a randomized sample.
Convenience sampling is a type of nonprobability sampling in which people are sampled simply because they are "convenient" sources of data for researchers. In probability sampling, each element in the population has a known nonzero chance of being selected through the use of a . A convenience sample is a non-probability sample in which the researcher uses the subjects that are nearest and available to participate in the research study. This technique is also referred to as "accidental sampling," and is commonly used in pilot studies prior to launching a larger research project.
Convenience sampling. Convenience sampling is a type of non-probability sampling technique. Non-probability sampling focuses on sampling techniques that are based on the judgement of the researcher [see our article Non-probability sampling to learn more about non-probability sampling]. 2 Ilker Etikan et al.: Comparison of Convenience Sampling and Purposive Sampling. include every subject because the population is almost finite. This is the rationale behind using sampling techniques like convenience sampling by most researchers .