Types of sampling methods in research

1. SAMPLING METHODS
DR HARDEV SINGH s/o HARPAL SINGH

2. INTRODUCTION
• Sampling is a method or process of selecting respondents or people to answer
questions meant to yield data for field study (Baraceros, 2016)
• Sampling is a method of selecting a subset or individual members of the
population to make statistical inferences from them and estimate characteristics of
the whole population.
• The sample selected should be representative of the population to ensure that we
can generalize the findings from the research sample to the population as a whole.

3. TYPE OF SAMPLING METHODS
• Type of sampling methods can be subdivided into two groups: probability sampling and
non-probability sampling.
• Probability Sampling : It is a sampling technique in which sample from larger population
are chosen using a method based upon theory of probability . For a participants to be
considered as probability sample, he or she must be selected using random selection
(Bhat,2019).
• It start with a complete sampling frame of all eligible individuals from which has been
selected for the sample.
• Non Probability Sampling: It is a sampling technique where samples are gathered in
process that does not give all individuals equal change of being selected.
• It does not start with a complete sampling frame, so some individuals have no chance of
being selected.

4. Type of Probability Sampling
1) Simple Random Sampling
It is a subset of statistical population in which each member of the subset has an equal probability of
being chosen.
Example: Write name in papers and fold then randomly mix and select the names.
This method allows the sampling error to be calculated and reduces selection bias but this simple
random sampling method may not select enough individuals with interest of certain characteristic ,
especially if that characteristic is uncommon.
2)Systematic Sampling
It is a method which sample members from larger population are selected according to a random
starting point and a fixed periodic interval.
Example: Population of 1000 , sample size 100, every 100th person in the list is selected.
Systematic Sampling is easier to administer but may also lead to bias, for example if there are
underlying patterns in the order of the individuals in the sampling frame, such that the sampling
technique coincides with the periodicity of the underlying pattern. For example , choosing every 5th
road user for road hazard in a college would result bias in sample of all males or all females.

5. 3) Stratified Sampling
• It is a type of sampling method in which total populations is divided into a smaller group
or strata to complete the sampling process.
• Example: Population size 1000, Sample size 100, group population by age then get the
sample by age.
• Samples within should be randomly selected for example, in a study of the health
outcomes of nursing staff in Malaysia, if to select from three hospitals, each with
different numbers of nursing staff (hospital A has 500 nurses, hospital B has 1000 and
hospital C has 2000), then it would be appropriate to choose the sample numbers from
each hospital proportionally as 10 nurses from hospital A, 20 nurses from hospital B and
40 nurses from hospital C.
• This ensures a more realistic and accurate estimation of the health outcomes of nurses
across the county by reducing sampling bias but it requires knowledge of the appropriate
characteristics of the sampling frame and it can be difficult to decide which characteristic
to stratify.

6. 4) Cluster Sampling
• It is a sampling method where multiple clusters of people are created from a populations , rather than
individuals where they are indicative of homogeneous characteristics and have an equal chance of
being a part of the sample known as clusters
• Example, Population 10,000, sample size 1000, group the population by age then get the samples of
ages.
• This method can be more efficient that simple random sampling, especially when the population is
large or when it involves subjects residing in large geographic area but if the chosen clusters are not
representative of the population, resulting in an increased sampling error. This would increase the
risk of bias.

7. Types of Non-Probability Sampling Methods
1. Convenience sampling
• Also known as availability, grab, opportunity or accidental sampling and can be considered as easier
method of sampling because participants are selected based on availability and willingness to take
part but the results are prone to significant bias, because those who volunteer to take part may be
different from those who choose not to participate. It creates volunteer bias and the sample may not
be representative of other characteristics, such as age or sex. Example, status of mental disorder
among students, only certain males was willing to participate
2. Quota Sampling.
• It is non probability sampling technique wherein the assembled sample has the same proportions of
individuals as the entire populations. Often used by market researchers as interviewers are given a
quota of subjects of a specified type to attempt to recruit.
• Example, an interviewer might be told to go out and select 20 adult men, 20 adult women, 10
teenage girls and 10 teenage boys so that they could interview them about their television viewing.
Ideally the quotas chosen would proportionally represent the characteristics of the underlying
population. Advantage of being relatively straightforward and potentially representative but the chosen
sample may not be representative of other characteristics that weren’t considered.

8. 3. Voluntary Sampling
Sampling method where people are voluntary to participate in a survey. For example
a game show in the television request the viewers to visit the relevant website and
respond to the online poll. The people who watched the show and understand the game
show will be oversample from the people who don’t understand the game show. This
create respond bias.
4.Purposive Sampling
Also known as Judgement sampling, selective, or subjective sampling as this sampling
method relies on the judgement of the researcher when choosing who to ask to
participate. It is selected based on characteristics of a populations and the purpose of
the study. This approach is often used by the media when canvassing the public for
opinions and in qualitative research.
The advantage of Purposive Sampling are being time-and cost-effective to perform but
volunteer bias is present and it is also prone to errors of judgement by the researcher
and the findings, whilst being potentially broad, will not necessarily be representative.

9. 5.Snowball Sampling
Where research participants recruits other participants for a test or research. Example, when
carrying out a survey of risk behaviors amongst intravenous drug users, participants may be
asked to nominate other users to be interviewed.
Advantages of snowballing is effective when a sampling frame is difficult to identify but
selecting friends of subjects already investigated by choosing a large number of people with
similar characteristics or views will create a risk of selection bias.

10. CONCLUSION
• Sampling is very common phenomenon in decision making process. Before delving
deeply into sampling process, one must be aware of several basic constructs involved with
sampling namely; population, target population, elements, sampling units and sampling
frame. Determining the final sample size for the research involves various qualitative and
quantitative considerations.
• Selecting a suitable sampling methods not only able to reduce cost and time but also
produce a valid and reliable information if the sample size with appropriate method and
bias is taken considerations.

11. Thank You