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Probability Sampling Methods
Josselyn, Marisol, Brianna
Non-Probability Sampling Methods
Probability Sampling Methods
Non-Probability Sampling Methods
Methods of sampling
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Transcript

Methods of sampling

Non-Probability Sampling Methods

Probability Sampling Methods

Non-Probability Sampling Methods

Josselyn, Marisol, Brianna

Simple Random Sampling

Systematic sampling

Snowball sampling

Convenience sampling

Quota sampling

Stratified sampling

Cluster sampling

Probability Sampling Methods

Judgement Sampling

Because participants are chosen based on their availability and willingness to participate, convenience sampling could be considered the easiest method. These findings are subject to significant bias since the sample may not be representative of other factors and because those who volunteer to participate may vary from those who chose not to

Simple random sample and systematic sampling are comparable, but systematic sampling is typically a little simpler to carry out. Every person in the population is assigned a number, but instead of assigning numbers at random, people are picked at predetermined intervals.

As more subjects who are known to the existing subjects are nominated, the sample grows in size like a snowball.When it is difficult to choose a random selection, snowball sampling may be useful. But still, there is a high chance of selection bias when choosing individuals who are friends of subjects who have already been examined.

Every person in the population has an equal probability of getting chosen in a simple random sample. The entire population should be included in your sampling frame.

Interviewers are instructed to try to recruit a certain number of participants of a particular category. The quotas selected should ideally proportionally reflect the traits of the underlying population. The chosen sample may not be typical of other factors that weren't taken into account, despite the fact that it is very simple and potentially representative.

Stratified sampling entails breaking the population up into smaller groups that might have significant differences. Ensuring that each subgroup is fairly represented in the sample, it enables you to reach more accurate findings. The population is divided into subgroups according to the relevant feature, such as gender identity, age range, income bracket, job type, etc

The population is also divided into smaller groups for cluster sampling, although each smaller group should share traits with the larger sample. You choose complete subgroups at random rather than picking a representative sample of each subgroup. Although there is a higher likelihood of mistakes in the sample, this strategy is effective for handling big and distant populations.

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The researcher's judgment will be used to select participants for this method. So, to meet their requirements, researchers may implicitly select a "normal" sample or target individuals who explicitly fit certain criteria.