Attendance — suitable to convenience sampling, sometimes asking permission to wait in a waiting room on a certain day or attending a program session will help in your recruitment efforts.

I do find that putting a face to a name can make potential participants feel more comfortable signing up. I come armed with recruitment flyers to leave on tables or hand out directly to people I meet.

Be careful. You want to ensure you have the right to access contact information and can contact them for evaluation purposes. Often, I get program staff to do the first cold call, introducing me. Recruiting for Mixed Methods — sometimes you need to recruit for multiple methods. That way I have permission to contact them directly, with the contact information they provide.

Again, using many of these strategies will make your recruitment faster and hopefully get you the sample you need.

In most of these recruitment methods and strategies, having a link where participants can access more information or even sign up directly for an interview or focus group using tools such as Calendly will boost your chances of reaching your sample, rather than asking participants to email or phone you.

Adherence to ethical practice is important throughout recruitment. Make sure you reflect on your strategies for accessibility and inclusion, but also look for potential coercion, including reviewing your use of an incentive.

Do you have any go-to recruitment methods? Or have you tried any of these before? Comment on this article or connect with us on LinkedIn or Twitter! Cart 0. Information People Resources About Contact. Back Overview Articles Dictionary Podcasts FAQs Courses.

Sampling and Recruitment This article is rated as:. Identify your Sample Identifying your sample size and characteristics of the sample ahead of your data collection will likely help you to select your recruitment methods and will definitely help you to identify when you can stop recruiting.

Sampling Methods There are several ways to sample! Here are some of the more common ones used in evaluation: Random — include all individuals who fit your inclusion criteria. Quota sampling. In this technique, the population is segmented into mutually-exclusive subgroups just as in stratified sampling , and then a non-random set of observations is chosen from each subgroup to meet a predefined quota.

In proportional quota sampling , the proportion of respondents in each subgroup should match that of the population. But you will have to stop asking Hispanic-looking people when you have 15 responses from that subgroup or African-Americans when you have 13 responses even as you continue sampling other ethnic groups, so that the ethnic composition of your sample matches that of the general American population.

In this case, you may decide to have 50 respondents from each of the three ethnic subgroups Caucasians, Hispanic-Americans, and African- Americans , and stop when your quota for each subgroup is reached.

Neither type of quota sampling will be representative of the American population, since depending on whether your study was conducted in a shopping center in New York or Kansas, your results may be entirely different.

The non-proportional technique is even less representative of the population but may be useful in that it allows capturing the opinions of small and underrepresented groups through oversampling. Expert sampling. This is a technique where respondents are chosen in a non-random manner based on their expertise on the phenomenon being studied.

For instance, in order to understand the impacts of a new governmental policy such as the Sarbanes-Oxley Act, you can sample an group of corporate accountants who are familiar with this act.

The advantage of this approach is that since experts tend to be more familiar with the subject matter than non-experts, opinions from a sample of experts are more credible than a sample that includes both experts and non-experts, although the findings are still not generalizable to the overall population at large.

Snowball sampling. In snowball sampling, you start by identifying a few respondents that match the criteria for inclusion in your study, and then ask them to recommend others they know who also meet your selection criteria. For instance, if you wish to survey computer network administrators and you know of only one or two such people, you can start with them and ask them to recommend others who also do network administration.

Although this method hardly leads to representative samples, it may sometimes be the only way to reach hard-to-reach populations or when no sampling frame is available. In the preceding sections, we introduced terms such as population parameter, sample statistic, and sampling bias.

In this section, we will try to understand what these terms mean and how they are related to each other. In other words, a response is a measurement value provided by a sampled unit. Each respondent will give you different responses to different items in an instrument. Responses from different respondents to the same item or observation can be graphed into a frequency distribution based on their frequency of occurrences.

For a large number of responses in a sample, this frequency distribution tends to resemble a bell-shaped curve called a normal distribution , which can be used to estimate overall characteristics of the entire sample, such as sample mean average of all observations in a sample or standard deviation variability or spread of observations in a sample.

Populations also have means and standard deviations that could be obtained if we could sample the entire population. Sample statistics may differ from population parameters if the sample is not perfectly representative of the population; the difference between the two is called sampling error.

Theoretically, if we could gradually increase the sample size so that the sample approaches closer and closer to the population, then sampling error will decrease and a sample statistic will increasingly approximate the corresponding population parameter.

If a sample is truly representative of the population, then the estimated sample statistics should be identical to corresponding theoretical population parameters. How do we know if the sample statistics are at least reasonably close to the population parameters?

Here, we need to understand the concept of a sampling distribution. Imagine that you took three different random samples from a given population, as shown in Figure 8.

If each random sample was truly representative of the population, then your three sample means from the three random samples will be identical and equal to the population parameter , and the variability in sample means will be zero.

But this is extremely unlikely, given that each random sample will likely constitute a different subset of the population, and hence, their means may be slightly different from each other. However, you can take these three sample means and plot a frequency histogram of sample means.

If the number of such samples increases from three to 10 to , the frequency histogram becomes a sampling distribution.

Hence, a sampling distribution is a frequency distribution of a sample statistic like sample mean from a set of samples , while the commonly referenced frequency distribution is the distribution of a response observation from a single sample.

Just like a frequency distribution, the sampling distribution will also tend to have more sample statistics clustered around the mean which presumably is an estimate of a population parameter , with fewer values scattered around the mean.

With an infinitely large number of samples, this distribution will approach a normal distribution. The variability or spread of a sample statistic in a sampling distribution i. In contrast, the term standard deviation is reserved for variability of an observed response from a single sample.

The mean value of a sample statistic in a sampling distribution is presumed to be an estimate of the unknown population parameter. Based on the spread of this sampling distribution i. Confidence interval is the estimated probability that a population parameter lies within a specific interval of sample statistic values.

All normal distributions tend to follow a percent rule see Figure 8. Since a sampling distribution with an infinite number of samples will approach a normal distribution, the same rule applies, and it can be said that:.

Skip to main content. Main Body. Search for:. 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.

With a simple random sample, there has to be room for error represented by a plus and minus variance sampling error. For example, if in a high school of 1, students a survey were to be taken to determine how many students are left-handed, random sampling can determine that eight out of the sampled are left-handed.

The same is true regardless of the subject matter. A survey on the percentage of the student population that has green eyes or is physical disability would result in a mathematical probability based on a simple random survey, but always with a plus or minus variance. Although simple random sampling is intended to be an unbiased approach to surveying, sample selection bias can occur.

When a sample set of the larger population is not inclusive enough, representation of the full population is skewed and requires additional sampling techniques. The simple random sampling process entails size steps.

Each step much be performed in sequential order. The origin of statistical analysis is to determine the population base. This is the group in which you wish to learn more about, confirm a hypothesis , or determine a statistical outcome.

This step is to simply identify what that population base is and to ensure that group will adequately cover the outcome you are trying to solve for.

Example: I wish to learn how the stocks of the largest companies in the United States have performed over the past 20 years. Before picking the units within a population, we need to determine how many units to select This sample size may be constrained based on the amount of time, capital rationing , or other resources available to analyze the sample.

However, be mindful to pick a sample size large enough to be truly representative of the population. In our example, the items within the population are easy to determine as they've already been identified for us i.

However, imagine analyzing the students currently enrolled at a university or food products being sold at a grocery store. This steps entails crafting the entire list of all items within your population.

The simple random sample process call for every unit within the population receiving an unrelated numerical value. This is often assigned based on how the data may be filtered. For example, I could assign the numbers 1 to to the companies based on market cap , alphabetical, or company formation date.

How the values are assigned doesn't entirely matter; all that matters is each value is sequential and each value has an equal chance of being selected. In step 2, we selected the number of items we wanted to analyze within our population. For the running example, we choose to analyze 20 items.

In the fifth step, we randomly select 20 numbers of the values assigned to our variables. In the running example, this is the numbers 1 through There are multiple ways to randomly select these 20 numbers discussed later in this article.

