Random Samples in Research Studies

Содержание
  1. How to use stratified random sampling to your advantage
  2. Definition — what is stratified random sampling?
  3. 1. Define the strata needed for your sample
  4. 2. Define your sample size
  5. 3. Randomly select from each stratum
  6. 4. Review stratum results
  7. 5. Combine all stratum samples into one representative sample
  8. Why do researchers use stratified random sampling?
  9. Advantages of stratified random sampling
  10. Disadvantages of stratified random sampling
  11. Example — Stratified random sampling in action
  12. What is the difference between stratified random sampling and cluster sampling?
  13. What is the difference between stratified random sampling and simple random sampling?
  14. What is simple random sampling?
  15. What should be the size of the sample chosen from each stratum?
  16. Conclusion: Where to go next to learn more?
  17. Technically Speaking: Why We Use Random Sampling in Reading Research
  18. Generalizing to a Wider Student Population with Random Sampling
  19. An Example of Random Sampling in Reading Research
  20. Figure 1. Hypothetical Population
  21. Figure 2. Random Sample
  22. References
  23. Sampling Methods | Types and Techniques Explained
  24. The Purpose of Sampling
  25. Random Sampling
  26. Stratified Sampling
  27. Opportunity Sampling
  28. Systematic Sampling
  29. How many participants should be used?
  30. How to reference this article:
  31. Random Sampling
  32. Summary
  33. Types of Random Sampling Methods
  34. 1. Simple random sampling
  35. 2. Systematic sampling
  36. 3. Stratified sampling
  37. 4. Cluster sampling
  38. Practical Example
  39. Why an Unbiased Random Sample Matters
  40. Probability (Random) Sampling vs. Non-Probability Sampling
  41. More Resources

How to use stratified random sampling to your advantage

Random Samples in Research Studies

When it comes to statistical surveys and getting the data you need, there’s no shortage of sampling techniques you can use.
Simple sampling, systematic sampling, quota sampling, cluster sampling — there are numerous methods for designing a sample to represent your population of interest.

Of course, each varies in accuracy, reliability, and efficiency. No two methods are the same and some are more complicated than others.

In this article, we’re going to focus on one in particular: stratified random sampling. We’re going to highlight what it is, how you can use it to your advantage, and several best-practice tips to help you get going.

Definition — what is stratified random sampling?

Stratified random sampling (also known as proportional random sampling and quota random sampling) is a probability sampling technique in which the total population is divided into homogenous groups (strata) to complete the sampling process.

Each stratum (the singular for strata) is formed shared attributes or characteristics — such as level of education, income and/or gender. Random samples are then selected from each stratum and can be compared against each other to reach specific conclusions.

For example, a researcher might want to know the correlation between income and education — they could use stratified random sampling to divide the population into strata and take a random sample from it.

Stratified random sampling is typically used by researchers when trying to evaluate data from different subgroups or strata. It allows them to quickly obtain a sample population that best represents the entire population being studied.

Stratified random sampling is one of four probability sampling techniques: Simple random sampling, systematic sampling, stratified sampling, and cluster sampling.

Of course, your choice of sampling technique will depend on your goals, budget, and desired level of accuracy. With this in mind, make sure to clearly outline what it is you want to achieve and try out different methods to see which work best for your research.

But for now, where do you start with stratified random sampling?

1. Define the strata needed for your sample

Strata are usually created the differences between participant’s shared characteristics – e.g. their race, gender, nationality, level of education, or age group. Researchers may or may not already have prior knowledge about a population’s shared characteristics.

2. Define your sample size

It’s important to define the ratio numbers of your sample so it is proportionally representative of the total population (see the FAQ section below for more information).

3. Randomly select from each stratum

After stratifying each member of the population into relevant subsections, you will apply random sampling techniques to randomly select participants from each stratum. Potential sampling methods for random selection include simple random sampling or systematic random sampling.

4. Review stratum results

When done correctly, stratified random sampling will provide a final sample that is exhaustive (each participant of the population must belong to one stratum) and mutually exclusive (where participants don’t overlap with another stratum).

5. Combine all stratum samples into one representative sample

For an accurate, representative sample of the entire population, you must combine all stratum examples into one. This will allow you to carry out a total population analysis.

Why do researchers use stratified random sampling?

