Quantitative research studies use random sampling techniques. These are techniques that ensure that the individuals in a population have an equal chance of being included in the sample. This is important because the end goal of most quantitative research methods is to generalise the findings from the sample to the entire population. In this article, I discuss the various random sampling techniques used in quantitative studies.

- Sample size in quantitative research
- The sampling process in quantitative research
- Define the population of your study
- Define your sampling unit
- Choose your sampling method
- Simple random sampling method
- Systematic sampling method
- Cluster sampling method
- Stratified sampling method
- Determine your sample size
- Select your sample units until you reach your sample size

## Sample size in quantitative research

Random sample sizes in quantitative studies are determined through mathematical formulas.

The formulas to be used vary depending on how strong the researcher wants the findings to be and the level of variation in the population. Taking these aspects into consideration helps ensure that the sample is representative of the entire population by ensuring that the different sub-groups of the population are included in the sample.

Failure to account for all the sub-groups of the population in the sample will lead to sampling bias.

Sampling bias occurs when some members of the population have a higher or lower chance of being included in the sample than others.

Sampling bias leads to inaccurate data and hence it cannot be generalised to the entire population.

## The sampling process in quantitative research

There are several steps involved in coming up with a sample. These include:

### Define the population of your study

This will depend on the nature of your study.

A population of study is the group on which the researcher wants to base his study.

When defining the population of study, one must clearly specify the inclusion and exclusion criteria.

### Define your sampling unit

A sampling unit is the individual, category of people, objects, or organisations from whom data will be collected.

Examples: children below the age of 5, pregnant women, households, companies etc.

Sampling units are the building blocks of datasets.

Examples: in a household survey, the building block (sampling unit) would be households; in an enterprise survey, the building block (sampling units) would be small and medium enterprises

### Choose your sampling method

After defining your population and sampling unit, the next step is to decide which sampling method will be used to calculate the sample size.

There are four main random sampling methods that can be used:

#### Simple random sampling method

In this method, each unit in the study population has an equal chance of being included in the sample.

It includes randomly selecting the units until the desired sample size is obtained.

#### Systematic sampling method

In this method, all the units are listed and numbered and then units are selected systematically, for instance, every 10th unit, every 15th unit etc until the desired sample size is obtained.

#### Cluster sampling method

In this method, the population is divided into clusters that occur naturally, for instance, by districts, by cities etc.

A random sample of the clusters is then selected.

All the members in the selected clusters may be included in the sample or some members from the selected clusters may be selected to act as the sample for the clusters.

#### Stratified sampling method

In this method, the researcher divides the population into groups (strata) of interest, for instance, by gender, by income level, by level of education etc.

Units in each strata are then randomly selected to make up the sample.

This sampling method is used when the researcher wants to compare a phenomenon across the strata of interest.

Cluster sampling and stratified sampling often confuse people but the main difference is that it cluster sampling, the clusters are randomly selected and the units of the selected clusters are included in the sample. On the other hand, in stratified sampling, units in all the strata are selected randomly to make up the sample.

### Determine your sample size

The issue of sample size is important in quantitative research because of the need to generalise findings to the entire population of interest.

The larger the sample size, the higher the chances of the sample representing the population.

### Select your sample units until you reach your sample size

It is easy to select the sample units to include in the sample if the researcher has a sampling frame.

A sampling frame is a list of all the units that can be used to generate the sample.

Examples: a registration list of teachers, a registration list of students, a registration of small-and-medium enterprises, a census of individuals etc.

The sample units are selected based on the sampling method the researcher decided on.

In conclusion, random sampling is used in quantitative research where the main goal of the researcher is to generalise the findings from the sample to the entire population of study. There are four main random sampling techniques and they include: simple random sampling, systematic sampling, cluster sampling and stratified sampling. The choice of the random sampling method will depend on the aim and objectives of the study.

**Also read:**

Sampling Strategies for Qualitative Research