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https://www.investopedia.com/terms/s/systematic-sampling.asp

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Systematic Sampling: What Is It, and How Is It Used in Research?

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Systematic sampling is a probability sampling method in which a random sample from a larger population is selected.

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1.Need for natural degree of randomness
A population needs to exhibit a natural degree of randomness along the chosen metric. If the population has a type of standardized pattern, the risk of accidentally choosing very common cases in more apparent.
2. Greater risk of data manipulation
3.There is a greater risk of data manipulation with systematic sampling, because researchers might be able to construct their systems to increase the likehood of achieving a targeted oucome.

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Advantages - captures key population, works well with a population with a variety of attributes.

Disadvantages - useless when it comes to a smaller population, can't be used in every study.

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Snowball sampling refers to the practice of randomly selecting some people and interviewing them, and then asking them to provide other survey objects belonging to the overall research target, and selecting the following survey objects according to the formed clues. Snowball sampling is often used to survey rare populations.

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the disadvantage of the convenience sample.

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the disadvantage of the convenience sample.

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One disadvantage of convenience sampling is that subjects in a convenience sample may not be representative of the population the researcher is interested in studying.

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If the population is small, it sometimes takes only a few visits to reach saturation, where subsequent visitors introduce people who have already visited.

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Quota sample is a kind of improbability sample, the researcher selects personnel according to certain criteria. That is, units are selected into a sample based on pre-specified features so that the total sample has the same distribution of features assumed to exist in the population under study.

For example, if you are a researcher conducting a sample of corporate quotas, you might need to know the proportion of the population that is male and the proportion that is female, work experience and academic level. If you collect a sample of the corporate population with the same proportion of those categories, you will get a quota sample.

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The samples can be controlled according to some sample characteristics, and it is suitable to find some people who are very rare in the overall population, which can greatly increase the possibility of contact with the investigation group. The cost is relatively low, and the feasibility is strong.

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Advantage
Ease of research
For researchers who are not looking for an accurate sampling, they can simply collect their information and move on to other aspects of their study. This type of sampling can be done by simply creating a questionnaire and distributing it to their targeted group. Through this method, researchers can easily finish collecting their data in a matter of hours, free from worrying about whether it is an accurate representation of the population. This allows for a great ease of research, letting researchers focus on analyzing the data rather than interviewing and carefully selecting participants.
Ready availability
Since most convenience sampling is collected with the populations on hand, the data is readily available for the researcher to collect. They do not typically have to travel great distances to collect the data, but simply pull from whatever environment is nearby. Having a sample group readily available is important for meeting quotas quickly, and allows for the researcher to even do multiple studies in an expeditious fashion.
Cost effectiveness
One of the most important aspects of convenience sampling is its cost effectiveness. This method allows for funds to be distributed to other aspects of the project. Oftentimes this method of sampling is used to gain funding for a larger, more thorough research project. In this instance, funds are not yet available for a more complete survey, so a quick selection of the population will be used to demonstrate a need for the completed project.

Disadvantages
Bias
The results of the convenience sampling cannot be generalized to the target population because of the potential bias of the sampling technique due to under-representation of subgroups in the sample in comparison to the population of interest. The bias of the sample cannot be measured. Therefore, inferences based on the convenience sampling should be made only about the sample itself.

Power
Convenience sampling is characterized with insufficient power to identify differences of population subgroups.

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strengths
-Random sampling is very convenient when working with small populations that have already been identified and listed.
-Simple random sampling means that every member of the population has an equal chance of being included in the study

weaknesses
-Giving every member of the population an equal chance at inclusion in a survey requires having a complete and accurate list of population members
-A simple random sample of the entire population might include only one or two of them, so it won't tell you anything.

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This process requires a close approximation of a population.

Some populations can detect the pattern of sampling.

A high risk of data manipulation exists.

Systematic sampling is less random than a simple random sampling effort.

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Systematic sampling is a type of probability sampling method in which sample members from a larger population are selected according to a random starting point but with a fixed, periodic interval. This interval, called the sampling interval, is calculated by dividing the population size by the desired sample size.

example: giving a survey to every fourth customer that comes in to the movie theater.

How do you do it?

1. Calculate the sampling interval (the number of households in the population divided by the number of households needed for the sample)

2. Select a random start between 1 and sampling interval.

3. Repeatedly add sampling interval to select subsequent households

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Systematic sampling also needs to be done in populations with natural randomness. Data will become skewed if it is taken from a group that already has a pattern.

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Quota sampling has several drawbacks. First, the quota frame—or the proportions in each category—must be accurate. This is often difficult because it can be hard to find up-to-date information on certain topics. For example, U.S. Census data is often not published until well after the data was collected, making it possible for some things to have changed proportions between data collection and publication.