Researchers use stratified random sampling when they are already aware of (or have become aware of) subdivisions within a population that need to be accounted for in their research. This leads to several advantages and disadvantages:

Advantages of stratified random sampling

  • Stratified random sampling gives you a systematic way of gaining a population sample that takes into account the demographic make-up of the population, which leads to stronger research results.
  • The method is fair for participants as the sample from each stratum can be randomly selected, meaning there is no bias in the process.
  • As participant grouping must be exhaustive and mutually exclusive, stratified random sampling removes variation and the chances of overlap between each stratum.
  • Lastly, it helps with efficient and accurate data collection. Having a smaller, more relevant sample to work with means a more manageable and affordable research project.

Disadvantages of stratified random sampling

  • Researchers may hold prior knowledge of the population’s shared characteristics beforehand, which increases the risk for selection bias when strata are defined.
  • There is more administration to do to conduct this process, so researchers must include this extra time and order.
  • When randomly sampling each stratum, the resulting sample may not be representative of the full population. It is worth reviewing the results to see if the sample is proportional to the whole population.
  • Once you have the final sample, data analysis of the information becomes more complicated to take into account the layers of the stratum.

If you’re worried about errors in your sampling, this article can help.

Example — Stratified random sampling in action

Let’s look at an example to bring this method to life:

If we’re investigating wage differences between genders, we can stratify a larger population into different genders (e.g. female and male) or pay grades (e.g. under $50k, $50-100k, $100-250k, over $250k).

If we choose to stratify by gender and randomly select a sample across each of the gender groups, then these samples can be compared using pay grades to explore wage gaps.

So in the example below, the total population is 15. When gender is applied to the population, we can see there are more men (9) than women (6). This gives us a sample ratio of 2:1, or a sample fraction of ⅔ men to ⅓ women.

If we want a sample size of 5 (one-third of the total population), we must randomly select participants in proportion to the size of each stratum. The number of participants selected must reflect the sample ratio.

As a result, the final sample will have 5 randomly selected participants, which will be split by gender (made up of 2 women and 3 men).

What is the difference between stratified random sampling and cluster sampling?

Let’s explore cluster sampling vs stratified random sampling.

There are three forms of cluster sampling: one-stage, two-stage and multi-stage.

One-stage cluster sampling first creates groups, or clusters, from the population of participants that represent the total population. These groups are comparable groupings that exist – e.g. zip codes, schools, or cities.

The clusters are randomly selected, and then sampling occurs within these selected clusters. There can be many clusters and these are mutually exclusive, so participants don’t overlap between the groups

Two-stage cluster sampling first randomly selects the cluster, then the participants are randomly selected from within that cluster.

Multi-stage cluster sampling is a more complex process which involves dividing the population into groups before one or more clusters are chosen at random and sampled.

The main difference between stratified sampling and cluster sampling is that with cluster sampling, there are natural groups separating your population. In cluster sampling, the sampling unit is the whole cluster. Instead of sampling individuals from each group, a researcher will study whole clusters.

In stratified random sampling, however, a sample is drawn from each strata (using a random sampling method simple random sampling or systematic sampling). Elements of each of the samples will be distinct, giving the entire population an equal opportunity to be part of these samples. Typically, natural groups do not exist, so you divide your target population into groups (stratum).

Generally, cluster sampling is much more affordable and “efficient”, whereas stratified random sampling is more precise.

What is the difference between stratified random sampling and simple random sampling?

Let’s explore simple random sampling vs stratified random sampling.

What is simple random sampling?

Simple random sampling selects a smaller group (the sample) from a larger group of the total number of participants (the population). It’s one of the simplest systematic sampling methods used to gain a random sample. Simple random sampling relies on using a selection method that provides each participant with an equal chance of being selected.

And, since the selection process is probability and a random selection, the smaller sample is more ly to be representative of the total population and free from researcher bias. This method is also called a method of chance.

Simple random sampling involves randomly selecting data from the entire population so each possible sample is ly to occur.

There are no constraints with this method and therefore no bias.

Stratified random sampling, on the other hand, divides the population into smaller groups (strata) shared characteristics. A random sample is then taken from each (in direct proportion to the size of the stratum compared to the population) and combined to create a random sample.

What should be the size of the sample chosen from each stratum?

The size of the sample you select will vary several factors:

  • ScaleIn general, to analyze and draw meaningful conclusions, you need a large sample that can provide you with sufficient data from the total population.
  • PracticalityFrom a practical standpoint, if you have a larger population, you want to also have a sample size that does not require a lot of administration to collect and manage.
  • AccuracyYou want a sample size that is going to accurately represent the total population to make the findings as truthful as possible.

