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Published: 12.06.2021  When to use it. Ensures a high degree of representativeness, and no need to use a table of random numbers.

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Probability sampling: Definition, types, examples, steps and advantages

Simple random sampling is a type of probability sampling technique [see our article, Probability sampling , if you do not know what probability sampling is]. 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 ].

This article a explains what simple random sampling is, b how to create a simple random sample, and c the advantages and disadvantages of simple random sampling. Imagine that a researcher wants to understand more about the career goals of students at a single university. Let's say that the university has roughly 10, students. These 10, students are our population N. Each of the 10, students is known as a unit although sometimes other terms are used to describe a unit; see Sampling: The basics.

In order to select a sample n of students from this population of 10, students, we could choose to use a simple random sample.

With simple random sampling, there would an equal chance probability that each of the 10, students could be selected for inclusion in our sample. If our desired sample size was around students, each of these students would subsequently be sent a questionnaire to complete imagining we choose to collect our data using a questionnaire.

To create a simple random sample, there are six steps : a defining the population; b choosing your sample size; c listing the population; d assigning numbers to the units; e finding random numbers; and f selecting your sample. In our example, the population is the 10, students at the single university.

The population is expressed as N. Since we are interested in all of these university students, we can say that our sampling frame is all 10, students. If we were only interested in female university students, for example, we would exclude all males in creating our sampling frame, which would be much less than 10, students. Let's imagine that we choose a sample size of students. The sample is expressed as n. This number was chosen because it reflects the limit of our budget and the time we have to distribute our questionnaire to students.

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. This is a major advantage because such generalisations are more likely to be considered to have external validity.

A simple random sample can only be carried out if the list of the population is available and complete. Even if a list is readily available, it may be challenging to gain access to that list.

The list may be protected by privacy policies or require a lengthy process to attain permissions. There may be no single list detailing the population you are interested in.

As a result, it may be difficult and time consuming to bring together numerous sub-lists to create a final list from which you want to select your sample. As an undergraduate and master? Many lists will not be in the public domain and their purchase may be expensive; at least in terms of the research funds of a typical undergraduate or master's level dissertation student.

In terms of human populations as opposed to other types of populations; see the article: Sampling: The basics , some of these populations will be expensive and time consuming to contact, even where a list is available.

Assuming that your list has all the contact details of potential participants in the first instance, managing the different ways e. In the case of human populations, to avoid potential bias in your sample, you will also need to try and ensure that an adequate proportion of your sample takes part in the research.

This may require re-contacting non-respondents, can be very time consuming, or reaching out to new respondents. If you are an undergraduate or master's level dissertation student considering using simple random sampling , you may also want to read more about how to put together your sampling strategy [see the section: Sampling Strategy ].

Simple random sampling Simple random sampling is a type of probability sampling technique [see our article, Probability sampling , if you do not know what probability sampling is]. Simple random sampling explained Creating a simple random sample Advantages and disadvantages of simple random sampling. Simple random sampling explained Imagine that a researcher wants to understand more about the career goals of students at a single university.

Creating a simple random sample To create a simple random sample, there are six steps : a defining the population; b choosing your sample size; c listing the population; d assigning numbers to the units; e finding random numbers; and f selecting your sample. Imagine the first three numbers from the random number table were: the 11 th student from the numbered list of 10, students the 9, nd student from the list the 2, st student from the list We would select the 11 th , 9, nd and 2, st students from our list to be part of the sample.

Advantages and disadvantages of simple random sampling The advantages and disadvantages of simple random sampling are explained below. Advantages of simple random sampling 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.

Disadvantages of simple random sampling A simple random sample can only be carried out if the list of the population is available and complete. Attaining a complete list of the population can be difficult for a number of reasons: Even if a list is readily available, it may be challenging to gain access to that list. Sampling Methods

Simple random sampling is a type of probability sampling technique [see our article, Probability sampling , if you do not know what probability sampling is]. 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 ]. This article a explains what simple random sampling is, b how to create a simple random sample, and c the advantages and disadvantages of simple random sampling. Imagine that a researcher wants to understand more about the career goals of students at a single university. Let's say that the university has roughly 10, students. These 10, students are our population N.

By Dr. Saul McLeod , updated 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. This is important because we want to generalize from the sample to target population.

Home QuestionPro Products Audience. Definition: Probability sampling is defined as a sampling technique in which the researcher chooses samples from a larger population using a method based on the theory of probability. Select your respondents. The most critical requirement of probability sampling is that everyone in your population has a known and equal chance of getting selected. For example, if you have a population of people, every person would have odds of 1 in for getting selected. Probability sampling gives you the best chance to create a sample that is truly representative of the population. Type of Sampling

It is a herculean task to collect the exact data by assessing the views of all the million audience. So, we go to the stadium and assign random numbers to each person in the audience. We then choose a person from each of the rows who has the highest value among the random numbers assigned to the persons in the same row. This way, we choose the samples and ask them about their views to get an unbiased analysis of what the audience thinks in general.

Simple random sampling means that every member of the sample is selected from the group of population in such a manner that the probability of being selected for all members in the study group of population is the same. Image: Simple random sampling. In other words, sampling units are selected at random so that the opportunity of every sampling unit being included in the sample is the same. This is the basic method of sampling. In this method, numbers are assigned to every member in the study group of population.

Simple random sampling occurs when a subset of a statistical population allows for each member of the demographic to have an equal opportunity of being chosen for surveys, polls, or research projects. The goal of collecting information in this way is to provide an unbiased representation of the entire group. Investopedia uses the example of a simple random sample as having the names of 25 employees being chosen out of a hat from a company of workers.

The goal of random sampling is simple. It helps researchers avoid an unconscious bias they may have that would be reflected in the data they are collecting.