Example: Using the random number table, I select the numbers 2, 7, 17, 67, 68, 75, 77, 87, 92, , , , , , , , , , , and The last step of a simple random sample is the bridge step 4 and step 5. Each of the random variables selected in the prior step corresponds to a item within our population.

The sample is selected by identifying which random values were chosen and which population items those values match. Example: My sample consists of the 2nd item in the list of companies alphabetically listed by CEO's last name.

My sample also consists of company number 7, 17, 67, etc. There is no single method for determining the random values to be selected i. Step 5 above. The analyst can not simply choose numbers at random as there may not be randomness with numbers.

For example, the analyst's wedding anniversary may be the 24th, so they may consciously or subconsciously pick the random value Instead, the analyst may choose one of the following methods:. When pulling together a sample, consider getting assistance from a colleague or independent person.

They may be able to identify biases or discrepancies you may not be aware of. A simple random sample is used to represent the entire data population.

A stratified random sample divides the population into smaller groups, or strata, based on shared characteristics. Unlike simple random samples, stratified random samples are used with populations that can be easily broken into different subgroups or subsets.

These groups are based on certain criteria, then elements from each are randomly chosen in proportion to the group's size versus the population.

This method of sampling means there will be selections from each different group—the size of which is based on its proportion to the entire population. Researchers must ensure the strata do not overlap. Each point in the population must only belong to one stratum so each point is mutually exclusive.

Overlapping strata would increase the likelihood that some data are included, thus skewing the sample. Systematic sampling entails selecting a single random variable, and that variable determines the internal in which the population items are selected.

For example, if the number 37 was chosen, the 37th company on the list sorted by CEO last name would be selected by the sample.

Stage 4: Determine Sample Size Stage 5: Collect Data Stage 6: Assess Response Rate

Sample selection process - Stage 3: Choose Sampling Technique Stage 4: Determine Sample Size Stage 5: Collect Data Stage 6: Assess Response Rate

Example: Using the random number table, I select the numbers 2, 7, 17, 67, 68, 75, 77, 87, 92, , , , , , , , , , , and The last step of a simple random sample is the bridge step 4 and step 5.

Each of the random variables selected in the prior step corresponds to a item within our population. The sample is selected by identifying which random values were chosen and which population items those values match. Example: My sample consists of the 2nd item in the list of companies alphabetically listed by CEO's last name.

My sample also consists of company number 7, 17, 67, etc. There is no single method for determining the random values to be selected i. Step 5 above. The analyst can not simply choose numbers at random as there may not be randomness with numbers.

For example, the analyst's wedding anniversary may be the 24th, so they may consciously or subconsciously pick the random value Instead, the analyst may choose one of the following methods:.

When pulling together a sample, consider getting assistance from a colleague or independent person. They may be able to identify biases or discrepancies you may not be aware of. A simple random sample is used to represent the entire data population.

A stratified random sample divides the population into smaller groups, or strata, based on shared characteristics. Unlike simple random samples, stratified random samples are used with populations that can be easily broken into different subgroups or subsets.

These groups are based on certain criteria, then elements from each are randomly chosen in proportion to the group's size versus the population. This method of sampling means there will be selections from each different group—the size of which is based on its proportion to the entire population.

Researchers must ensure the strata do not overlap. Each point in the population must only belong to one stratum so each point is mutually exclusive. Overlapping strata would increase the likelihood that some data are included, thus skewing the sample.

Systematic sampling entails selecting a single random variable, and that variable determines the internal in which the population items are selected. For example, if the number 37 was chosen, the 37th company on the list sorted by CEO last name would be selected by the sample.

Then, the 74th i. the next 37th and the st i. the next 37th after that would be added as well. Simple random sampling does not have a starting point; therefore, there is the risk that the population items selected at random may cluster.