Second, the selection of sample elements within a given category of the quota frame may be biased even though the proportion of the population is accurately estimated. For instance, if a researcher set out to interview five people who met a complex set of characteristics, he or she might introduce bias into the sample by avoiding or including certain people or situations. If the interviewer studying a local population avoided going to homes that looked particularly run-down or visited only homes with swimming pools, for example, their sample would be biased.

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Quota sampling is advantageous in that it can be fairly quick and easy to assemble a quota sample locally, which means it has the benefit of time-saving within the research process. A quota sample can also be achieved on a low budget because of this. These features make quota sampling a useful tactic for field research.

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Quota sampling is where you take a customized  sample in order look for a specific characteristic of a population.
In order for the quota sampling to be accurate they should:

1. Divide the sample population into subgroups.

2. Figure out the weightages of subgroups.

3. Select an appropriate sample size.

4. Survey while adhering to the subgroup population proportions.

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what:
If a population contains N individuals, a sampling method is called simple random sampling if a sample is drawn from the population individually and each individual is equally likely to be selected each time.

how:
1: Define the population
2: Decide on the sample size
3: Randomly select your sample
4: Collect data from your sample

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A convenience sample is a type of non-probability sampling method where the sample is taken from a group of people easy to contact or to reach. For example, standing at a mall or a grocery store and asking people to answer questions would be an example of a convenience sample. This type of sampling is also known as grab sampling or availability sampling. There are no other criteria to the sampling method except that people be available and willing to participate. In addition, this type of sampling method does not require that a simple random sample is generated, since the only criterion is whether the participants agree to participate.

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A method of sampling involves a breaking-up of a population into smaller groups, known as a "strata". The factors influencing the strata are income and educational fulfillment.
The importance of stratified random sampling is that it allows researchers to obtain the most accurate information about the target population, for more beneficial research.

https://www.investopedia.com/terms/stratified_random_sampling.asp

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What Is Stratified Random Sampling?

Stratified random sampling is a method of sampling that involves the division of a population into smaller subgroups known as strata. In stratified random sampling, or stratification, the strata are formed based on members' shared attributes or characteristics, such as income or educational attainment. Stratified random sampling has numerous applications and benefits, such as studying population demographics and life expectancy.

Stratified random sampling is also called proportional random sampling or quota random sampling.

How Stratified Random Sampling Works

When completing analysis or research on a group of entities with similar characteristics, a researcher may find that the population size is too large to complete research on it. To save time and money, an analyst may take on a more feasible approach by selecting a small group from the population. The small group is referred to as a sample size, which is a subset of the population used to represent the entire population. A sample may be selected from a population through a number of ways, one of which is the stratified random sampling method.

Stratified random sampling involves dividing the entire population into homogeneous groups called strata (plural for stratum). Random samples are then selected from each stratum. For example, consider an academic researcher who would like to know the number of MBA students in 2021 who received a job offer within three months of graduation.

The researcher will soon find that there were almost 200,000 MBA graduates for the year. They might decide just to take a simple random sample of 50,000 graduates and run a survey. Better still, they could divide the population into strata and take a random sample from the strata. To do this, they would create population groups based on gender, age range, race, country of nationality, and career background. A random sample from each stratum is taken in a number proportional to the stratum's size compared with the population. These subsets of the strata are then pooled to form a random sample.

Example of Stratified Random Sampling

Suppose a research team wants to determine the grade point average (GPA) of college students across the United States. The research team has difficulty collecting data from all 21 million college students; it decides to take a random sample of the population by using 4,000 students.

Now assume that the team looks at the different attributes of the sample participants and wonders if there are any differences in GPAs and students' majors. Suppose it finds that 560 students are English majors, 1,135 are science majors, 800 are computer science majors, 1,090 are engineering majors, and 415 are math majors. The team wants to use a proportional stratified random sample where the stratum of the sample is proportional to the random sample in the population.

Assume the team researches the demographics of college students in the U.S. and finds the percentage of what students major in: 12% major in English, 28% major in science, 24% major in computer science, 21% major in engineering, and 15% major in mathematics. Thus, five strata are created from the stratified random sampling process.

The team then needs to confirm that the stratum of the population is in proportion to the stratum in the sample; however, they find the proportions are not equal. The team then needs to resample 4,000 students from the population and randomly select 480 English, 1,120 science, 960 computer science, 840 engineering, and 600 mathematics students.

With those groups, it has a proportionate stratified random sample of college students, which provides a better representation of students' college majors in the U.S. The researchers can then highlight specific stratum, observe the varying types of studies of U.S. college students and observe the various GPAs.

Simple vs. Stratified Random Samples

Simple random samples and stratified random samples are both statistical measurement tools. 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. However, stratified sampling is more complicated, time consuming, and potentially more expensive to carry out than simplified random sampling.

The simple random sample is often used when there is very little information available about the data population, when the data population has far too many differences to divide into various subsets, or when there is only one distinct characteristic among the data population.

For instance, a candy company may want to study the buying habits of its customers to determine the future of its product line. If there are 10,000 customers, it may use choose 100 of those customers as a random sample. It can then apply what it finds from those 100 customers to the rest of its base. Unlike stratification, it will sample 100 members purely at random without any regard for their individual characteristics.

Proportionate and Disproportionate Stratification

Stratified random sampling ensures that each subgroup of a given population is adequately represented within the whole sample population of a research study. Stratification can be proportionate or disproportionate. In a proportionate stratified method, the sample size of each stratum is proportionate to the population size of the stratum. This type of stratified random sampling is often a more precise metric because it's a better representation of the overall population.

For example, if the researcher wanted a sample of 50,000 graduates using age range, the proportionate stratified random sample will be obtained using this formula: (sample size/population size) × stratum size. The table below assumes a population size of 180,000 MBA graduates per year.

The strata sample size for MBA graduates in the age range of 24 to 28 years old is calculated as (50,000/180,000) × 90,000 = 25,000. The same method is used for the other age-range groups. Now that the strata sample size is known, the researcher can perform simple random sampling in each stratum to select his survey participants. In other words, 25,000 graduates from the 24-28 age group will be selected randomly from the entire population, 16,667 graduates from the 29-33 age range will be selected from the population randomly, and so on.

In a disproportional stratified sample, the size of each stratum is not proportional to its size in the population. The researcher may decide to sample half of the graduates within the 34-37 age group and one-third of the graduates within the 29-33 age group.

It is important to note that one person cannot fit into multiple strata. Each entity must only fit in one stratum. Having overlapping subgroups means that some individuals will have higher chances of being selected for the survey, which completely negates the concept of stratified sampling as a type of probability sampling.

Portfolio managers can use stratified random sampling to create portfolios by replicating an index such as a bond index.

Advantages of Stratified Random Sampling

The main advantage of stratified random sampling is that it captures key population characteristics in the sample. Similar to a weighted average, this method of sampling produces characteristics in the sample that are proportional to the overall population. Stratified random sampling works well for populations with a variety of attributes but is otherwise ineffective if subgroups cannot be formed.

Stratification gives a smaller error in estimation and greater precision than the simple random sampling method. The greater the differences among the strata, the greater the gain in precision.

Disadvantages of Stratified Random Sampling

Unfortunately, this method of research cannot be used in every study. The method's disadvantage is that several conditions must be met for it to be used properly. Researchers must identify every member of a population being studied and classify each of them into one, and only one, subpopulation. As a result, stratified random sampling is disadvantageous when researchers can't confidently classify every member of the population into a subgroup. Also, finding an exhaustive and definitive list of an entire population can be challenging.

Overlapping can be an issue if there are subjects that fall into multiple subgroups. When simple random sampling is performed, those who are in multiple subgroups are more likely to be chosen. The result could be a misrepresentation or inaccurate reflection of the population.

The above examples make it easy: Undergraduate, graduate, male, and female are clearly defined groups. In other situations, however, it might be far more difficult. Imagine incorporating characteristics such as race, ethnicity, or religion. The sorting process becomes more difficult, rendering stratified random sampling an ineffective and less-than-ideal method.

When would you use stratified random sampling?

Stratified random sampling is often when researchers want to know about different subgroups or strata based on the entire population being studied-for instance, if one is interested in differences among groups based on race, gender, or education.

Which sampling method is best?

The method of sampling best to use will depend on the nature of the analysis and the data being used. In general, simple random sampling is often the easiest and cheapest, but stratified sampling can produce a more accurate sample relative to the population under study.

What are the two types of stratified random sampling?

Proportionate sampling takes each stratum in the sample as proportionate to the population size of the stratum. In disproportionate sampling, the analyst will over- or under-sample certain strata based on the research question or study design that they are employing. For example, those interested in childhood education outcomes may over-sample school-age children and those in their early work lives while under-sampling younger and older strata.

How are strata chosen for stratified random sampling?

The strata will depend on the subgroups in which you are interested that appear in your population. These subgroups are based on shared differences among participant characteristics such as gender, race, educational attainment, geographic location, or age group.

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How Stratified Random Sampling Works, with Examples

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Stratified random sampling is a method of sampling that involves the division of a population into smaller groups known as strata.

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Provides results quickly: In cases when time is limited and you need to collect data fast, convenience sampling could be the most fast method for researcher to gain the date or example.

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Convenience sampling is a key aspect of research. Learn how to make the most of convenience sampling as well as other effective research strategies here!

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Stratified random sample allows researchers to obtain a sample population that can represent the entire population.

It divides the population into homogeneous groups called strata.

It involves the random selection of data from an entire population, so each possible sample is equally likely to occur.

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Stratified random sampling is a method of sampling that involves the division of a population into smaller sub-groups known as strata. This way could cover different kind of people who have different characteristics. And know can the the precent of different people who have the characteristcs in the whole sample.

Unfortunately, this method of research cannot be used in every study. The method's disadvantage is that several conditions must be met for it to be used properly.