With stratified random sampling, you will end up with a sample that is proportionally representative to the population the stratum used.

In most cases, this will work well. However, you may need to vary the proportions manually if you’re aware of additional information that could skew the results.

For instance, using our wage example from above, the sample has 5 randomly selected participants, which will be split by gender (made up of 2 women and 3 men). If you’re aware that the wage gap range is larger across men, then this sample may miss key information as you don’t have enough male data to support the reality.

In this case, you may want to:

Either, adjust the sample ratio to include more men – e.g. from 2:1 (6 men to 3 women) to 3:1 (8 men to 2 women).
Or, increase the sample size to include more of the population, to better reflect the wage range in the male proportion of the sample – e.g. increasing the sample size from 5 to 10.

If you’re unsure where to start, try our sample size calculator to get a good indication.

Conclusion: Where to go next to learn more?

And that’s stratified random sampling. Hopefully, you now have a good idea of how to use this probability sampling technique to aid your research and surveys.

But what if you want to simplify the process further by using a research panel?

If you’re thinking of using a research panel instead of conducting research yourself, you may way to read our in-depth eBook: The Panel Management Guide

In it, we discuss how you can:

  • Ensure the right panel size
  • Create the right profiling questions
  • Optimize contact frequency
  • Identify the key indicators of a healthy panel
  • Find out how rewards and incentives can benefit your surveys

Источник: https://www.qualtrics.com/experience-management/research/stratified-random-sampling/

Technically Speaking: Why We Use Random Sampling in Reading Research

Random Samples in Research Studies

Editor’s 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:

  1. Be able to generalize results to a wider student population
  2. Establish a causal relationship between the reading intervention and changes observed in student reading

Two essential principles related to the study’s 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.

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.

Once a population of interest is defined, how do we know that our sample of students participating in the study is representative of that population? When feasible, statisticians select a sampling approach. Sampling involves selecting units from a population of interest such that the sampling units represent the whole population.

Within the context of studying reading interventions within schools, the unit being sampled can range from a number of students within a classroom to entire classrooms or schools, or even a combination of these units.

Random sampling is one such procedure that selects a sample of units from a population by chance, typically to facilitate generalization from the sample to the population (Shadish, Cook, & Campbell, 2002).

Random sampling ensures that results obtained from your sample should approximate what would have been obtained if the entire population had been measured (Shadish et al., 2002). The simplest random sample allows all the units in the population to have an equal chance of being selected. Often in practice we rely on more complex sampling techniques.

The phrase by chance 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 chosen—schools 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.

wise, 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 (known as the external validity of the study) can be compromised.

Perhaps the most important benefit to selecting random samples is that it enables the researcher to rely upon assumptions of statistical theory to draw conclusions from what is observed (Moore & McCabe, 2003).

For example, if data are produced by random sampling, any statistics generated from the data can be assumed to follow a specific distribution. The distribution with which many educators are most familiar is the normal distribution of a bell-shaped curve.

In this distribution, most of the students’ data would fall in the middle, or the average range of performance, and fewer students’ data would fall in the very high or very low performance ranges on either side of the middle.

This provides the researcher a better understanding of how the results from the sample relate to what the results would be for the whole population. Quantifying the degree to which we can confidently know how sample results relate to the population is key to drawing sound inferences and generalizing those results to the student population.

Of course, even within the context of random sampling, several other factors influence a reading study’s external validity. For example, there is the role of sample size to consider.

Larger random samples will typically produce more stable results, meaning estimates for the effect the intervention had on student outcomes can be obtained with smaller margins of error.

There is often a balance the school researcher must consider: obtaining large enough samples to adequately represent the population and achieve reliable results while also working within the financial and logistical constraints of conducting the study.

An Example of Random Sampling in Reading Research

Assume that Figure 1 below displays our student population of interest, and the colored dots represent students with different characteristics.

Figure 1. Hypothetical Population

Figure 2 is a simple random sample of 100 students from the population of interest (i.e., from Figure 1).

Figure 2. Random Sample

Simple random sampling does not guarantee that all important student characteristics are represented in the sample.

If the student characteristics represented by the distinct colors are something believed to be of importance when designing the study, typically we will separate the sample into groups those characteristics (a process referred to as stratifying the sample) and then sample units from those groups or strata. This is done to ensure that all characteristics are properly represented. 

