This document discusses different types of sampling methods used in research. It defines key terms like population, sample, and sampling techniques. There are two main types of sampling: probability sampling, where every unit has an equal chance of being selected; and non-probability sampling, which does not use random selection. Some probability sampling methods described are simple random sampling, systematic random sampling, and stratified random sampling. Non-probability sampling techniques discussed include quota sampling, convenience sampling, purposive sampling, snowball sampling, and self-selection sampling.

1. Sampling and its Types
By: Prabhleen Arora

2. • Population – the population is a group that is studied by
researcher.
• Sampling – the process of selecting a part of population.
• Sample – the selected part of the population.
• Sample size – the number of people in the selected sample.
• Sampling frame- the list of individual or people included in
the sample.
• Sampling technique – it refers to the technique or procedure
used to select members of the sample.
Introduction

3. Sample

4. Stages in the selection of a sample

5. Need of Sampling
• Large population is conveniently covered.
• Used when the data is unlimited.
• Lower cost
• Less time consuming.
• Accuracy of result.

6. Advantages of Sampling
• Time Saving
• Economical
• Accuracy
• Covers size of population

7. Disadvantages of Sampling
• Biasedness.
• Need for specialized knowledge.
• Selection of true representative sample.
Ex- difficult to select the right representative
sample.
• Difficulty in finding sample.
Ex- Sometimes population is too small or too
heterogeneous.

8. Sampling Types
There are mainly two types of Sampling:
Probability sample – a method of sampling
that uses of random selection so that all units/
cases in the population have an equal
probability of being chosen.
Non-probability sample – does not involve
random selection and methods are not based
on the rationale of probability theory.

9. Probability Sampling
• A sample that selects subjects with a known
probability.
• Every unit in the population has equal chances
of being selected as a sample unit.
• Probability samples are important when one
wishes to generalize to the larger population
because one knows the responses will fit the
characteristics of the population.

10. Simple Random Sampling
• Assures that each element in the population has an equal
chance of being selected.
• Selection is free from bias.
• Suppose your college has 500 students (population)and
you need to conduct a short survey on the quality of the
food served in the cafeteria. You decide that a sample of
70 students (sample) should be sufficient for your
purposes. In order to get your sample, you;
• a. Assign a number from 001 to 500 to each students,
• b. Use a table of randomly generated numbers (Random
Number Tables)

11. Systematic Random Sampling
• There is a gap, or interval, between each selected unit
in the sample.
• Selection of units is based on sample interval “k”
starting from a determined point, where k = N/n
• Steps:
• i. Number the units on your frame from 1 to N.
ii. determine point/ the random start. iii. Afterwards,
every k-th must be drawn until the total sample has
been drawn.
• E.g., If population is 500 and we want a sample of 50,
500/50=10. We will choose every 10th subject or object
from the population.

12. Stratified Random Sampling
• A population is divided into homogenous,
mutually exclusive subgroups, called strata and a
sample is selected from each stratum.
• Goal: To guarantee that all groups in the
population are adequately represented.
• Within stratum - uniformity (homogenous),
Between strata – differences (heterogeneous).
• Can be stratified by any variable that is available
e.g. Gender (Male & Female), Education Level
(Undergraduate, Graduate & Postgraduate) etc.

13. Cluster Sampling
• To reduce the cost of sampling a population
scattered over a large geographic area.
• To gather data quickly and cheaply.
• It helps in winding up with respondents who
come from all over the area.
• Divides the population into groups or clusters.
• Within cluster- differences (heterogeneous)
• Between cluster– uniformity (homogenous).
• Select clusters at random - all units within
selected clusters are included in the sample.

14. Non-Probability Sampling
Non-probability sampling is a sampling
technique where the samples are gathered in a
process that does not give all the individuals in
the population equal chances of being selected.

15. Types of Non-Probability Sampling
1) Quota Sampling: Quota sampling is a method
for selecting survey participants that is a non-
probabilistic version of stratified sampling.

16. 2) Covenience Sampling: A convenience sample
is simply one where the units that are selected
for inclusion in the sample are the easiest to
access.

17. 3) Purposive sampling: It is also known as judgmental
sampling, reflects a group of sampling techniques that
rely on the judgement of the researcher when it comes
to selecting the units that are to be studied.

18. 4) Snowball Sampling: Snowball sampling is
particularly appropriate when the population you are
interested in is hidden and/or hard-to-reach. These
include populations such as drug addicts, homeless
people, individuals with AIDS/HIV, prostitutes, and so
forth.

19. 5) Self-Selection Sampling: Self-selection sampling is
appropriate when we want to allow units or cases,
whether individuals or organizations, to choose to take
part in research on their own accord.