This document discusses different types of sampling methods. It begins by explaining the purpose of sampling is to obtain information about populations by taking samples and computing estimators. There are two main types of errors: sampling errors due to random selection and non-sampling errors such as non-response or measurement errors. Some common sampling methods described include simple random sampling, stratified sampling which divides a heterogeneous population into homogeneous groups, systematic sampling which selects every k-th item, cluster sampling which groups items into clusters, and multistage sampling which has multiple stages of selection. Other non-probability sampling methods mentioned include convenience sampling and purposive or judgment sampling.

1. Sampling
BY-
BHAWANA

3. Why Sample?
Samples are taken to obtain
information about populations.
Sample estimators are computed to
estimate parameters of the the
population from which the sample was
drawn.

4. Advantages

5. Types of Sampling Errors
Sample Errors
Non Sample Errors

6. Sampling Error
Sampling error refers to differences
between the sample and the population
that exist only because of the
observations that happened to be
selected for the sample Increasing the
sample size will reduce this type of
error

7. Non Sample Errors
Response Error A non-response error occurs when units
selected as part of the sampling procedure do not
respond in whole or in part
Respondent error (e.g., lying, forgetting)
Interviewer bias
Recording errors
Poorly designed questionnaires
Measurement error (difficult to answer)

9. Simple Random
 with replacement
 without replacement
…must be able to identify the target population
and ensure each item has an equal likelihood of
being selected…
….use table of random numbers …or computer
generate a series of random numbers…

10. Simple Random
 How to Select
– assign numbers to
elements using
random numbers
table
 Strengths/Weaknesses
– basic, simple, often
costly
– must assign a number
to each element in
target population

11. Stratified
When the population
is heterogeneous
overall, but within it
there are
homogeneous
populations (strata)
the population is
stratified.

12. Stratified
 How to Select
– divide population
into groups that are
similar within and
different between
variable of interest
 Strengths/Weaknesses
– with proper strata, can
produce very accurate
estimates.
– less costly than
simple random
sampling
– must stratify target
population correctly

13. Systematic
Selecting a random
sample, as opposed
to the simple random
selection technique.
Select the K-th item.
Draw every I-th item.

14. Systematic
 How to Select
– select every K-th
element are from a
list after a random
start
 Strengths/Weaknesses
– produces very accurate
estimates when
elements in population
exhibit order
– used when pop. size
not known
– simplifies selection
process

15. Cluster
Another modified
random sample design
-- requires that the
sample unites be
grouped in clusters in
the universe.
Not grouped by
homogeneous strata in
the population.

16. Cluster
 How to Select
– randomly choose
clusters and sample
all elements within
each cluster
 Strengths/Weaknesses
– with proper clusters,
can produce accurate
estimates
– useful when sample
frame not available or
travel costs high
– must cluster target
population correctly

17. Multistage
The selection procedure takes place in
a hierarchy of stages.
– first primary sample unit
– second second sample unit
– third tertiary sample unit
– . . . . .
– last final (or ultimate) sample unit

18. Convenience sampling
the process of including whoever happens to be
available at the time…called “accidental” or
“haphazard” sampling.
This type of sampling is most useful for pilot
testing.
In social science research, snowball sampling is
a similar technique, where existing study
subjects are used to recruit more subjects into
the sample.

19. Purposive sampling
the process whereby the researcher
selects a sample based on
experience or knowledge of the
group to be sampled…called
“judgment” sampling

20. Quota sampling
the process whereby a researcher
gathers data from individuals
possessing identified characteristics
and quotas

21. Homogeneous sampling: selecting
participants who are very similar in
experience, perspective, or outlook
Criterion sampling: selecting all cases that
meet some pre-defined characteristic
Snowball sampling relies upon respondent
referrals of others with like characteristics

22. Factors to Consider in Sample
Design
Research objectives
Degree of accuracy
Resources
Time frame
Knowledge of target population
Research scope

23. THANK YOU