For sound inferences to be drawn from reading intervention studies, researchers need to be able to generalize results from their student samples to a wider student population of interest.

An essential principle for making that generalization is integrating random sampling of students or classrooms into the study design.

In an upcoming part two post, we will discuss the importance of randomly assigning students to study conditions if we want to make sound causal inferences about reading interventions.

References

Moore, D. S., & McCabe, G. P. (2003). Introduction to the practice of statistics (4th ed.). WH Freeman: New York, NY.

Shadish, W. R., Cook, T. D., & Campbell, D. T. (2002). Experimental and quasi-experimental designs for generalized causal inference. Cengage Learning: Boston, MA.

Источник: https://iowareadingresearch.org/blog/technically-speaking-random-sampling

Sampling Methods | Types and Techniques Explained

Random Samples in Research Studies

  • Sampling is the process of selecting a representative group from the population under study.
  • The target population is the total group of individuals from which the sample might be drawn.
  • A sample is the group of people who take part in the investigation. The people who take part are referred to as “participants”.
  • Generalisability refers to the extent to which we can apply the findings of our research to the target population we are interested in. This can only occur if the sample of participants is representative of the population.
  • Biased sample iswWhen certain groups are over or under represented within the sample selected. For instance if only males are selected, or if the advert for volunteers is put into the Guardian, only people who read the Guardian are selected. This limits how much the findings of the study can be generalised to the whole population.

The Purpose of Sampling

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 ly 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. The more representative the sample, the more confident the researcher can be that the results can be generalized to the target population.

One of the problems that can occur when selecting a sample from a target population is sampling bias. Sampling bias refers to situations where the sample does not reflect the characteristics of the target population.

Many psychology studies have a biased sample because they have used an opportunity sample that comprises university students as their participants (e.g. Asch).

OK, so you’ve thought up this brilliant psychological study and designed it perfectly. But who are you going to try it out on and how will you select your participants?

There are various sampling methods. The one chosen will depend on a number of factors (such as time, money etc.).

Random Sampling

Random sampling is a type of probability sampling where everyone in the entire target population has an equal chance of being selected.

This is similar to the national lottery. If the “population” is everyone who has bought a lottery ticket, then each person has an equal chance of winning the lottery (assuming they all have one ticket each).

Random samples require a way of naming or numbering the target population and then using some type of raffle method to choose those to make up the sample.

Random samples are the best method of selecting your sample from the population of interest.

  • The advantages are that your sample should represent the target population and eliminate sampling bias.
  • The disadvantage is that it is very difficult to achieve (i.e. time, effort and money).

Stratified Sampling

The researcher identifies the different types of people that make up the target population and works out the proportions needed for the sample to be representative.

A list is made of each variable (e.g. IQ, gender etc.) which might have an effect on the research. For example, if we are interested in the money spent on books by undergraduates, then the main subject studied may be an important variable.

For example, students studying English Literature may spend more money on books than engineering students so if we use a very large percentage of English students or engineering students then our results will not be accurate.

We have to work out the relative percentage of each group at a university e.g. Engineering 10%, Social Sciences 15%, English 20%, Sciences 25%, Languages 10%, Law 5%, Medicine 15% The sample must then contain all these groups in the same proportion as in the target population (university students).

  • The disadvantage of stratified samplingis that gathering such a sample would be extremely time consuming and difficult to do. This method is rarely used in Psychology.
  • However, the advantage is that the sample should be highly representative of the target population and therefore we can generalize from the results obtained.

Opportunity Sampling

Uses people from target population available at the time and willing to take part. It is convenience.

An opportunity sample is obtained by asking members of the population of interest if they would take part in your research. An example would be selecting a sample of students from those coming the library.

  • This is a quick way and easy of choosing participants (advantage)
  • It may not provide a representative sample, and could be biased (disadvantage).

Systematic Sampling

Chooses subjects in a systematic (i.e. orderly / logical) way from the target population, every nth participant on a list of names.

To take a systematic sample, you list all the members of the population, and then decided upon a sample you would . By dividing the number of people in the population by the number of people you want in your sample, you get a number we will call n.

If you take every nth name, you will get a systematic sample of the correct size. If, for example, you wanted to sample 150 children from a school of 1,500, you would take every 10th name.

