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Sampling (statistics)

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The Purpose of Sampling

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1. Random Sampling
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A sample is the group of people who take part in the investigation. Generalisability refers to the extent to which we can apply the findings of our research to the target population we are interested in. In psychological research we are interested in learning about large groups of people who all have something in common.

We call the group that we are interested in studying our 'target population'. In some types of research the target population might be as broad as all humans, but in other types of research the target population might be a smaller group such as teenagers, pre-school children or people who misuse drugs.

It is more or less impossible to study every single person in a target population so psychologists select a sample or sub-group of the population that is likely to be representative of the target population we are interested in. If the sample we select is going to represent the target population then we need to make sure that the people in it are similar to the other members of the target population.

This is important because we want to generalize from the sample to target population. The participants in research, the sample, should be as representative as possible of the target population.

The more representative the sample, the more confident the researcher can be that the results can be generalized to the target population. One of the problems that can occur when selecting a sample from a target population is sampling bias. Sampling bias refers to situations where the sample does not reflect the characteristics of the target population. Many psychology studies have a biased sample because they have used an opportunity sample that comprises university students as their participants e.

But who are you going to try it out on and how will you select your participants? There are various sampling methods. The one chosen will depend on a number of factors such as time, money etc. This is similar to the national lottery. Random samples require a way of naming or numbering the target population and then using some type of raffle method to choose those to make up the sample.

Random samples are the best method of selecting your sample from the population of interest. The advantages are that your sample should represent the target population and eliminate sampling bias, but the disadvantage is that it is very difficult to achieve i. The researcher identifies the different types of people that make up the target population and works out the proportions needed for the sample to be representative.

A list is made of each variable e. For example, if we are interested in the money spent on books by undergraduates, then the main subject studied may be an important variable.

For example, students studying English Literature may spend more money on books than engineering students so if we use a very large percentage of English students or engineering students then our results will not be accurate.

In many experiments, sampling an entire population as part of a research experiment is impossible, due to the time, expense and sheer number of subjects.

Imagine, for example, an experiment to test the effects of a new education technique on schoolchildren. It would be impossible to select the entire school age population of a country, divide them into groups and perform research.

A research group sampling the diversity of flowers in the African savannah could not count every single flower, because it would take many years. This is where statistical sampling comes in, the idea of trying to take a representative section of the population, perform the experiment and extrapolate it back to the population as a whole. In the education example, the research group could test all of the schools in a city, or select one school in a few different cities.

Of course, the process is not that easy, and the researchers must use a battery of statistical techniques, and a good research design , to ensure that this subset is as representative as possible.

Failure to take into account all of the various experimental biases and errors that can creep into an experiment, if the sample group is chosen poorly, will inevitably lead to invalid results. The basic question that a researcher should be asking when selecting a sample group is:.

For example, if an opinion poll company canvasses opinion by phoning people between 9am and 5pm, they are going to miss most people who are out working, totally invalidating their results.

These are called determining factors, and also include poor experiment design , confounding variables and human error.

When sampling, a researcher has two distinct choices:. For example, a study that needs to ask for volunteers is never representative of a population. In such cases, the researcher needs to be aware that they cannot extrapolate the findings to represent an entire population. A study into heart disease that only looks at middle aged men, between 40 and 60, will say very little about heart disease in women or younger men, although it can always be a basis for future research involving other groups.

However robust the research design , there is always an inherent inaccuracy with any sample-based experiment, due to chance fluctuations and natural variety. Most statistical tests take this into account, and this is why results are judged to a significance level , or given a margin of error. Sampling is an essential part of most research, and researchers must know how to choose sample groups that are as free from bias as possible, and also be aware of the extent to which they can extrapolate their results back to the general population.

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The Advantages of Sampling

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Sampling and types of sampling methods commonly used in quantitative research are discussed in the following module. Learning Objectives: Define sampling and randomization. Explain probability and non-probability sampling and describes the different types of each.

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The sample of a study is the group of subjects in the study. Sampling is the process whereby a researcher chooses his or her sample. The five steps to sampling are: Identify the population. Specify a sampling frame. Specify a sampling method. Determine the sample size. Implement the plan.

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Choosing a sampling method. There are many methods of sampling when doing research. This guide can help you choose which method to use. Simple random sampling is the ideal, but researchers seldom have the luxury of time or money to access the whole population, so many compromises often have to be made. Sampling is the process of selecting units (e.g., people, organizations) from a population of interest so that by studying the sample we may fairly generalize our results back to the population from which they were chosen.

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This was a presentation that was carried out in our research method class by our group. It will be useful for PHD and master students quantitative and qualitat. How to do sampling for qual and quant research designs Slideshare uses cookies to improve functionality and performance, and to provide you with relevant advertising. If you continue browsing the site, you agree to the use of cookies on this website.