NR 505 COMPLETE WEEKLY DISCUSSIONS NEW Week 4 Discussion Latest

Sampling Issues (graded)

What are the various types of sampling, along with their advantages and disadvantages? As we think about sampling and ethical practices, review these two documents on ethical issues in research: Tuskegee syphilis study at http://www.cdc.gov/tuskegee/ An overview of questionable studies: Resnik, D. (2012). Research ethics timeline (1932-present). Retrieved from http://www.niehs.nih.gov/research/resources/bioethics/timeline/index.cfm What are the implications of this information with respect to the recruitment of human subjects in a study related to your chosen issue?

ADDITIONAL INFORMATION 

The various types of sampling

Introduction

Sampling is a method of collecting data that uses a random subset of the whole population. Sampling is often used in surveys and other types of research studies because it can be more efficient and cost-effective than a traditional method like random selection. However, there are many different ways to perform sampling—and each one has its own advantages and drawbacks. In this article, we’ll explore some of these variations so that you can choose the best approach for your research project!

Simple random sampling

Simple random sampling is the most common method of selecting a subset of the population in which every member of the population has an equal chance of being selected. The selection process begins with listing all possible items or events, then selecting one at random from those that have been listed. If you’re trying to determine who voted for president last year and want to find out how many people voted Republican or Democrat, you’d use simple random sampling because this method randomly samples citizens who could have voted either way (Republican or Democrat).

Stratified sampling

Stratified sampling is a form of cluster sampling in which you stratify the population into subgroups, and then select each group as part of your sample. This helps reduce bias and increase representativeness by ensuring that each subgroup is represented in equal numbers in your sample. For example, if you were interested in finding out how many people are married or single at age 40+, then using stratified random sampling would help ensure that these two groups (married vs single) were not over-represented on average compared with other age groups.

Cluster sampling

Cluster sampling is a type of probability sampling. It is used when the population being studied is not evenly distributed across space or time, but instead has a tendency to cluster into groups. For example, if you’re collecting data on people who live in your neighborhood, it may be more efficient to select one person from each household than it would be to select random samples of households at large intervals.

In this case, you’ll need some way of assigning each household its own number (or “cluster”). Clustering allows us to estimate how many people there are in total within that region; this information can then be used with simple random sampling methods like so:

Systematic sampling

Systematic sampling is the most common type of sampling. In this method, you select an element from the population at random and then select every nth element thereafter. For example, if there are 10 elements in your sample, then each time you select one element it will be randomly selected with replacement from those 10 elements. You can also choose to choose only one element at a time or all ten elements simultaneously; however, we’ll focus on selecting only one at a time here because that’s what we’ll do with our example (this will become clearer later).

If systematic sampling were used in our toy example above where we wanted to know how many different colors there were overall but didn’t have enough money for full DNA sequencing—there would still be some uncertainty about whether or not any particular color was unique within itself because now there may be multiple versions of each color floating around! If we knew beforehand exactly how many colors existed (which isn’t possible), then it wouldn’t matter which ones happened to show up first when measuring how many times each color showed up within the set; however since we could never tell which specific ones would turn up first based on prior knowledge alone , randomization was necessary so as not guarantee any particular outcome unless all possibilities were considered equally likely .

Convenience sampling

Convenience sampling is a type of non-random sampling that can be used when you have a small group of people to sample from. Convenience sampling involves selecting a sample based on convenience, rather than randomness. This might mean that you choose the first person who answers your phone call or knocks on your door and asks them if they’d like to participate in your study (although this would not necessarily be considered convenience). You might also choose someone based on their gender or ethnicity—this cannot always be done with perfect accuracy, but it may help reduce bias in your results.

Judgment sampling

Judgment sampling is used when the population is not known. The sample is selected based on the judgment of the researcher, who then chooses a subset of individuals from whom they will draw conclusions. Examples of judgment samples include convenience samples and self-selected samples (self-selected means that respondents were asked to pick themselves).

There are a lot of different types of samples.

There are a lot of different types of samples. It’s important to know what you’re getting into when you start sampling, so it’s worth taking the time to understand some basic concepts.

• Simple Random Sampling (SRS) is one way to do this, and it’s a great way to start out if you’re new to sampling or just want an easy way to get started with your research project. In SRS, every member in the population has an equal chance of being selected for the sample—for example, if there are 100 people who live in this neighborhood then each person would be picked once independently from everyone else. This type of sampling technique can also help researchers control bias by ensuring that only people who actually exist in reality will be included in their study population; however, because these kinds of studies are often done by researchers who don’t know anything about what they’re doing yet must rely on random selection methods like flipping coins until someone wins (or loses), sometimes these samples can lead us astray when we try using them later on down the road!

Conclusion

We hope this guide has taught you more about how to choose the right sampling method for your study. As you can see, there are many different techniques out there and it’s important to know which ones will work best for your project. The key is finding one that fits in with both your goals and research questions!

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