In our example, there may be an abundance of CEOs with the last name that start with the letter 'F'. Systematic sampling strives to even further reduce bias to ensure these clusters do not happen. Cluster sampling can occur as a one-stage cluster or two-stage cluster. In a one-stage cluster, items within a population are put into comparable groupings; using our example, companies are grouped by year formed.

Then, sampling occurs within these clusters. Two-stage cluster sampling occurs when clusters are formed through random selection. The population is not clustered with other similar items. Then, sample items are randomly selected within each cluster.

Simple random sampling does not cluster any population sets. Though sample random sampling may be a simpler, clustering especially two-stage clustering may enhance the randomness of sample items. In addition, cluster sampling may provide a deeper analysis on a specific snapshot of a population which may or may not enhance the analysis.

While simple random samples are easy to use, they do come with key disadvantages that can render the data useless. 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 sub-populations 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. Therefore, simple random sampling is known for its randomness and less chance of sampling bias. 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, men, and nonbinary people. For this reason, simple random sampling is more commonly used when the researcher knows little about the population.

If the researcher knew more, it would be better to use a different sampling technique, such as stratified random sampling, which helps to account for the differences within the population, such as age, race, or gender. Other disadvantages include the fact that for sampling from large populations, the process can be time-consuming and costly compared to other methods.

Researchers may find a certain project not worth the endeavor of its cost-benefit analysis does not generate positive results. As every unit has to be assigned an identifying or sequential number prior to the selection process, this task may be difficult based on the method of data collection or size of the data set.

No easier method exists to extract a research sample from a larger population than simple random sampling. Selecting enough subjects completely at random from the larger population also yields a sample that can be representative of the group being studied.

Among the disadvantages of this technique are difficulty gaining access to respondents that can be drawn from the larger population, greater time, greater costs, and the fact that bias can still occur under certain circumstances. A stratified random sample, in contrast to a simple draw, first divides the population into smaller groups, or strata, based on shared characteristics.

Therefore, a stratified sampling strategy will ensure that members from each subgroup are included in the data analysis. Stratified sampling is used to highlight differences between groups in a population, as opposed to simple random sampling, which treats all members of a population as equal, with an equal likelihood of being sampled.

Using simple random sampling allows researchers to make generalizations about a specific population and leave out any bias. Using statistical techniques, inferences and predictions can be made about the population without having to survey or collect data from every individual in that population.

When analyzing a population, simple random sampling is a technique that results in every item within the population to have the same probability of being selected for the sample size.

This more basic form of sampling can be expanded upon to derive more complicated sampling methods. However, the process of making a list of all items in a population, assigning each a sequential number, choosing the sample size, and randomly selecting items is a more basic form of selecting units for analysis.

Use limited data to select advertising. Create profiles for personalised advertising. Use profiles to select personalised advertising. Create profiles to personalise content. Use profiles to select personalised content.

Measure advertising performance. Measure content performance. Understand audiences through statistics or combinations of data from different sources.

Develop and improve services. Use limited data to select content. List of Partners vendors. Table of Contents Expand. Table of Contents. What Is a Simple Random Sample? How It Works.

Conducting a Simple Random Sample. Author: Will Webster. In survey research, sampling is the process of using a subset of a population to represent the whole population. To ask every person would be almost impossible.

Sampling allows large-scale research to be carried out with a more realistic cost and time-frame because it uses a smaller number of individuals in the population with representative characteristics to stand in for the whole. However, when you decide to sample, you take on a new task.

You have to decide who is part of your sample list and how to choose the people who will best represent the whole population. How you go about that is what the practice of sampling is all about.

Free eBook: Market Research Trends. Although the idea of sampling is easiest to understand when you think about a very large population, it makes sense to use sampling methods in research studies of all types and sizes. And because sampling allows you to research larger target populations using the same resources as you would smaller ones, it dramatically opens up the possibilities for research.

Sampling is a little like having gears on a car or bicycle. It allows us to do things like carrying out exit polls during elections, map the spread and effects rates of epidemics across geographical areas, and carry out nationwide census research that provides a snapshot of society and culture.

