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What is Sampling?

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Sampling: The Basics
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Simple random sampling

However, we could have also determined the sample size we needed using a sample size calculation , which is a particularly useful statistical tool. This may have suggested that we needed a larger sample size; perhaps as many as students.

To select a sample of students, we need to identify all 10, students at the university. If you were actually carrying out this research, you would most likely have had to receive permission from Student Records or another department in the university to view a list of all students studying at the university.

You can read about this later in the article under Disadvantages of simple random sampling. We now need to assign a consecutive number from 1 to N , next to each of the students. In our case, this would mean assigning a consecutive number from 1 to 10, i. Next, we need a list of random numbers before we can select the sample of students from the total list of 10, students. These random numbers can either be found using random number tables or a computer program that generates these numbers for you.

Finally, we select which of the 10, students will be invited to take part in the research. In this case, this would mean selecting random numbers from the random number table. Imagine the first three numbers from the random number table were:. We would select the 11 th , 9, nd and 2, st students from our list to be part of the sample.

We keep doing this until we have all students that we want in our sample. The advantages and disadvantages of simple random sampling are explained below. Many of these are similar to other types of probability sampling technique, but with some exceptions.

Whilst simple random sampling is one of the 'gold standards' of sampling techniques, it presents many challenges for students conducting dissertation research at the undergraduate and master's level. The aim of the simple random sample is to reduce the potential for human bias in the selection of cases to be included in the sample. As a result, the simple random sample provides us with a sample that is highly representative of the population being studied, assuming that there is limited missing data.

Since the units selected for inclusion in the sample are chosen using probabilistic methods , simple random sampling allows us to make generalisations i. Perhaps our population is not Facebook users , but frequent, male Facebook users in the United States. When we come to describe our population further, we would also need to define what we meant by frequent users e.

As discussed above, the population that you are interested consists of units , which can be people , cases or pieces of data. These terms can sometimes be used interchangeably. In this website, we use the word units whenever we are referring to those things that make up a population. However, since you may find other textbooks referring to these units as people, cases, or pieces of data, we have provided some further clarification below:. The population you are interested in consists of one or more units.

For example, if the population we were interested in was all million or more Facebook users, each of these Facebook users would be a unit. So we would have million or more units in our population.

Sometimes the word units is replaced with the word cases. As highlighted in the population examples above, sometimes the populations we are interested in are organisations, institutions and countries.

In such cases, it is often more appropriate to refer to each of these e. You may be interested in a population that consists of only one case e. Finally, researchers sometimes refer to populations consisting of data or pieces of data instead of units or cases. For example, researchers may be interested in customer transactions at a particular supermarket e.

When we are interested in a population, it is often impractical and sometimes undesirable to try and study the entire population. For example, if the population we were interested in was frequent, male Facebook users in the United States , this could be millions of users i.

If we chose to study these Facebook users using structured interviews i. Therefore, we choose to study just a sample of these Facebook users. Whilst we discuss more about sampling and why we sample later in this article, the important point to remember here is that a sample consists of only those units in this case, Facebook users from our population of interest i.

The sample size is simply the number of units in your sample. 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. As a result, sample size calculations are sometimes performed to determine how large your sample size needs to be to avoid such problems. However, these calculations can be complex, and are typically not performed at the undergraduate and master?

The sampling frame is very similar to the population you are studying, and may be exactly the same. When selecting units from the population to be included in your sample, it is sometimes desirable to get hold of a list of the population from which you select units. This is the case when using certain types of sampling technique i.

This list can be referred to as the sampling frame. We explain more about sampling frames in the article: Students in those preschools could then be selected at random through a systematic method to participate in the study. This does, however, lead to a discussion of biases in research. For example, low-income children may be less likely to be enrolled in preschool and therefore, may be excluded from the study.

Extra care has to be taken to control biases when determining sampling techniques. There are two main types of sampling: The difference between the two types is whether or not the sampling selection involves randomization.

Randomization occurs when all members of the sampling frame have an equal opportunity of being selected for the study. Following is a discussion of probability and non-probability sampling and the different types of each. Probability Sampling — Uses randomization and takes steps to ensure all members of a population have a chance of being selected. There are several variations on this type of sampling and following is a list of ways probability sampling may occur:. Non-probability Sampling — Does not rely on the use of randomization techniques to select members.

This is typically done in studies where randomization is not possible in order to obtain a representative sample. Bias is more of a concern with this type of sampling. The different types of non-probability sampling are as follows:. The following Slideshare presentation, Sampling in Quantitative and Qualitative Research — A practical how to, offers an overview of sampling methods for quantitative research and contrasts them with qualitative method for further understanding.

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A sampling frame is the group of people from which you will draw your sample. For example, Brooke might decide that her sampling frame is every student at the university where she works. For example, Brooke might decide that her sampling frame is every student at the university where she works.

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How to do sampling for qual and quant research designs Sampling Methods in Qualitative and Quantitative Research 1. Sampling in Qualitative and Quantitative Research A practical how-to 2. Key themes• A famous sampling mistake• Quantitative assumptions in sampling• Qualitative assumptions in sampling• Types of sampling.

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Good sampling is time-consuming and expensive. Not all experimenters have the time or funds to use more accurate methods. There is a price, of course, in the potential limited validity of results. The following Slideshare presentation, Sampling in Quantitative and Qualitative Research – A practical how to, offers an overview of sampling methods for quantitative research and contrasts them with qualitative method for further understanding.

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What is Sampling? 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 . Sampling: The Basics. Sampling is an important component of any piece of research because of the significant impact that it can have on the quality of your results/ you are new to sampling, there are a number of key terms and basic principles that act as a foundation to the subject. This article explains these key terms and basic principles.