Technically Speaking: Why We Use Random Sampling In Reading Research
During studies investigating reading interventions, researchers use random sampling to obtain samples that represent a wide reach of students or classrooms.
Editors note: This blog post is the first in an ongoing series entitled Technically Speaking. In these posts, we write in a way that is understandable about very technical principles that we use in reading research. We want to improve busy practitioners and family members abilities to be good consumers of reading research and to deepen their understanding of how our research operates to provide the best information.
When conducting a study that attempts to measure the effectiveness of a reading intervention on student outcomes, there are two important goals of the researcher and the education stakeholders:
Two essential principles related to the studys design that help toward reaching those goals are random sampling and random assignment. In this part one post, we will focus on random sampling, which helps accomplish the first goal. A part two post on random assignment, which helps accomplish the second goal, will come soon after.
Advantages And Disadvantages Of Random Sampling
In this technique, each member of the population has the same probability of being selected as a subject. The whole process of sampling is done in one step, where each subject is selected independently of the other members of the population .
Random sampling can only be applied in many methods. The most primitive and mechanical would be the lottery. Each member of the population is assigned a number. All numbers are placed in a container or a hat and mixed. Blindfolded, the researcher takes out the labels with numbers. All individuals who have the numbers drawn by the researcher are the subjects of the study. Another way would be for a computer to randomly select the population. In the case of populations with few members, it is advisable to use the first method, but if the population has many members, a random selection by computer is preferable.
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Random assignment refers to the method you use to place participants into groups in an experimental study. For example, say you are conducting a study comparing the blood pressure of patients after taking aspirin or a placebo. You have two groups of patients to compare: patients who will take aspirin and patients who will take the placebo . Ideally, you would want to randomly assign the participants to be in the experimental group or the control group, meaning that each participant has an equal probability of being placed in the experimental or control group. This helps ensure that there are no systematic differences between the groups before the treatment is given to the participants. Random assignment is a fundamental part of a true experiment because it helps ensure that any differences found between the groups are attributable to the treatment, rather than a confounding variable.
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Why Do Researchers Use Random Selection
What is the reason that researchers choose to use random selection when conducting research?
Some key reasons include:
- Random selection is one way to help improve the generalizability of the results. Because the sample is drawn from a larger population, the researchers want to be sure that the sample they are using in their study accurately reflects the characteristics of the larger group. The more representative the sample is, the better able the researchers are able to generalize the results of their experiment to a larger population. By randomly selecting participants for a study, researchers can also help minimize the possibility of bias influencing the results.
- Random selection helps ensure that anomalies will not skew results. By randomly selecting participants for a study, researchers are less likely to draw on subjects that may share unusual characteristics in common. For example, if researchers were interested in learning how many people in the general population are left-handed, the results might be skewed if subject were inadvertently drawn from a group that included an unusually high number of left-handed individuals. Using random selection ensures that the group better represents what exists in the real-world.
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In this technique, each member of the population has an equal chance of being selected as subject. The entire process of sampling is done in a single step with each subject selected independently of the other members of the population.
There are many methods to proceed with simple random sampling. The most primitive and mechanical would be the lottery method. Each member of the population is assigned a unique number. Each number is placed in a bowl or a hat and mixed thoroughly. The blind-folded researcher then picks numbered tags from the hat. All the individuals bearing the numbers picked by the researcher are the subjects for the study. Another way would be to let a computer do a random selection from your population. For populations with a small number of members, it is advisable to use the first method but if the population has many members, a computer-aided random selection is preferred.
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The Importance Of Random Sampling
Let’s say I want to create a study. My hypothesis is that people at the mall have more money than people in the park. So I create a study design: I will go to the mall and ask 100 people how much money they have, and then I will go to the park and ask 100 people how much money they have. I will then compare the two averages.
I go to the mall and I pick 100 people shopping in a high-end watch store. After I’m done at the mall, I go to the park and pick 100 people who are having picnics. Just by my sampling, this study is already inherently flawed. I chose to pick people shopping in a high-end watch store, so it is likely that these people would have more money. My sample does not accurately reflect the mall’s population as a whole.