Sampling strategies in research vary widely across different disciplines and research areas, and from study to study. Here are some of the best-known options. With simple random sampling , every element in the population has an equal chance of being selected as part of the sample.

Simple random sampling can be done by anonymizing the population — e. by assigning each item or person in the population a number and then picking numbers at random.

Pros: Simple random sampling is easy to do and cheap. Designed to ensure that every member of the population has an equal chance of being selected, it reduces the risk of bias compared to non-random sampling.

Cons: It offers no control for the researcher and may lead to unrepresentative groupings being picked by chance. With systematic sampling the random selection only applies to the first item chosen.

A rule then applies so that every nth item or person after that is picked. This is commonly achieved using a random number generator. This means you would start with person number three on your list and pick every tenth person. Pros: Systematic sampling is efficient and straightforward, especially when dealing with populations that have a clear order.

It ensures a uniform selection across the population. Stratified sampling involves random selection within predefined groups. They can then decide how to subdivide stratify it in a way that makes sense for the research. We know that gender is highly correlated with height, and if we took a simple random sample of students out of the 2, who attend the college , we could by chance get females and not one male.

This would bias our results and we would underestimate the height of students overall. Pros: Stratified sampling enhances the representation of all identified subgroups within a population, leading to more accurate results in heterogeneous populations.

With cluster sampling, groups rather than individual units of the target population are selected at random for the sample. These might be pre-existing groups, such as people in certain zip codes or students belonging to an academic year.

Cluster sampling can be done by selecting the entire cluster, or in the case of two-stage cluster sampling, by randomly selecting the cluster itself, then selecting at random again within the cluster. Pros: Cluster sampling is economically beneficial and logistically easier when dealing with vast and geographically dispersed populations.

Cons: Due to potential similarities within clusters, this method can introduce a greater sampling error compared to other methods. Here are some forms of non-probability sampling and how they work. People or elements in a sample are selected on the basis of their accessibility and availability.

If you are doing a research survey and you work at a university, for example, a convenience sample might consist of students or co-workers who happen to be on campus with open schedules who are willing to take your questionnaire.

Pros: Convenience sampling is the most straightforward method, requiring minimal planning, making it quick to implement. Cons: Due to its non-random nature, the method is highly susceptible to biases, and the results are often lacking in their application to the real world.

Like the probability-based stratified sampling method, this approach aims to achieve a spread across the target population by specifying who should be recruited for a survey according to certain groups or criteria. For example, your quota might include a certain number of males and a certain number of females.

Alternatively, you might want your samples to be at a specific income level or in certain age brackets or ethnic groups. Participants for the sample are chosen consciously by researchers based on their knowledge and understanding of the research question at hand or their goals. Also known as judgment sampling, this technique is unlikely to result in a representative sample , but it is a quick and fairly easy way to get a range of results or responses.

Pros: Purposive sampling targets specific criteria or characteristics, making it ideal for studies that require specialized participants or specific conditions. With this approach, people recruited to be part of a sample are asked to invite those they know to take part, who are then asked to invite their friends and family and so on.

The participation radiates through a community of connected individuals like a snowball rolling downhill. Pros: Especially useful for hard-to-reach or secretive populations, snowball sampling is effective for certain niche studies. Cons: The method can introduce bias due to the reliance on participant referrals, and the choice of initial seeds can significantly influence the final sample.

Choosing the right sampling method is a pivotal aspect of any research process, but it can be a stumbling block for many. If you aim to get a general sense of a larger group, simple random or stratified sampling could be your best bet.

For focused insights or studying unique communities, snowball or purposive sampling might be more suitable.

Sample selection process - Stage 3: Choose Sampling Technique Stage 4: Determine Sample Size Stage 5: Collect Data Stage 6: Assess Response Rate

Evaluation is not research; evaluation often benefits from a more heterogenous, representative sample. For example, you may choose not to include attendance rates of a program as an inclusion criterion to allow you to explore barriers faced in participation.

For example, are they youth, or could they have any cognitive disabilities? This may require close attention to your consent process. Access is another key consideration. If not, you may want to consider using an interpreter in your data collection process.

