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.” (Proctor, 2003, p.100).


Brown (2006) summarizes the advantages of sampling in the following points:
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.


In researches involving primary data collection sampling can be done following four stages explained below:


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 a 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.



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 judgement, quota, convenience and extensive sampling methods. The following figure illustrates specific sampling methods belonging to each category:




Brief definitions and advantages and disadvantages of sampling techniques are illustrated on the following table:


Technique Definition/


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



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

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


SamplingMy e-book, The Ultimate Guide to Writing a Dissertation in Business Studies: a step by step assistance offers practical assistance to complete a dissertation with minimum or no stress. The e-book covers all stages of writing a dissertation starting from the selection of the research area to submitting the completed version of the work before the deadline.


John Dudovskiy