With this kind of sampling, I can’t really answer the question of whether the people in the mall have higher incomes. I didnt really sample the mall’s population. Random sampling helps fix this problem.
When doing a research study, there are different ways you can recruit participants. If your participants are chosen at random, this means that all members of the population had the same chance of getting chosen to participate in the study. This type of sampling method is considered to be an unbiased sampling method, which is helpful in research because it helps limit outcomes which dont truly reflect the population.
A hypothesis is an educated guess created before the initiation of a study.
S For Stratified Sampling
Define your population of interest and choose the characteristic that you will use to divide your groups. Divide your sample into strata depending on the relevant characteristic. Each strata must be mutually exclusive, but together, they must represent the entire population. Define the sample size for each stratum and decide whether your sample will be proportionate or disproportionate. The size of the sample in each strata should ideally be in proportion to the members of that group within the target population or sampling frame. Draw a random sample from each stratum and combine them to form your final sample.
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How To Cluster Sample
First, choose the target population that you wish to study and determine your desired sample size. Then, divide your sample into clusters. When forming the clusters, make sure each clusterââ¬â¢s population is diverse, has a similar distribution of characteristics to the distribution of the population as a whole, and has the same number of members. The goal is to form clusters that are representative of the total population as a whole. Next, select clusters by a random selection process. It is important to randomly select from the clusters in order to preserve the validity of your results. The number of clusters selected is based on how large the sample size is. In single-stage sampling, collect data from each individual unit of the clusters you selected in Step 3. In the case of double-stage or multi-stage sampling, you randomly select individual units from within the selected clusters to use as your sample. You will then collect your data from each of these individual units. Double-stage and multi-stage clustering tend to be easier than single-stage because you will be working with a much smaller sample. à Ã
Generalizing To A Wider Student Population With Random Sampling
When conducting reading research, we must first define our population of interest, or the universe of students for whom we want to learn more about the way they read. Are conclusions being drawn about all third graders in the district? Third graders in the district who are struggling readers? Third graders only within the school where the study is conducted? We will be generalizing any conclusions about these students we may draw from our study.
The phrase in the definition for random sampling is what distinguishes it from many other sampling procedures. In many intervention studies, for instance, a convenience sample is chosenschools are selected that have the infrastructure and time to partake in the study, or certain teachers within the school are selected because they are willing or able to have their students participate in the study. Likewise, a purposive sample may be chosen. For example, administrators volunteer their highest-quality teachers to participate because they feel it increases the chances that the reading intervention will be found to be successful. In each of these cases, the type of sampling used is not random by definition, because not every teacher or school in the population has an equal chance of being selected to participate. Thus, the ability to generalize results from such studies to a larger population can be compromised.
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Introduction Of Random Sampling
A sample is a part of the population. You can gain information about a population by examining samples of the population.
There are two types of samples
- An unbiased sample is representative of the population. It is chosen at random and is large enough to provide reliable information.
- A biased sample is not representative of a population. One or more groups of people are given benefits.
- An unbiased samples results are proportional to the populations results. As a result, unbiased samples can be used to draw conclusions about a population. Samples that are skewed are not representative of the population. So, you should not use them to make conclusions about the population.
- You can use unbiased samples to make conclusions about populations. Different samples often have slightly different conclusions due to variability in the sample data.
Type Of Random Sampling
The random sampling method uses some manner of a random choice. In this method, all the suitable individuals have the possibility of choosing the sample from the whole sample space. It is a time consuming and expensive method. The advantage of using probability sampling is that it ensures the sample that should represent the population. There are four major types of this sampling method, they are
Now let us discuss its types one by one here.
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Simple Random Sampling: Definition Steps And Examples
By , published Jan 26, 2022
Simple random sampling is a sampling technique in which each member of a population has an equal chance of being chosen, through the use of an unbiased selection method. Each subject in the sample is given a number and then the sample is chosen by a random method.
Key Terms
- A sample is the participants you select from a target population to make generalizations about. As an entire population tends to be too large to work with, a smaller group of participants must act as a representative sample.
- Representative means the extent to which a sample mirrors a researcher’s target population and reflects its characteristics . In an attempt to select a representative sample and avoid sampling bias , psychologists utilize a variety of sampling methods.
