Sampling

Sampling is the process of selecting a subset of individuals from a population in order to represent that population in a research study. It allows researchers to draw conclusions efficiently without studying the entire population.

On this page:

• What is Sampling?
• Sampling Process Step-by-Step
• Types of Sampling Methods
• Probability vs Non-Probability Sampling
• When to Use Different Sampling Methods

 

Aspect	Probability Sampling	Non-Probability Sampling
Selection method	Random	Non-random
Bias level	Low	Higher
Representativeness	High	Limited
Generalisation	Possible	Limited
Examples	Random, stratified	Convenience, purposive

Probability vs Non-probability sampling

Probability sampling aims for representativeness, whereas non-probability sampling focuses on practicality and accessibility.

Sampling involves:

• Defining who you want to study (population)
• Selecting a smaller group (sample)
• Using that group to represent the whole population

It allows research to be feasible, efficient, and manageable.

 

What is Sampling?

Sampling can be explained as a specific principle used to select members of population to be included in the study. It has been rightly noted that “because many populations of interest are too large to work with directly, techniques of statistical sampling have been devised to obtain samples taken from larger populations.”[1].

In other words, due to the large size of target population, researchers have no choice but to study the a number of cases of elements within the population to represent the population and to reach conclusions about the population (see Figure 1 below).

Figure 1. Population, sample and individual cases[2]

Brown (2006) summarizes the advantages of sampling in the following points[3]:
a) Makes the research of any type and size manageable;
b) Significantly saves the costs of the research;
c) Results in more accurate research findings;
d) Provides an opportunity to process the information in a more efficient way;
e) Accelerates the speed of primary data collection.

 

The Process of Sampling in Primary Data Collection

The process of sampling in primary data collection involves the following stages:

1. Defining target population. Target population represent specific segment within wider population that are best positioned to serve as a primary data source for the research. For example, for a dissertation entitled ‘Impact of social networking sites on time management practices amongst university students in the UK” target population would consist of individuals residing in the UK.

2. Choosing sampling frame. Sampling frame can be explained as a list of people within the target population who can contribute to the research. For a sample dissertation named above, sampling frame would be an extensive list of UK university students.

3. Determining sampling size. This is the number of individuals from the sampling frame who will participate in the primary data collection process. The following observations need to be taken into account when determining sample size:

a) The magnitude of sampling error can be diminished by increasing the sample size.

b) There are greater sample size requirements in survey-based studies than in experimental studies.

c) Large initial sample size has to be provisioned for mailed questionnaires, because the percentage of responses can be as low as 20 to 30 per cent.

d) The most important factors in determining the sample size include subject availability and cost factors

For example, for the same research of ‘Impact of social networking sites on time management practices amongst university students in the UK’ sample size could be determined to include 200 respondents.

4. Selecting a sampling method. This relates to a specific method according to which 200 university students in the UK are going to be selected to participate in research named above.

5. Applying the chosen sampling method in practice.

 

Types of Sampling Methods

Sampling methods are broadly divided into two categories: probability and non-probability.

In probability sampling every member of population has a known chance of participating in the study. Probability sampling methods include simple, stratified systematic, multistage, and cluster sampling methods.

In non-probability sampling, on the other hand, sampling group members are selected on non-random manner, therefore not each population member has a chance to participate in the study. Non-probability sampling methods include purposive, quota, convenience and snowball sampling methods. The Figure 2 below  illustrates specific sampling methods belonging to each category:

Figure 2. Categorisation of sampling techniques

The following table illustrates brief definitions, advantages and disadvantages of sampling techniques:

Technique	Definition/ Explanation	Advantages	Disadvantages
Random	Sample group members are selected in a random manner	Highly effective if all subjects participate in data collection	High level of sampling error when sample size is small
Stratified	Representation of specific subgroup or strata	Effective representation of all subgroups Precise estimates in cases of homogeneity or heterogeneity within strata	Knowledge of strata membership is required Complex to apply in practical levels
Systematic	Including every Nth member of population in the study	Time efficient   Cost efficient	High sampling bias if periodicity exists
Multistage	Sampling conducted on several stages	High level of flexibility at various levels	Complex to conduct Impacted by limitations of cluster and stratified sampling methods
Cluster	Clusters of participants representing population are identified as sample group members	Time efficient   Cost efficient	Group-level information needs to be known Usually higher sampling errors compared to alternative sampling methods
Judgement	Sample group members are selected on the basis of judgement of researcher	Time efficiency   Samples are not highly representative	Unscientific approach   Personal bias
Quota	Sample group members are selected on the basis of specific criteria	High level of reliability than random sampling Usually cost-effective	High level of subjectivity   Difficult to estimate sampling error
Convenience	Obtaining participants conveniently with no requirements whatsoever	High levels of simplicity and ease   Usefulness in pilot studies	Highest level of sampling error   Selection bias
Snowball	Sample group members nominate additional members to participate in the study	Possibility to recruit hidden population	Over-representation of a particular network Reluctance of sample group members to nominate additional members

 

When to Use Different Sampling Methods

The choice of sampling method depends on your research aim, available resources, and the need for generalisation.

You should:

• Use probability sampling when your research requires statistical analysis and generalisable findings
• Use non-probability sampling when conducting exploratory research or pilot studies
• Use random sampling methods when you want to minimise bias
• Use purposive or convenience sampling when access to participants is limited
• Consider cost, time, and access constraints when selecting a sampling strategy

 

Choose sampling based on whether you need accuracy (probability) or practicality (non-probability).

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John Dudovskiy

[1] Proctor, T. (2003) “Essentials of Marketing Research”, 3rd edition, Prentice Hall

[2] Source: Saunders, M., Lewis, P. & Thornhill, A. (2012) “Research Methods for Business Students” 6^th edition, Pearson Education Limited

[3] Brown, R.B. (2006) “Doing Your Dissertation in Business and Management: The Reality of Research and Writing” Sage Publications