Sampling techniques
In research, it is often impractical to study an entire population due to constraints such as time, cost, and accessibility. This is where sampling techniques come in. In addition, choosing the right sampling method is essential to ensure accurate, reliable, and valid research results. This lesson introduces various sampling techniques and guides you in selecting the most appropriate method for your study.
Lesson objectives
At the end of the lesson, you should be able to:
- explain the concepts in sampling such as population, sample, probability and non-probability sampling;
- determine the appropriate sampling technique and sample size in a given problem
Population
- The research population, also known as the target population, refers to the entire group or set of individuals, objects, or events that possess specific characteristics and are of interest to the researcher. It represents the larger population from which a sample is drawn (Thomas, 2023).
- A parameter is a numeric characteristic of a population.
Sample
- It is impossible to collect data from the entire population (Barrot, 2017).
- A sample is a subset of the research population that is carefully selected to represent its characteristics (Thomas, 2023).
- A statistic is a numeric characteristic of a sample.
Advantages and disadvantages of using a sample
Source: Cristobal & dela Cruz-Cristobal, 2016.
Sampling
- It is the process of the selecting units from population of interest so that by studying the sample we may fairly generalize our results back to the population from which they were chosen.
Sampling methods or sampling techniques in research are statistical methods for selecting a sample representative of the whole population to study the population’s characteristics. Sampling methods serve as invaluable tools for researchers, enabling the collection of meaningful data and facilitating analysis to identify distinctive features of the people. Different sampling strategies can be used based on the characteristics of the population, the study purpose, and the available resources. (Bisht, 2023)
The primary purpose of sampling is the selection of suitable participants to enable the focus of the study to be appropriately researched. As with all types of research, effective sample selection is a vital part of the research design process. Inappropriate sampling approaches may seriously affect the findings and outcomes of a study. There are a number of types of sampling procedures that can be adopted and the choice of the qualitative research design will often guide that process.
Factors to consider in determining the sample size
- Homogeneity of the population. The higher the degree of homogeneity of the population, the smaller the sample size that can be utilized.
- Degree of precision desired by the researcher. The larger the sample size, the higher the precision or accuracy of the results will be.
- Types of sampling procedure. Probability sampling uses smaller sample sizes than nonprobability sampling.
Various approaches to determining the sample size
- Sample sizes as small as 30 are generally adequate to ensure that the sampling distribution of the mean will approximate the normal curve (Shott, 1990).
- When the total population is equal to or less than 100, this same number may serve as the sample size. This is called universal sampling.
- Slovin’s formula is used to compute for sample size (Sevilla, 2003)
n = N / (1 + Ne²)
where: n – a sample size
N – population size
e – desired margin of error
Example: The population total is 8,000 with a desired 2% margin of error
n = N / (1 + Ne²)
= 8,000 / (1 + 8,000(0.02)²)
= 8,000 / (1 + 8,000(0.0004))
= 8,000 / (1 + 3.2)
= 8,000 / 4.2
= 1,905
There are two most common sampling methods:
1. Probability sampling
A sampling method in which each unit or element in the population has an equal chance of being selected in the final sample. This is called random sampling, emphasizing the random and non-zero probability nature of selecting samples. Such a sampling technique ensures a more representative and unbiased sample, enabling robust inferences about the entire population.
Probability sampling types
A. Simple Random Sampling
In simple random sampling technique, every item in the population has an equal and likely chance of being selected in the sample. Since the item selection entirely depends on the chance, this method is known as “Method of chance Selection”. As the sample size is large, and the item is chosen randomly, it is known as “Representative Sampling”.
Example: Suppose we want to select a simple random sample of 200 students from a school. Here, we can assign a number to every student in the school database from 1 to 500 and use a random number generator to select a sample of 200 numbers.
B. Systematic Sampling
In the systematic sampling method, the items are selected from the target population by selecting the random selection point and selecting the other methods after a fixed sample interval. It is calculated by dividing the total population size by the desired population size.
Example: Suppose the names of 300 students of a school are sorted in the reverse alphabetical order. To select a sample in a systematic sampling method, we have to choose some 15 students by randomly selecting a starting number, say 5. From number 5 onwards, will select every 15th person from the sorted list. Finally, we can end up with a sample of some students.
C. Stratified Sampling
In a stratified sampling method, the total population is divided into smaller groups to complete the sampling process. The small group is formed based on a few characteristics in the population. After separating the population into a smaller group, the statisticians randomly select the sample.
Example: There are three bags (A, B and C), each with different balls. Bag A has 50 balls, bag B has 100 balls, and bag C has 200 balls. We have to choose a sample of balls from each bag proportionally. Suppose 5 balls from bag A, 10 balls from bag B and 20 balls from bag C.
D. Clustered Sampling
In the clustered sampling method, the cluster or group of people are formed from the population set. The group has similar significatory characteristics. Also, they have an equal chance of being a part of the sample. This method uses simple random sampling for the cluster of population.
Example: An educational institution has ten branches across the country with almost the number of students. If we want to collect some data regarding facilities and other things, we can’t travel to every unit to collect the required data. Hence, we can use random sampling to select three or four branches as clusters.
2. Non-probability sampling
Another sampling method is non-probability sampling, which involves collecting data conveniently through a non-random selection based on predefined criteria. This offers a straightforward way to gather data, although the resulting sample may or may not accurately represent the entire population.
Non-probability sampling types
A. Convenience Sampling
In a convenience sampling method, the samples are selected from the population directly because they are conveniently available for the researcher. The samples are easy to select, and the researcher did not choose the sample that outlines the entire population.
Example: In researching customer support services in a particular region, we ask your few customers to complete a survey on the products after the purchase. This is a convenient way to collect data. Still, as we only surveyed customers taking the same product. At the same time, the sample is not representative of all the customers in that area.
B. Quota Sampling
In the quota sampling method, the researcher forms a sample that involves the individuals to represent the population based on specific traits or qualities. The researcher chooses the sample subsets that bring the useful collection of data that generalizes the entire population.
C. Purposive or Judgmental Sampling
In purposive sampling, the samples are selected only based on the researcher’s knowledge. As their knowledge is instrumental in creating the samples, there are the chances of obtaining highly accurate answers with a minimum marginal error. It is also known as judgmental sampling or authoritative sampling.
D. Snowball Sampling
Snowball sampling is also known as a chain-referral sampling technique. In this method, the samples have traits that are difficult to find. So, each identified member of a population is asked to find the other sampling units. Those sampling units also belong to the same targeted population.
E. Consecutive Sampling
Consecutive sampling is similar to convenience sampling with a slight variation. The researcher picks a single person or a group of people for sampling. Then the researcher research for a period of time to analyze the result and move to another group if needed.