  • The advantage of this method is that is should provide a representative sample.
  • The disadvantage is that it is very difficult to achieve (i.e. time, effort and money).

How many participants should be used?

This depends on several factors; the size of the target population is important. If the target population is very large (e.g. all 4-6 yr olds in Britain) then you need a fairly large sample in order to be representative.

If the target population is much smaller, then the sample can be smaller but still be representative. There must be enough participants to make the sample representative of the target population.

Lastly, the sample must not be so large that the study takes too long or is too expensive!

How to reference this article:

McLeod, S. A. (2019, August 03). Sampling methods. Simply Psychology. www.simplypsychology.org/sampling.html

Random Sampling

Random Samples in Research Studies

Random sampling, or probability sampling, is a sampling method that allows for the randomization of sample selection, i.e., each sample has the same probability as other samples to be selected to serve as a representation of an entire population.

Random sampling is considered one of the most popular and simple data collection methods in research fields (probability and statisticsStatistics, mathematics, etc.). It allows for unbiased data collection, which lets studies arrive at unbiased conclusions.

Summary

  • Random sampling, also known as probability sampling, is a sampling method that allows for the randomization of sample selection.

  • It is essential to keep in mind that samples do not always produce an accurate representation of a population in its entirety; hence, any variations are referred to as sampling errors.

  • There are four primary, random (probability) sampling methods – simple random sampling, systematic sampling, stratified sampling, and cluster sampling.

Types of Random Sampling Methods

There are four primary, random (probability) sampling methods. These methods are:

1. Simple random sampling

Simple random sampling is the randomized selection of a small segment of individuals or members from a whole population. It provides each individual or member of a population with an equal and fair probability of being chosen. The simple random sampling method is one of the most convenient and simple sample selection techniques.

2. Systematic sampling

Systematic sampling is the selection of specific individuals or members from an entire population. The selection often follows a predetermined interval (k). The systematic sampling method is comparable to the simple random sampling method; however, it is less complicated to conduct.

3. Stratified sampling

Stratified sampling, which includes the partitioning of a population into subclasses with notable distinctions and variances. The stratified sampling method is useful, as it allows the researcher to make more reliable and informed conclusions by confirming that each respective subclass has been adequately represented in the selected sample.

4. Cluster sampling

Cluster sampling, which, similar to the stratified sampling methodStratified Random Sampling, includes dividing a population into subclasses.

Each of the subclasses should portray comparable characteristics to the entire selected sample. This method entails the random selection of a whole subclass, as opposed to the sampling of members from each subclass.

This method is ideal for studies that involve widely spread populations.

Practical Example

A company currently employs 850 individuals. The company wishes to conduct a survey to determine employee satisfaction a few identified variables. The research team decides to have the sample set at 85 employees. The 85 employees will be part of the survey and will be used as a representation for the total population of 850 employees.

In such a scenario, the sample is the 85 employees, and the population is the entire workforce consisting of 850 individuals. the sample size, any employee from the workforce can be selected for the survey. It goes to say that each employee has an equivalent probability of being randomly selected for the survey.

It is important to keep in mind that samples do not always produce an accurate representation of a population in its entirety; hence, any variations are referred to as sampling errorsSampling Errors.

A sampling error can be defined as the difference between the respective statistics (sample values) and parameters (population values). The sampling error is inevitable when sample data is being used.

Why an Unbiased Random Sample Matters

Unbiased random sampling results in more reliable and unbiased conclusions.

For example, the employee satisfaction survey mentioned above makes use of a sample size of 85 employees. Of these employees, it is possible to have selected more females than males for the study, despite the entire workforce having 450 men and 400 women. It would result in a sampling error, as it causes variations in the results obtained. Ideally, results should be objective and unbiased.

Probability (Random) Sampling vs. Non-Probability Sampling

Probability – or random sampling – is the random selection of sample participants to derive conclusions and assumptions about an entire population. On the other hand, non-probability sampling is the selection of sample participants specified criteria or suitability.

More Resources

Thank you for reading CFI’s guide to Random Sampling. To keep advancing your career, the additional CFI resources below will be useful:

Источник: https://corporatefinanceinstitute.com/resources/knowledge/other/random-sampling/

Psychologydo
Добавить комментарий

;-) :| :x :twisted: :smile: :shock: :sad: :roll: :razz: :oops: :o :mrgreen: :lol: :idea: :grin: :evil: :cry: :cool: :arrow: :???: :?: :!: