Probability Sampling

Probability Sampling

Saint Francis of Assisi College & Saint Anthony School

Open Ended

What is your initial knowledge about sampling?

Open Ended

What do you want to know about sampling?

Sampling

In research, sampling is a word that refers to your method or process of selecting respondents or people to answer questions meant to yield data for a research study.

Subject | Subject

Some text here about the topic of discussion

Sampling

The chosen one constitutes the sample through which you will derive facts and evidence to support the claims or conclusions propounded by your research problem.

Subject | Subject

Some text here about the topic of discussion

​A population is the entire group that you want to draw conclusions about.

A sample is the specific group that you will collect data from. The size of the sample is always less than the total size of the population.

Population vs. Sample

In research, a population doesn’t always refer to people. It can mean a group containing elements of anything you want to study, such as objects, events, organizations, countries, species, organisms, etc.

Population vs. Sample

Multiple Choice

Why do we randomly select our samples?

• for control

• reduce bias

• so we don't get the same answers from everyone

• to eliminate confounding results

Types of Probability Sampling

Multiple Choice

A part of the population is called a _____________.

• biased sample

• random sample

• sample

• convenience sample

1. Simple random sampling

In a simple random sample, every member of the population has an equal chance of being selected. Your sampling frame should include the whole population. To conduct this type of sampling, you can use tools like random number generators or other techniques that are based entirely on chance.

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Example: You want to select a simple random sample of 100 employees of Company X. You assign a number to every employee in the company database from 1 to 1000, and use a random number generator to select 100 numbers.​

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2. Systematic sampling

Systematic sampling is similar to simple random sampling, but it is usually slightly easier to conduct. Every member of the population is listed with a number, but instead of randomly generating numbers, individuals are chosen at regular intervals.​

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If you use this technique, it is important to make sure that there is no hidden pattern in the list that might skew the sample. For example, if the HR database groups employees by team, and team members are listed in order of seniority, there is a risk that your interval might skip over people in junior roles, resulting in a sample that is skewed towards senior employees.​

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3. Stratified Sampling

Stratified sampling involves dividing the population into subpopulations that may differ in important ways. It allows you draw more precise conclusions by ensuring that every subgroup is properly represented in the sample.

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To use this sampling method, you divide the population into subgroups (called strata) based on the relevant characteristic (e.g. gender, age range, income bracket, job role).

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Based on the overall proportions of the population, you calculate how many people should be sampled from each subgroup. Then you use random or systematic sampling to select a sample from each subgroup.

4. Cluster Sampling

Cluster sampling also involves dividing the population into subgroups, but each subgroup should have similar characteristics to the whole sample. Instead of sampling individuals from each subgroup, you randomly select entire subgroups.

If it is practically possible, you might include every individual from each sampled cluster. If the clusters themselves are large, you can also sample individuals from within each cluster using one of the techniques above. This is called multistage sampling.

4. Cluster Sampling

This method is good for dealing with large and dispersed populations, but there is more risk of error in the sample, as there could be substantial differences between clusters. It’s difficult to guarantee that the sampled clusters are really representative of the whole population.

Multiple Choice

A teacher splits her classes up by period. She then randomly picks 3 students from each period to do a survey.

• Simple Random Sampling

• Systematic Random Sampling

• Cluster Sampling

• Stratified Random Sampling

Multiple Choice

A teacher wants to know how well her students are doing on a topic. She randomly picks one class to survey.

• Simple Random Sampling

• Systematic Random Sampling

• Cluster Sampling

• Stratified Random Sampling

Multiple Choice

An independent research company wants to go door to door to survey people in the city of Fontana. The company decides to number all blocks within the city limit, randomly choose 1 block and survey all households on that block.

This is an example of:

• Simple Random Sampling

• Systematic Random Sampling

• Cluster Sampling

• Stratified Random Sampling

Multiple Choice

A teacher wants to know the average time spent doing homework by the students in her class of 20 girls and 5 boys.

She picks the student in every 5th seat.

• Simple Random Sampling

• Systematic Random Sampling

• Cluster Sampling

• Stratified Random Sampling