# Types of Sampling

In this post, we will discuss the various types of sampling that are used in practice. We will start with a brief overview of the different types of sampling and then move on to more detailed discussions about each type.

There are two types of sampling techniques: probability sampling and non-probability sampling.

### 1. Probability Samples

- Everyone in the population has an equal chance of being selected

### 2. Non-Probability Samples

- Where the probability of selection can't be accurately determined.
- The sample may not be (generally isn’t) representative of the general population

## Types of Sampling

Probability and non-probability sampling could be further subdivided into:

### 1. Probability Sampling

Probability sampling is the most commonly used form of sampling, in which every member of the population has an equal chance of being selected for inclusion in the sample. This type of sampling can be done by choosing people at random from a list or by using some other method that ensures that all members of the population have an equal chance of being chosen. There are four types of probability sampling techniques:

### A. Simple Random Sampling

- Each item in the population has an equal chance of being selected.
- Examples: Using random tables, Random draw of the lot (lottery)

### B. Systematic Sampling

- Select elements at regular intervals through that ordered list.
- Example: Checking every 6th piece produced by the machine.

### C. Stratified Sampling

- Selection is based on characteristics that divide the population into groups.
- Used to ensure that sub-groups within a population are represented proportionally in the sample.
- Example: If 10 people are drawn to represent a country, 5 of them are male and 5 females to avoid sex bias.

### D. Cluster Sampling

- Selection occurs within clusters or small areas of the population.
- sometimes it is more cost-effective to select respondents in groups ('clusters'). Sampling is often clustered by geography, or by time periods.
- Example: Survey all customers visiting particular stores on particular days.

### 2. Non-probability Sampling

Non-probability sampling is when there is no guarantee that each element in the population will be equally likely to be included in the sample. It is usually used where the number of samples needed is too large to allow for probability sampling. The main types of non-probability samples include convenience sampling, judgemental sampling and quota sampling.

### A. Convenience Sampling

- This method involves selecting participants from a convenient sample.
- It's usually done when you want to get as many responses as possible quickly.
- Convenience samples are typically biased towards those who agree with your research question.
- Example: Conducting a survey among friends and family members.

### B. Judgemental Sampling

- Judgemental sampling is similar to convenience sampling but uses judgement rather than availability.
- The researcher chooses the sample based on who they think would be appropriate for the study.
- Example: Auditor selects a sample based on the concerns he/she had in the earlier audit
- Example: Choosing students who have been absent from school for more than 2 weeks

### C. Quota Sampling

- Quota sampling is a form of non-probability where the total number of items needed is known beforehand.
- In this case, the researcher identifies the target group and then randomly selects a percentage of the group.
- Example: A university wants to conduct a survey about student attitudes towards their courses. They know that there are around 1000 students enrolled each year, so they decide to randomly select 200 students from each year.

## Conclusion:

Sampling can be very useful if you need a representative sample of a population. However, it has some limitations such as:

1. Selection bias - the sample may not reflect the whole population.

2. Sample size - how big should the sample be?

3. Cost - how much does it cost to collect data?

4. Time - how long does it take to collect data?

5. Representativeness - what kind of sample do we need?

6. Response rate - how many people actually respond to our surveys?

7. Accessibility - how accessible was the sample?