Check out our article on Top Tips for Using a Real-Time Interpreter for Interviews and Focus Groups. Recruiting participants who dropped out, quit, or never started a program can be very difficult.

If this is part of your identified sample, you will likely need to work with program staff to implement a process where there is an exit survey, or a few questions asked by intake or administrative staff at the time of contact with potential participants. There are several ways to sample!

Here are some of the more common ones used in evaluation:. Random — include all individuals who fit your inclusion criteria. Random sampling means everyone in the population has an equal chance of participation.

Convenience — you recruit those who are most accessible to you. For example, you may attend a program session and use the participants from that session as your sample, or you may sit in the waiting room of an office and use people who have appointments that day as your sample. Snowball — using word of mouth you build your sample starting from the first participant.

If you can identify one or only a small number of participants, you can use the assumption that your first participants likely know others that fit your inclusion criteria as they did. In snowball sampling, you ask your participants to help you recruit by spreading the word, or at least to help you identify other means to recruit your sample.

You likely want to have recruitment cards or flyers available to give out see an example below. Maximum Variation — you intentionally recruit for variation. Not only do you have an identified overall sample size, but you have it broken down — for example, perhaps you want 10 participants from each of the three program sites or spanning certain ages, genders, or backgrounds.

This is a favourite of mine in evaluation because I often try to capture varied experiences. The disadvantage is that sometimes recruitment can take longer, and you may need very targeted recruitment strategies.

Remember when I said recruiting sometimes involves hustling and using a number of strategies? You can definitely use a combination of these sampling methods!

Some concrete methods for doing so include:. Posters — Particularly if you are recruiting from individuals who attend a certain office or program, hanging posters in a waiting room can be effective. Virtual posters — most people are online, so using social media is probably one of the strongest recruitment tools.

Website pop-ups — like virtual posters, if your program has a website you can work with the web team to include a pop-up for visitors to the site inviting them to participate. Collaborators — I often tell my clients at our kickoff meeting that I expect them to be champions of evaluation, which includes making connections or introductions, and advocating the importance of participation in evaluation.

Your evaluation advisory committee or working group, if one exists, can probably do a lot in terms of identifying staff to talk to, or customers to recruit. Just keep in mind that they can introduce their own bias and direct you to the more favourable participants.

Attendance — suitable to convenience sampling, sometimes asking permission to wait in a waiting room on a certain day or attending a program session will help in your recruitment efforts.

I do find that putting a face to a name can make potential participants feel more comfortable signing up. With a simple random sample, there has to be room for error represented by a plus and minus variance sampling error.

For example, if in a high school of 1, students a survey were to be taken to determine how many students are left-handed, random sampling can determine that eight out of the sampled are left-handed. The same is true regardless of the subject matter.

A survey on the percentage of the student population that has green eyes or is physical disability would result in a mathematical probability based on a simple random survey, but always with a plus or minus variance.

Although simple random sampling is intended to be an unbiased approach to surveying, sample selection bias can occur. When a sample set of the larger population is not inclusive enough, representation of the full population is skewed and requires additional sampling techniques.

The simple random sampling process entails size steps. Each step much be performed in sequential order. The origin of statistical analysis is to determine the population base.

This is the group in which you wish to learn more about, confirm a hypothesis , or determine a statistical outcome. This step is to simply identify what that population base is and to ensure that group will adequately cover the outcome you are trying to solve for. Example: I wish to learn how the stocks of the largest companies in the United States have performed over the past 20 years.

Before picking the units within a population, we need to determine how many units to select This sample size may be constrained based on the amount of time, capital rationing , or other resources available to analyze the sample. However, be mindful to pick a sample size large enough to be truly representative of the population.

In our example, the items within the population are easy to determine as they've already been identified for us i. However, imagine analyzing the students currently enrolled at a university or food products being sold at a grocery store.

This steps entails crafting the entire list of all items within your population. The simple random sample process call for every unit within the population receiving an unrelated numerical value. This is often assigned based on how the data may be filtered.

