Probability Sampling in research method

1. PROBABILITY
SAMPLING
:PRESENTED BY
MUHAMMAD ALI

2. SAMPLING

3. WHY SAMPLING?
• It would be impracticable for you to survey the entire
population
• Your budget constraints prevent you from surveying
the entire population
• Your time constraints prevent you from surveying the
entire population
• You have collected all the data but need the results
quickly

4. DEFINITIONS YOU SHOULD KNOW
▫ Element: The object about
which or from which the
information is desired, e.g.,
the respondent
▫ Sampling unit: An element,
or a unit containing the
element, that is available for
selection at some stage of the
sampling process
▫ Sampling Frame: List
containing all the elements
from which you choose the
sample

6. PROBABILITY SAMPLING
• “Probability sampling (or representative
sampling) is most commonly associated with
survey-based research strategies where you need
to make inferences from your sample about a
population to answer your research question(s)
or to meet your objectives.”

7. FOUR STAGES OF PROB. SAMPLING
1 ) Identify a suitable sampling frame based on
your research question(s) or objectives.
2 ) Decide on a suitable sample size.
3 ) Select the most appropriate sampling
technique and select the sample.
4) Check that the sample is representative of the
population.

8. SUITABLE SAMPLE SIZE
• The larger your sample’s size, the lower the
likely error in generalizing to the population
• Probability sampling is therefore a compromise
between the accuracy of your findings and the
amount of time and money you invest in
collecting, checking and analyzing the data.

9. Probability
Sampling
Simple
Random
Systematic
Stratified
Random
Cluster Multi-stage
TYPES OF PROB. SAMPLING

10. PROBABILITY SAMPLING
• Each element in the population has a known and
equal probability of selection.
• Every element is selected independently of every
other element.

11. SYSTEMATIC SAMPLING
1) Number each of the cases in your sampling frame
with a unique number. The first case is numbered 0,
the second 1 and so on.
2) Select the first case using a random number.
3) Calculate the sampling fraction.
sampling fraction =actual sample size
total population
4) Select subsequent cases systematically using the
sampling fraction to determine the frequency of
selection.

12. STRATIFIED RANDOM SAMPLING
1) Choose the stratification variable or variables.
2) Divide the sampling frame into the discrete
strata.
3) Number each of the cases within each stratum
with a unique number.
4) Select your sample using either simple random
or systematic sampling.

13. CLUSTER SAMLPING
1) Choose the cluster grouping for your sampling
frame.
2) Number each of the clusters with a unique
number. The first cluster is numbered 0,
the second 1 and so on.
3) Select your sample using some form of random
sampling.

14. MULTI-STAGE SAMPLING
• Also called multi-stage cluster sampling
• Used to overcome problems associated with a
geographically dispersed population when face-
to-face contact is needed or where it is expensive
and time consuming to construct a sampling
frame for a large geographical area

15. MULTI-STAGE SAMPLING
• Example: Population is very large, sample size is
very small
• To select 40 persons from all over Pakistan
• Select 10 persons from each province randomly