Units: students Population: all undergraduate and postgraduate students, full-time and part-time, at the University of Oxford, England Sampling frame: all students enrolled at the University of Oxford, as of 05 January i. Note the difference between the target population and the sampling frame, from which we select our sample when using a probability sampling technique.

They are the same in all respects apart from the fact that the sampling frame tells the reader that only those students enrolled in the university according to Student Records on a particular date i. If the list of students kept by Student Records is very different from the population of all students studying at the university, this should be made clear [see the article, Sampling: The basics, to understand more about sampling frames and potential sampling bias].

By the time you come to write up the Sampling Strategy section of your Research Strategy chapter, you should know whether the sampling frame is the same as the population. If it is not, you should highlight the difference between the two. This completes the first part of the Sampling Strategy section of your Research Strategy chapter. Once you know what units you are studying, as well as your population and sampling frame , the reader will often want to know what types of sampling technique you could use.

We say could use rather than should use because whilst there are certain ideal choices of sampling technique, there is seldom a right or wrong answer. Instead, researchers choose sampling techniques that they feel are most appropriate to their study, based on theoretical and practical reasons. Broadly speaking, you could choose to select your sample from a your sampling frame using either a probability sampling technique e. To understand the differences between these techniques, as well as their advantages and disadvantages, you may want to start by reading the articles: Probability sampling and Non-probability sampling.

When explaining the types of sampling technique that were available to you in this part of your Sampling Strategy section, you should take into account: a the research strategy guiding your dissertation; and b theoretical and practical sampling issues. Theoretically , the ideal sampling technique for a piece of research i. Whilst there are theoretical ideals when it comes to choosing a sampling technique to use for your dissertation i. Such practical issues range from whether your target population is known i.

Assuming that you understand the differences between these sampling techniques, and their relative merits, let's consider what sampling choices are open to us using our example of career choices of students at the University of Oxford, England. The green text illustrates what we have already written above. Since our research drew on a quantitative research design , the ideal would have been to use a probability sampling technique because this allows us to make statistical inferences i. Such a probability sampling technique would provide greater external validity for our findings.

Since we wanted to compare the career choices of different strata i. However, if it were not possible to use a probability sampling technique , we could have used a non-probability sampling technique. Since we wanted to compare different strata i. Defining the population in terms of these characteristics will help when performing the second step: creating a list of the population. When you create a list of the population that you want to be part of your total population sample, the list should only focus on those people i.

To create a list of the population, you may need to use a gatekeeper to achieve this. For example, if you were interested in the effect of senior manager mentorship on employee motivation in a single firm with employees, you may need the Human Resources Director to act as the gatekeeper to ensure that you had access to the list of all senior managers within the firm.

### Total population sampling

Since you are trying to create a total population sample, you will need to contact all members on the list NOTE: these members are likely to be people, but could also be organisations. There are a number of advantages and disadvantages to using total population sampling. These are discussed in turn below:. Since total population sampling involves all members within the population of interest, it is possible to get deep insights into the phenomenon you are interested in. With such wide coverage of the population of interest, there is also a reduced risk of missing potential insights from members that are not included.

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Whilst total population sampling is a purposive sampling technique i. As with probability sampling techniques that require the researcher to get a list of the population i.

However, as can be learnt from probability sampling, being able to get hold of such a population list can be very time consuming and challenging. Often a list does not exist. It may also be difficult to build a list if the population is geographically dispersed or requires the permission of a gatekeeper not only to get the list, but also to contact members on the list. If the list of the population is incomplete or a large or even small proportion of members choose not to take part in the research, the ability of the total population sample to allow the researcher to make analytical generalisations can be severely compromised.

To learn more about other purposive sampling techniques , see the article: Purposive sampling. Since purposive sampling is just one type of non-probability sampling , see the article: Non-probability sampling. Total population sampling Total population sampling is a type of purposive sampling technique that involves examining the entire population i.

