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3.5 Simple Random Sampling and Other Sampling Methods

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## This article is a part of the guide:

❶Simple Random Sampling Lest there be any doubt, we stress that a random sample is not a sample taken in a haphazard way!
## Advantages of Simple Random Sampling

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Researchers can create a simple random sample using a couple of methods. With a lottery method, each member of the population is assigned a number, after which numbers are selected at random. The example in which the names of 25 employees out of are chosen out of a hat is an example of the lottery method at work. Each of the employees would be assigned a number between 1 and , after which 25 of those numbers would be chosen at random.

For larger populations, a manual lottery method can be quite onerous. Selecting a random sample from a large population usually requires a computer-generated process, by which the same methodology as the lottery method is used, only the number assignments and subsequent selections are performed by computers, not humans. Ease of use represents the biggest advantage of simple random sampling. Unlike more complicated sampling methods such as stratified random sampling and probability sampling, no need exists to divide the population into subpopulations or take any other additional steps before selecting members of the population at random.

It is considered a fair way to select a sample from a larger population, since every member of the population has an equal chance of getting selected. A sampling error can occur with a simple random sample if the sample does not end up accurately reflecting the population it is supposed to represent. For example, in our simple random sample of 25 employees, it would be possible to draw 25 men even if the population consisted of women and men.

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. Since the people who have landline phone service tend to be older than people who have cell phone service only, another potential source of bias is introduced. National polling organizations that use random digit dialing in conducting interviewer based polls are very careful to match the number of landline versus cell phones to the population they are trying to survey.

The following sampling methods that are listed in your text are types of non-probability sampling that should be avoided:. Since such non-probability sampling methods are based on human choice rather than random selection, statistical theory cannot explain how they might behave and potential sources of bias are rampant. In your textbook, the two types of non-probability samples listed above are called "sampling disasters.

The article provides great insight into how major polls are conducted. When you are finished reading this article you may want to go to the Gallup Poll Web site, https: It is important to be mindful of margin or error as discussed in this article. We all need to remember that public opinion on a given topic cannot be appropriately measured with one question that is only asked on one poll.

Such results only provide a snapshot at that moment under certain conditions. The concept of repeating procedures over different conditions and times leads to more valuable and durable results. Within this section of the Gallup article, there is also an error: In 5 of those surveys, the confidence interval would not contain the population percent. Eberly College of Science. Printer-friendly version Sampling Methods can be classified into one of two categories: Sample has a known probability of being selected Non-probability Sampling: Sample does not have known probability of being selected as in convenience or voluntary response surveys Probability Sampling In probability sampling it is possible to both determine which sampling units belong to which sample and the probability that each sample will be selected.

Simple Random Sampling SRS Stratified Sampling Cluster Sampling Systematic Sampling Multistage Sampling in which some of the methods above are combined in stages Of the five methods listed above, students have the most trouble distinguishing between stratified sampling and cluster sampling.

With stratified sampling one should: With cluster sampling one should divide the population into groups clusters. Stratified sampling would be preferred over cluster sampling, particularly if the questions of interest are affected by time zone. For example the percentage of people watching a live sporting event on television might be highly affected by the time zone they are in.

Simple random sampling (also referred to as random sampling) is the purest and the most straightforward probability sampling strategy. It is also the most popular method for choosing a sample among population for a wide range of purposes. In simple random sampling each .

Random sampling is one of the most popular types of random or probability sampling. Home; Research. Random sampling is one of the most popular types of random or probability sampling. There are many methods to proceed with simple random sampling. The most primitive and mechanical would be the lottery method.

Simple random sampling is the most basic and common type of sampling method used in quantitative social science research and in scientific research generally. The main benefit of the simple random sample is that each member of the population has an equal chance of being chosen for the study. What is 'Simple Random Sample' A simple random sample is a subset of a statistical population in which each member of the subset has an equal probability of being chosen. A simple random sample is.

Simple random sampling is a sampling technique where every item in the population has an even chance and likelihood of being selected in the sample. Here the selection of items completely depends on chance or by probability and therefore this sampling technique is also . With the simple random sample, there is an equal chance (probability) of selecting each unit from the population being studied when creating your sample [see our article, Sampling: The basics, if you are unsure about the terms unit, sample and population].