- Generalisability means the extent to which their findings can be applied to the larger population of which their sample was a part.
The random sampling method is one of the simplest and most common forms of collecting data as it is meant to provide an unbiased representation of a group. The random subset of selected individuals is used to represent an entire data set.
The goal of simple random sampling is to create a manageable, balanced subset of individuals that is representative of a larger group that would otherwise be too challenging to sample.
When Comparing Different Groups
Sometimes, differences between participants are the main focus of a study, for example, when comparing men and women or people with and without health conditions. Participants are not randomly assigned to different groups, but instead assigned based on their characteristics.
In this type of study, the characteristic of interest is an independent variable, and the groups differ based on the different levels . All participants are tested the same way, and then their group-level outcomes are compared.
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Disadvantages Of A Simple Random Sample
Although there are distinct advantages to using a simple random sample, it does come with inherent drawbacks. These disadvantages include the time needed to gather the full list of a specific population, the capital necessary to retrieve and contact that list, and the bias that could occur when the sample set is not large enough to adequately represent the full population. We go into more detail below.
What Are The 4 Types Of Random Sampling
There are four types of random sampling. Simple random sampling involves an unbiased study of a smaller subset of a larger population. Stratified random sampling uses smaller groups derived from a larger population that is based on shared characteristics and attributes. Systematic sampling is a method that involves specific members of a larger dataset. These samples are selected based on a random starting point using a fixed, periodic interval. The final type of random sampling is cluster sampling, which takes members of a dataset and places them into clusters based on shared characteristics. Researchers then randomly select clusters to study.
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How Researchers Create Random Samples
Random sampling can be costly and time-consuming. However, this approach to gathering data for research does provide the best chance of putting together an unbiased sample that is truly representative of an entire group as a whole.
Going back to the imaginary study of alcohol use among college students, here’s how random sampling might work. According to the National Center for Education Statistics , approximately 19.7 million students were enrolled in U.S. colleges and universities in 2020, the most recent statistics available. These 20 million individuals represent the total population to be studied.
For the purpose of drawing a random sample of this group, all students must have an equal chance of being selected. For example, scientists conducting the study would need to make sure that the sample included the same percentage of men and women as the larger population. According to the NCES statistics, 11.3 million of the total population of college students are female and 8.5 million are male. The sample group would need to reflect this same ratio of women to men.
When you read a health study based on a random sample, be aware that the findings are based not on every single person in the population that fit certain criteria, but on a subset of subjects chosen to represent them. This should help you put the study in perspective.
How To Random Sample
First, choose the target population that you wish to study and determine your desired sample size. The smaller the sample size the less likely it can be generalised to the wider research population and is unlikely to be fully representative. The list of the people from which the sample is drawn is called the sampling frame. Examples of samplong frames include the electoral register, schools, drug addicts etc.). Then, assign a sequential number to each subject in the sampling frame. Next, individuals are selected using an unbiased selection method. Some examples of simple random sampling techniques include lotteries, random computer number generators, or random draws.
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Simple Random Sample: An Overview
As noted above, simple random sampling involves choosing a smaller subset of a larger population. This is done randomly. But the catch here is that there is an equal chance that any of the samples in the subset will be chosen. Researchers tend to choose this method of sampling when they want to make generalizations about the larger population.
Simple random sampling can be conducted by using:
- The lottery method. This method involves assigning a number to each member of the dataset then choosing a prescribed set of numbers from those members at random.
- Technology. Using software programs like Excel makes it easier to conduct random sampling. Researchers just have to make sure that all the formulas and inputs are correctly laid out.
For simple random sampling to work, researchers must know the total population size. They must also be able to remove all hints of bias as simple random sampling is meant to be a completely unbiased approach to garner responses from a large group.
Keep in mind that there is room for error with random sampling. This is noted by adding a plus or minus variance to the results. In order to avoid any errors, researchers must study the entire population, which for all intents and purposes, isn’t always possible.
To ensure bias does not occur, researchers must acquire responses from an adequate number of respondents, which may not be possible due to time or budget constraints.