The document discusses various sampling methods used in research including population, sample, random sampling, cluster sampling, and systematic random sampling. Random sampling methods aim to select a sample that accurately represents the population without bias. Cluster sampling divides the population into clusters or groups and then randomly selects clusters. Systematic random sampling selects every nth unit from a randomly chosen starting point with a fixed interval between selections. Both cluster and systematic random sampling can reduce costs compared to simple random sampling of large, dispersed populations.

2. POPULATION;
• Including all peoples or items with the
characteristics on wishes to understand.
• Research population is also known as a
well define collection of individuals or
objects known to have similar
characteristics.

3. Lets understand concepts
sample
Study
population
Target
population

4. STATISTICAL APPROACH;
• Sampling is the statistical approach used
in every field for the purpose of collecting
information and on the basis of this
information deduction about the trait of
population can made (chaudhry.2008)

5. SAMPLING;
 “ A sampling is a process of selecting a
number of individuals for a study in such a
way that the individuals represent the large
group from which they are selected”.
 “ Group of people who take part in the
investigation”. The people who take part are
referred to as “participants”.
 Sampling is the process of selecting a
group of subject for a studying such a way
that the individuals represent the large group
from which they were selected. This
representative portion of population is called
sample.

6. WHY NOT STUDY EVERY ONE?
• In research we are interested in learning about
large group of people who all have something in
common. We call the group that we are interested
in studying our target population. In some type of
research the target population might be a smaller
group such as teenager, per school children. It is
more or less impossible to study every person in a
target population so researchers select a sample
or sub group of the population that is likely to the
representative of the target population we are
interested in . Group is geographically scattered it
result in considerable expenditure, time, money
and effort.

7. Types of sampling
RANDOM SAMPLING
NON RANDOM
SAMPLIG

8. RANDOM SAMPLING
• The term random means that each unit
(individual)in the selected population has the equal
chance of selection and selection of one
individual in no way affects selection of another
individual.
• The key to random selection is that there is no
bias involved in the selection of sample. Sampling
bias refer to the situation where the sample does
not reflect the characteristics of the target
population.
• Any variation between the sample characteristics
and the population is only a matter of chance .

9. EXPLAINATION THROUGH EXAMPLE;
• This is similar to national lottery. If the
population is every one who has bought the
lottery ticket, than each person has an equal
chance of winning the lottery assuming they
all have one ticket.
Random sampling requires
a way of naming or numbering the target
population and then using some type of
referral to choose those to make the sample.
Random sampling is the best method of
selecting sample from population of interest.

10. RANDOM SAMPLING
SUB TYPES
CLUSTER RANDOM
SAMPLING
SYSTEMATIC RANDOM
SAMPLING

11. CLUSTER SAMPLING;
• Cluster sampling is sampling in which
groups, not individuals, are randomly
selected. All the members of selected
groups have similar characteristics.
• Cluster sampling is more convenient when
the population is very large or spread over
large geographical area.

12. CLUSTER (AREA) RANDOM SAMPLING;
• Cluster sampling refer to a type of
sampling method, with cluster sampling,
the researcher divide the population in to
separate group called cluster. Than simple
random sampling of cluster is selected
from the population.

13. Various definition;
• In this technique, the total population is
divided in to groups and sample random
sample of these group is selected than the
required information is collected from a
simple random sample of the elements
with each selected group.
• It is sampling method where different
groups with in a population are used as a
sample.

14. Sampling method has following
characteristics;
• The population is divided in to N groups,
called cluster.
• The researcher randomly select N cluster
to include in the sample.
• The number of observations within each
cluster Mi is known and M=M1+M2…..Mn
• Each element of the population can be
assigned to one and only on cluster.

15. EXPLAINATION;
• In cluster sampling, instead of selecting all
the subjects from the entire population
right off, the researcher take several steps
in gathering his sample population.
• First the researcher selects groups
• From each group researcher (cluster) the
researcher select the individual subject by
either simple random sampling or
systematic random sampling.

16. ASPECTS OF CLUSTER SAMPLING;
• The most common cluster used in research is a
geographical cluster.
• For example a researcher wants to survey
academic performance of a high school student in
Pakistan.
• Geographically dispersed population can be
expensive to survey, greater economy than simple
random sampling.
• He divide the entire population in to different
cluster(group).
• Than from the selected cluster the researcher can
either include all the high school students from
each group through simple or systematic random
sampling.

17. TYPES OF CLUSTER SAMPLING;
One stage cluster
sampling
Two stage cluster
sampling

18. ONE STAGE CLUSTER SAMPLING;
• All of the elements within selected clusters
are included in the sample.
• One stage cluster sample occurs when the
researcher includes all high school
students from all randomly selected cluster
or sample.

19. TWO STAGE CLUSTER SAMPLING;
• A subset of elements within selected
clusters are randomly selected for
inclusion in the sample.
• Obtain when researcher only select a
number of students from each cluster by
using simple or systematic random
sampling.

20. MULTISTAGE SAMPLING;
• Cluster sampling can be done in many
stages involves the selection of clusters
within clusters this process is called multi
stage sampling.
• Schools can be randomly selected and
then classrooms within each selected
school can be randomly selected.

21. ADVANTAGES AND DISADVANTAGES
• This technique is quick, cheap, easy
beside using simple random sampling.
• Feasibility ;this method takes large
population into account, since these group
are so large, developing any other
technique will be difficult task
• Economy; The regular major concern
expenditure i.e. travelling and listing effort
will be greatly reduced.

22. DISADVANTAGES;
• Other probabilistic methods give fewer
errors than this method, for this reason it is
discouraged for the beginners.
• Biased sample; if the group in population
that is chosen as sample has a biased
opinion then entire population is inferred to
have the same opinion.

23. Systematic random sampling;
• In systematic random sampling the
researcher first randomly pick the first item
or subject from the population than the
researcher will select each nth subject
from the list.
• The process of obtaining systematic
random sampling is much like an
arithmetic progression.

24. Starting number +INTERVAL:
• STARTING NUMBER:
• The researcher select an integer that must
b less than the total number of individual in
the population this integer will correspond
to the first subject.
• INTERVAL:
• The researcher will pick another integer
which will serve as the constant difference
b/w any two consecutive number in the
progression.

25. FORMULA:
• TOTAL POPULATION=100
• NEEDED POPULATION=12
• PICK ANY STARTING INTERVAL=5
• PICK ANY INTERVAL=8
• THE NUMBER OF SAMPLE WILL
BE=5,13,21,29,37,45,53,61,69,77,85,93.

26. 2ND METHOD:
• A type of random sampling method in
which sampling members from a large
population are selected according to
random starting point and a fix periodic
interval. This interval is called periodic
interval and calculating by divide the
population size by sample size.
• Total population 50000,required sample
size=1000,interval 50000/1000=50 thus
every 50th person is our sample for study.

27. ADVANTAGES AND DISADVANTAGES:
simplicity
foolproof
Convenient
and easy to
administer
Need
special skill