DETERMINING THE SAMPLE PLAN

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CHAPTER 12

• DETERMINING THE SAMPLE PLAN

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Important Topics of This Chapter

• Differences between population and sample.
• Sampling frame and frame error.
• Developing sampling plan.
• Basic sampling methods.
• Strength and Weaknesses of Basic Sampling
  techniques.
• Choosing Probability Vs. non-probability sampling.

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Definitions of Important Terms

• Population or Universe
• The total group of people from whom information
  is needed.
• Census
• Data obtained from or about every member of the
  population of interest.
• Sample
• A subset of the population of interest
• Sampling Error
• Selection error
• Sampling size
• Sample Frame and Frame Error

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Sample vs. Census
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The Sampling Design Process
Determine the Sampling Frame
Select Sampling Technique(s)
Determine the Sample Size
Execute the Sampling Process
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Steps in Developing a Sampling Plan

• Step 1 Defining the Population
• Bases for defining the population of interest
  include
• Geography
• Demographics
• Use
• Awareness
• Step 2 Choosing a Sampling Frame
• Sampling frame
• List of population elements from which to select
  units to be sampled.

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Steps in Developing a Sampling Plan (cont.)

• Step 3 Selecting the Sampling Technique(s)
• Probability samples
• Samples in which every element of the population
  has a known, nonzero probability of selection.
• Non-probability samples
• Include the selection of specific elements from
  the population in a nonrandom manner.
• Sampling error
• The difference between the sample value and the
  true value of the population mean.

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Steps in Developing a Sampling Plan (cont.)
Advantages of probability samples
Disadvantages of probability samples
- The researcher can be sure of obtaining
information from a representative cross
section of the population of interest. -
Sampling error can be computed. - The survey
results are projectable to the total
population.
- They are more expensive than non-probability
samples of the sample size in most cases.
The rules for selection increase
interviewing costs and professional time must
be spent in developing the sample design. -
Probability samples take more time to design
and execute than non- probability samples.
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Steps in Developing a Sampling Plan (cont.)
Advantages of non-probability samples
Disadvantages of non-probability samples

• - Non-probability samples cost less
• than probability samples. This
• characteristic of non-probability
• samples may have considerable
• appeal in those situations where
• accuracy is not of critical
• importance.
• Non-probability samples
• ordinarily can be conducted more
• quickly than probability samples.

- Sampling error cannot be computed. - The
researcher does not know the degree to which
the sample is representative of the population
from which it was drawn. - The results of
non-probability samples cannot and should not
be projected to the total population.
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Steps in Developing a Sampling Plan (cont.)

• Step 4 Determine the Sample Size
• Once the sampling method has been chosen, the
  next step is to determine the appropriate sample
  size.
• Developing Operational Procedures
• Involves determining whether a probability or
  non-probability sample is being used.

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Steps in Developing a Sampling Plan (cont.)

• Step 5 Execute the Sampling Process
• The final step in the sampling process involves
  execution of the operational sampling plan
  discussed in the previous steps.
• It is important that this step include adequate
  checking to make sure that specified procedures
  are adhered to.

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Classification of Sampling Techniques
Probability Sampling Techniques
Stratified Sampling
Cluster Sampling
Simple random Sampling
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Probability Sampling Methods

• Simple Random Sampling
• Is considered to be the purest form of
  probability sampling. A probability sample is a
  sample in which every element of the population
  has a known and equal probability of being
  selected into the sample.

Sample Size
Probability of Selection
Population Size
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Procedures for Drawing Probability Samples
Simple Random Sampling
1. Select a suitable sampling frame 2. Each
element is assigned a number from 1 to N (pop.
size) 3. Generate n (sample size) different
random numbers between 1 and N 4. The numbers
generated denote the elements that should be
included in the sample
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Probability Sampling Methods (cont.)

• Systematic Sampling
• Probability sampling in which the entire
  population is numbered, and elements are drawn
  using a skip interval.

Population Size
Skip Interval
Sample Size
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Systematic Sampling
1. Select a suitable sampling frame 2. Each
element is assigned a number from 1 to N (pop.
size) 3. Determine the sample interval iiN/n.
If i is a fraction, round to the nearest
integer 4. Select a random number, r, between 1
and i, as explained in simple random sampling 5.
The elements with the following numbers will
comprise the systematic random sample r,
ri,r2i,r3i,r4i,...,r(n-1)i
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Probability Sampling Methods (cont.)

• Stratified Samples
• Stratified samples are probability samples that
  are distinguished by the following procedural
  steps
• First, the original or parent population is
  divided into two or more mutually exclusive and
  exhaustive subsets (e.g., male and female).
• Second, simple random samples of elements from
  the two or more subsets are chosen independently
  from each other.

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Stratified Sampling
1. Select a suitable frame 2. Select the
stratification variable(s) and the number of
strata, H 3. Divide the entire population into H
strata. Based on the classification variable,
each element of the population is assigned to one
of the H strata 4. In each stratum, number the
elements from 1 to Nh (the pop. size of stratum
h) 5. Determine the sample size of each stratum,
nh, based on proportionate or disproportionate
stratified sampling, where 6. In each stratum
select a simple random sample of size nh
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Probability Sampling Methods(cont.)

• Cluster Samples
• In the case of cluster samples, the sampling
  units are selected in groups. There are two basic
  steps in cluster sampling
• First, the population of interest is divided into
  mutually exclusive and exhaustive subsets.
• Second, a random sample of the subsets is
  selected.

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Cluster Sampling
1. Assign a number from 1 to N to each element in
the population 2. Divide the population in C
clusters of which c will be included in the
sample 3. Calculate the sampling interval i,
iN/c (round to nearest integer) 4. Select a
random number r between 1 and i, as explained in
simple random sampling 5. Identify elements with
the following numbers r,ri,r2i,... r(c-1)i 6.
Select the clusters that contain the identified
elements 7. Select sampling units within each
selected cluster based on SRS or systematic
sampling 8. Remove clusters exceeding sampling
interval i. Calculate new population size N,
number of clusters to be selected C C-1, and
new sampling interval i.
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Types of Cluster Sampling
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Non-probability Sampling Methods

• Convenience Samples
• Non-probability samples used primarily because
  they are easy to collect.
• Judgment Samples
• Non-probability samples in which the selection
  criteria are based on personal judgment that the
  element is representative of the population under
  study.

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Non-probability Sampling Methods (cont.)

• Quota Samples
• Non-probability samples in which population
  subgroups are classified on the basis of
  researcher judgment.
• Snowball Samples
• Non-probability samples in which selection of
  additional respondents is based on referrals from
  the initial respondents.

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Strengths and Weaknesses of Basic Sampling
Techniques
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Choosing Non-probability vs. Probability Sampling
Table 11.4