For example, I could assign the numbers 1 to to the companies based on market cap , alphabetical, or company formation date.

How the values are assigned doesn't entirely matter; all that matters is each value is sequential and each value has an equal chance of being selected. In step 2, we selected the number of items we wanted to analyze within our population. For the running example, we choose to analyze 20 items.

In the fifth step, we randomly select 20 numbers of the values assigned to our variables. In the running example, this is the numbers 1 through There are multiple ways to randomly select these 20 numbers discussed later in this article.

Example: Using the random number table, I select the numbers 2, 7, 17, 67, 68, 75, 77, 87, 92, , , , , , , , , , , and The last step of a simple random sample is the bridge step 4 and step 5. Each of the random variables selected in the prior step corresponds to a item within our population.

The sample is selected by identifying which random values were chosen and which population items those values match. Example: My sample consists of the 2nd item in the list of companies alphabetically listed by CEO's last name. My sample also consists of company number 7, 17, 67, etc.

There is no single method for determining the random values to be selected i. Step 5 above. The analyst can not simply choose numbers at random as there may not be randomness with numbers.

For example, the analyst's wedding anniversary may be the 24th, so they may consciously or subconsciously pick the random value Instead, the analyst may choose one of the following methods:. When pulling together a sample, consider getting assistance from a colleague or independent person.

They may be able to identify biases or discrepancies you may not be aware of. A simple random sample is used to represent the entire data population. A stratified random sample divides the population into smaller groups, or strata, based on shared characteristics. Unlike simple random samples, stratified random samples are used with populations that can be easily broken into different subgroups or subsets.

These groups are based on certain criteria, then elements from each are randomly chosen in proportion to the group's size versus the population. This method of sampling means there will be selections from each different group—the size of which is based on its proportion to the entire population.

Researchers must ensure the strata do not overlap. Each point in the population must only belong to one stratum so each point is mutually exclusive. Overlapping strata would increase the likelihood that some data are included, thus skewing the sample. Systematic sampling entails selecting a single random variable, and that variable determines the internal in which the population items are selected.

For example, if the number 37 was chosen, the 37th company on the list sorted by CEO last name would be selected by the sample. Then, the 74th i. the next 37th and the st i. the next 37th after that would be added as well.

Simple random sampling does not have a starting point; therefore, there is the risk that the population items selected at random may cluster. In our example, there may be an abundance of CEOs with the last name that start with the letter 'F'.

Systematic sampling strives to even further reduce bias to ensure these clusters do not happen. Cluster sampling can occur as a one-stage cluster or two-stage cluster.

In a one-stage cluster, items within a population are put into comparable groupings; using our example, companies are grouped by year formed. Then, sampling occurs within these clusters. Two-stage cluster sampling occurs when clusters are formed through random selection. The population is not clustered with other similar items.

Then, sample items are randomly selected within each cluster. Simple random sampling does not cluster any population sets. Though sample random sampling may be a simpler, clustering especially two-stage clustering may enhance the randomness of sample items.

In addition, cluster sampling may provide a deeper analysis on a specific snapshot of a population which may or may not enhance the analysis. While simple random samples are easy to use, they do come with key disadvantages that can render the data useless. 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 sub-populations 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. Therefore, simple random sampling is known for its randomness and less chance of sampling bias.

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, men, and nonbinary people.

For this reason, simple random sampling is more commonly used when the researcher knows little about the population. If the researcher knew more, it would be better to use a different sampling technique, such as stratified random sampling, which helps to account for the differences within the population, such as age, race, or gender.

Other disadvantages include the fact that for sampling from large populations, the process can be time-consuming and costly compared to other methods. Researchers may find a certain project not worth the endeavor of its cost-benefit analysis does not generate positive results.

As every unit has to be assigned an identifying or sequential number prior to the selection process, this task may be difficult based on the method of data collection or size of the data set.

No easier method exists to extract a research sample from a larger population than simple random sampling.

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