sample designs and sampling procedures , sampling terminology , two major categories of sampling , simple random sampling , systematic sampling , cluster sampling , stratified sampling , why non probability sampling , errors

1. Sample Designs and Sampling Procedures

2. Sampling Terminology
 Sample: A subset, or some part, of a larger population.
 Population or universe: A complete group of entities
sharing some common set of characteristics.
 Population element: An individual member of a specific
population.
 Census / Survey: An investigation of all the individual
elements making up a population.

3. Define the target population
Select a sampling frame
Conduct fieldwork
Determine if a probability or nonprobability
sampling method will be chosen
Plan procedure
for selecting sampling units
Determine sample size
Select actual sampling units
Stages in the
Selection
of a Sample

4. Two Major Categories of Sampling…
 Probability sampling: A sampling technique in which every member of
the population has known, nonzero probability of selection. [An event
that has zero probability may be possible or impossible. However, if
an event has a nonzero probability, it must be possible; that is, it
cannot be impossible.]
 Two types of probability sampling
o Simple Random Sampling
o Complex Random Sampling: Includes
i)Systematic sample [Systematic sampling is a type of
probability sampling method in which sample members from
a larger population are selected according to a random starting
point and a fixed periodic interval. This interval, called
the sampling interval, is calculated by dividing the population
size by the desired sample size.],

5. Two Major Categories of Sampling…
ii) Stratified sample [Stratified sampling, the researcher divides the
population into separate groups, called strata. Then, a
probability sample (often a simple random sample ) is drawn from
each group. Stratified sampling has several advantages over
simple random sampling. For example, using stratified sampling, it
may be possible to reduce the sample size required to achieve a
given precision. Or it may be possible to increase the precision with
the same sample size.]
iii) Cluster sample [cluster sampling, the researcher divides the
population into separate groups, called clusters. Then, a simple
random sample of clustersis selected from the population. The
researcher conducts his analysis on data from the sampled clusters.
For example, given equal sample sizes, cluster sampling usually
provides less precision than either simple random sampling or
stratified sampling. On the other hand, if travel costs between
clusters are high, cluster sampling may be more cost-effective than
the other methods.],

6. Two Major Categories of Sampling…
iv) Multistage area sample [Multistage sampling can be a complex form
of cluster sampling because it is a type of sampling which involves
dividing the population into groups (or clusters). Then, one or more
clusters are chosen at random and everyone within the chosen cluster
is sampled. Example, The Australian Bureau of Statistics divides
cities into “collection districts”, then blocks, then households. Each
stage uses random sampling, creating a need to list specific
households only after the final stage of sampling.]
 Non probability sampling : A sampling technique in which units of
the sample are selected on the basis of personal judgment or
convenience.
i) Convenience [Examples, One of the most common examples of convenience
sampling is using student volunteers as subjects for the research.
Another example is using subjects that are selected from a clinic, a class or an
institution that is easily accessible to the researcher.],

7. Two Major Categories of Sampling
ii) Judgment [Judgment sample is a type of nonrandom sample that is
selected based on the opinion of an expert. Results obtained from
a judgment sample are subject to some degree of bias, due to the frame
and population not being identical.],
iii) Quota [A sampling method of gathering representative data from a
group. As opposed to random sampling, quota sampling requires that
representative individuals are chosen out of a specific subgroup.
For example, a researcher might ask for asample of 100 females, or
100 individuals between the ages of 20-30.] ,
iv) Snowball [snowball sampling (or chain sampling, chain-
referral sampling, referral sampling) is a
nonprobability sampling technique where existing study subjects
recruit future subjects from among their acquaintances.]

8. Simple Random Sampling
The simple random sample is considered a special
case in which each population element has a known
and equal chance of selection.
Advantage
Easy to implement with automatic dialing (random-digit
dialing) and with computerized voice response systems
Disadvantage
 Requires a listing of population elements.
 Takes more time to implement.
 Uses larger sample sizes.
 Produces larger errors.

9. Systematic Sampling…
 In this approach, every kth ele-ment in the population
is sampled, beginning with a random start of an
element in the range of 1 to k.
To draw a systematic sample, do the following:
 Identify, list, and number the elements in the
population.
 Identify the skip interval (k).
 Identify the random start.
 Draw a sample by choosing every kth entry.

10. Advantage
 Simple to design.
 Easier to use than the simple random. Easy to
determine sampling distribution of mean or proportion.
Disadvantage
 Periodicity within the population may skew the sample
and results.
 If the population list has a mono-tonic trend, a biased
estimate will result based on the start point.
Systematic Sampling

11. Stratified Sampling
Most populations can be segregated into several
mutually exclusive subpopulations, or strata. The
process by which the sample is constrained to include
elements from each of the segments is called stratified
random sampling.
Advantage
 Researcher controls sample size in strata.
 Increased statistical efficiency. Provides data to represent and analyze
subgroups.
 Enables use of different methods in strata
Disadvantage
 Increased error will result if subgroups are selected at different rates.
 Especially expensive if strata on the population have to be created.

12. Cluster Sampling
The population can also be divided into groups of elements
with some groups randomly selected for study. This is
cluster sampling. Cluster sampling differs from stratified
sampling in several ways
Advantage
 Provides an unbiased estimate of population parameters if properly
done.
 Economically more efficient than simple random.
 Lowest cost per sample, especially with geographic clusters.
 Easy to do without a population list.
Disadvantage
 Often lower statistical efficiency (more error) due to subgroups being
homogeneous rather than heterogeneous.

13. Why Non probability
Sampling?
1.Satisfactorily meet the sampling objectives.
2. Cost and time
3. Provide acceptable results
4. Feasible alternative

14. Non-probability Sampling
 Convenience: The sampling procedure used to obtain
those units or people most conveniently available.
 Judgment: A technique in which an experienced
individual selects the sample based upon some
appropriate characteristic of the sample member.
 Quota: A procedure that ensures that various
subgroups in the population are represented pertinent
characteristics to the exact extent that the investigator
desire
 Snowball: A sampling procedure in which initial
respondents are selected by probability methods and
additional respondents are obtained from information
provided by the initial respondents.

15. Errors…
Sampling Error:
 Random Sampling Error
 Non Sampling Error/Systematic Error
Random Sampling Error:
 The difference between the sample results and the
result of a census conducted using identical
procedures

16. Systematic Errors:
Occurs when
 Imperfect aspect of the research design that involves;
 Response error
 Mistake in the execution of the research
 Error comes from such sources as sample bias
 Mistake in recording responses
 Sampling frame error (when certain sample elements
are excluded or when the entire population is not
accurately represented in the sampling frame)
 Non response error (The statistical difference between a
survey that includes only those who responded and a
survey that also includes who failed to respond).
Errors

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