The tendency when using convenience samples is to treat the results as representative, even though they are not. Over-sized samples A sample is over-sized when there are more units e. This is used when the researcher knows that the population has sub-groups strata that are of interest. Let’s say that the university has roughly 10, students. Say we are interested in knowing what is the average monthly income of all the full-time students at our university. As such possibly there is no other sampling technique which is less time consuming or less costly. For example, maybe the researcher would avoid approaching certain groups e.
These types of probability sampling technique include simple random sampling , systematic random sampling , stratified random sampling and cluster sampling. Unlike probability sampling, the goal is not to achieve objectivity in the selection of samples, or necessarily attempt to make generalisations i. As such possibly there is no other sampling technique which is less time consuming or less costly. Where this happens, it raises ethical issues because the picture being built through the research can be excessively narrow, and arguably, unethically narrow. When we come to describe our population further, we would also need to define what we meant by frequent users e. Similarly the scientists needing to conduct research involving human subjects must invite volunteers. An over-sized sample is considered to be an ethical issue because it potentially exposes an excessive number of people or other units to your research.
You decide to conduct an experiment where you measure concentration levels amongst 40 female students that are not on any specific diet.
We discuss non-probability sampling in more detail in the article, Non-probability sampling. First, you measure their concentration levels. One example of this misrepresentation is if certain magazine subscribers are polled on political opinion, when the magazine appeals to a more liberal sect of the populous.
As such, we would continue to invite students to take part in the research until our sample sampking was reached. The sample size of 60 was decided before the research was carried out; this is known as convenience sampling. In the example above, the sample size selected may be just or of the Facebook users that are part of our population of frequent, male, Facebook users in the United States.
Non Probability sampling & its types
This is used when the researcher knows that the population has sub-groups strata that are of interest. It is also cheaper to administer than a statewide sample sampilng high school seniors, because there are many fewer sites to obtain information from. This is a case where size matters pun intended.
Cluster sampling must use a random sampling method at each stage. This may result in a somewhat larger sample than using a simple random sampling method, but it saves time and money. What if conveninece only needed to ask 10 students to go on the carbohydrate free diet rather than 20?
When thinking about the population you are interested in studying, it is important to be precise.
In our example of the 10, university students, if we were only interested in achieving a sample size of say students, we may simply stand at one of the main entrances to campus, where it would be easy to invite the many students that pass by to take part in the research.
Do I think that participants are taking part voluntarily?
Sampling techniques, sample size and other factors affecting your dissertation | Lærd Dissertation
When we are interested in a population, it is often impractical and sometimes undesirable to try and study the entire population. As a researcher, even when you’re an undergraduate or master’s level student, you have a duty not to expose an excessive number of people to unnecessary distress or harm.
These purposive sampling techniques samplijg maximum variation samplinghomogeneous sampling, typical case samplingextreme or deviant case samplingtotal population sampling and expert sampling. Advantages of Convenience Sampling Simplicity of sampling and ease of research Helpful for pilot studies and for hypothesis dissdrtation Data collection can be facilitated in short duration of time Cost effectiveness Disadvantages of Convenience Sampling Highly vulnerable to selection bias Generalisability unclear High level of sampling error by John Dudovskiy, http: Theoretical reasons Non-probability sampling represents a valuable group of sampling techniques that can be used in research that follows qualitativemixed methodsand even quantitative research designs.
Sample size has little to do with the size of the population, however.
You know that carbohydrate free diets dissertatlon. A more concrete example is choosing five people from a class or choosing the first five names from the list of patients. This is bad from two perspectives, but only one is arguably a potential ethical issue: We also do not know why some employees agreed to take part in the survey, whilst others did not.
If you are considering whether to use non-probability sampling, it is important to consider how your choice of research strategy will influence whether this is an appropriate decision.
Total population sampling | Lærd Dissertation
In sampling, units are the things that make up the population. Non-probability sampling represents a conveniencs of sampling techniques that help researchers to select units from a population that they are interested in studying. For example, to interview a fixed number of shoppers at a mall, half of whom are male and half of whom are female. This enables you to achieve dissertwtion sample size you want in a relatively fast and inexpensive way.
It is the reason that sample statistics only provide estimates of population parameters, but the amount of random error is known. These units can be people, cases e. As a result, researchers following a quantitative research design often feel that they are forced to use non-probability sampling techniques because of some inability to use probability sampling e.
Ease of Research Because researchers are not looking for an accurate sampling, they can simply collect their information and move on to other aspects of their study. Biased Results One of the major drawbacks to this form of sampling is the opportunity for bias to cloud the results of the survey.