What is total population sampling? Examples of total population sampling Creating a total population sample Advantages and disadvantages of total population sampling. In this respect, there are two aspects of this example that illustrate when total population sampling may be appropriate: The population size is relatively small In total population sampling, researchers choose to study the entire population because the size of the population that has the particular set of characteristics that we are interest in is typically very small.

The population shares an uncommon characteristic s The characteristic shared by the population is considered to be uncommon because this tends to explain why the population that can be studied is very small.

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A population is a complete set of people with a specialized set of characteristics, and a sample is a subset of the population. In medical research, the criteria for population may be clinical, demographic and time related.

Clinical and demographic characteristics define the target population, the large set of people in the world to which the results of the study will be generalized e. The study population is the subset of the target population available for study e. Snowball sample, where one case identifies others of his kind e. Non-random samples have certain limitations. The larger group target population is difficult to identify.

This may not be a limitation when generalization of results is not intended. The results would be valid for the sample itself internal validity. They can, nevertheless, provide important clues for further studies based on random samples.

Another limitation of non-random samples is that statistical inferences such as confidence intervals and tests of significance cannot be estimated from non-random samples. However, in some situations, the investigator has to make crucial judgments. One should remember that random samples are the means but representativeness is the goal.

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When non-random samples are representative compare the socio-demographic characteristics of the sample subjects with the target population , generalization may be possible. A sample may be defined as random if every individual in the population being sampled has an equal likelihood of being included. Random sampling is the basis of all good sampling techniques and disallows any method of selection based on volunteering or the choice of groups of people known to be cooperative.

In order to select a simple random sample from a population, it is first necessary to identify all individuals from whom the selection will be made. This is the sampling frame. In developing countries, listings of all persons living in an area are not usually available. Census may not catch nomadic population groups. Whether or not such deficiencies are major barriers in random sampling depends on the particular research question being investigated.

To undertake a separate exercise of listing the population for the study may be time consuming and tedious. Two-stage sampling may make the task feasible. The usual method of selecting a simple random sample from a listing of individuals is to assign a number to each individual and then select certain numbers by reference to random number tables which are published in standard statistical textbooks. A simple method of random sampling is to select a systematic sample in which every n th person is selected from a list or from other ordering.

A systematic sample can be drawn from a queue of people or from patients ordered according to the time of their attendance at a clinic. Thus, a sample can be drawn without an initial listing of all the subjects. Because of this feasibility, a systematic sample may have some advantage over a simple random sample. To fulfill the statistical criteria for a random sample, a systematic sample should be drawn from subjects who are randomly ordered. The starting point for selection should be randomly chosen. If every fifth person from a register is being chosen, then a random procedure must be used to determine whether the first, second, third, fourth, or fifth person should be chosen as the first member of the sample.

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Sometimes, a strictly random sample may be difficult to obtain and it may be more feasible to draw the required number of subjects in a series of stages. For example, suppose we wish to estimate the number of CATSCAN examinations made of all patients entering a hospital in a given month in the state of Maharashtra. It would be quite tedious to devise a scheme which would allow the total population of patients to be directly sampled. However, it would be easier to list the districts of the state of Maharashtra and randomly draw a sample of these districts.

Within this sample of districts, all the hospitals would then be listed by name, and a random sample of these can be drawn. Within each of these hospitals, a sample of the patients entering in the given month could be chosen randomly for observation and recording. Thus, by stages, we draw the required sample.

It should be cautioned that multistage sampling should only be resorted to when difficulties in simple random sampling are insurmountable. Those who take a simple random sample of 12 hospitals, and within each of these hospitals select a random sample of 10 patients, may believe they have selected patients randomly from all the 12 hospitals. In statistical sense, they have in fact selected a sample of 12 rather than If a condition is unevenly distributed in a population with respect to age, gender, or some other variable, it may be prudent to choose a stratified random sampling method.