3. Probability sampling
• A type of sampling in which every element
of population has equal chance of
selection
4. Types of probability sampling
•
•
•
•
Simple random sampling
Systematic random sampling
Stratified random sampling
Cluster sampling
5. Simple random sampling
• A method of probability sampling in which
every unit has known and equal chance of
being selected.
6. Method of simple random sampling
• With replacement
• Without replacement
7. Systematic random sampling
• A method of probability sampling in which
the defined population is ordered and
sample is selected by using skip interval.
8. Method of systematic random
sampling
• Obtain the ordered list of units
• Determine number of units in list and
desired sample size
• Determine the start point randomly
• Select sample by skip interval.
9. Stratified random sampling
• A method of sampling in which a
population is divided into different
subgroups and samples are drawn
randomly.
10. Cluster sampling
• A method of sampling in which sampling
units are selected in groups rather than
individually.
11. Non probability sampling
• A method of sampling in which every
element has unknown and unequal
chance of selection.
12. Convenience sampling
• convenience sampling is a type of non
probability sampling which involves the
sample being drawn from that part of the
population which is close to hand
13. Judgmental sampling
• the researcher chooses the sample based
on who they think would be appropriate for
the study. This is used primarily when
there is a limited number of people that
have expertise in the area being
researched.
14. Quota sampling
• A quota is established (say 65% women)
and researchers are free to choose any
respondent they wish as long as the quota
is met.
15. Snow ball sampling
• he first respondent refers a friend. The
friend also refers a friend, and so on. Such
samples are biased because they give
people with more social connections an
unknown but higher chance of selection
16. Sample design
• A methodological plan to obtain a sample
from a given population.
18. Characteristics of ideal sample
design
• Must produce representative sample
• Must result in less sampling error
• Must be feasible in context of available
funds.
• Should have results which can be applied
to whole population.
• Should be able to prevent systematic bias.
19. Types of errors in sampling
• Sampling error
• Non sampling error
20. Sampling error
• The difference between a sample statistic
and population parameters.
21. How to minimize sampling error
sampling error
• Increasing sample size
• stratification
