Mgt 540
Research Methods
Sampling Issues
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Basic Research Progress
Explorative - Descriptive Hypothesis Testing
(Qualitative)
(Quantative)
1. Framework / Domain
 extant knowledge for
reference
2. Research Design
3. Data collection /
presentation
4. Data analysis
 Emergent themes
5. Relationship to extant
knowledge?
 Tie to lit review, other
research
6. Findings?
 Possible hypothesis?
1. Framework /
Domain
 Foundation
2. Conceptual
framework
3. Hypothesis
presentation
4. Research Design
5. Data collection /
presentation
6. Data analysis
 Confirm/disconfirm
7. Findings?
 Possible additional
hypotheses?
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Research Design Flowchart
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Copyright © 2003 John Wiley & Sons, Inc. Sekaran/RESEARCH 4E
FROM CHAPTER 11
Sampling

Why sample?
 Budget
restrictions
 Time constraints
 Inaccessibility of some population
members
 Sufficient accuracy, reliability with good
sample
Larger sample required for more
heterogeneous population
 Randomly chosen sample is fair in the
sense that every member of the
population has an equal chance of
being chosen

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Sampling issues (terms)

Sampling
 Selection
of sufficient number of items or
elements so that the properties of the sample
(statistic) could be generalized to the
population (parameter)

Population Frame
 Listing

of population elements
Population
 Entire
group of interest to researcher (people,
things, events)

Sample
 Subgroup

Subject
 Single

of the population
member of a sample
Element
 Single
member of the population
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Sampling precision
 Precision
Degree
of sampling error
Measured by the standard error of
the estimate
See page 286 and
Statistical tables, beginning on page 432
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Sampling
= Sample Mean
S = Std. Deviation
μ= Population Mean
σ = Std. Deviation
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Copyright © 2003 John Wiley & Sons, Inc. Sekaran/RESEARCH 4E
FIGURE 11.1
Questions for determining
sample
 Relevant
target population?
 Exact parameters of interest?
 Kind of sampling frame
available?
 Sample size needed (for desired
level of confidence)?
 Cost relating to sampling
design?
 Time available to collect data
from sample?
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Copyright © 2003 John Wiley & Sons, Inc. Sekaran/RESEARCH 4E
FIGURE 11.2
Sampling Frame
 The
empirical representation of
the theoretical universe of
interest
 In theory may be the entire
population
 But, for example
 Not
all own telephones (for a telephone
survey)
 Some may be homeless (for a mail survey)
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Sampling Unit
 Compare
to the desired unit of
analysis
Individuals
Dyads
Work
groups, teams
Companies
Industries
Markets
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Sampling Issues
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Copyright © 2003 John Wiley & Sons, Inc. Sekaran/RESEARCH 4E
11B
Probability & Non Probability
Sampling
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Copyright © 2003 John Wiley & Sons, Inc. Sekaran/RESEARCH 4E
11C
Simple Random Sampling
 Most
representative for most
purposes
Disadvantages
Cumbersome
and tedious
Entire listing of all elements in the
desired population are usually not
available
Very expensive
Not the most efficient design
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Complex probability
sampling(s)
1.
2.
3.
4.
5.
Systematic
Stratified random sampling
Cluster sampling
Area sampling
Double sampling
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Systematic sampling
 Every
nth element is sampled,
starting from a randomly chosen
element
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Copyright © 2003 John Wiley & Sons, Inc. Sekaran/RESEARCH 4E
11F
Stratified random sampling
 Number
of mutually exclusive
sub-populations or strata
e.g.
university students divided into
juniors, seniors, etc.
Homogeneity within stratum and
heterogeneity between strata
Statistical efficiency greater in
stratified samples
Sub-groups can be analyzed
Different methods of analysis can be
used for different sub-groups
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Copyright © 2003 John Wiley & Sons, Inc. Sekaran/RESEARCH 4E
11G
Stratified random sample
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Copyright © 2003 John Wiley & Sons, Inc. Sekaran/RESEARCH 4E
11H
Stratified random sample
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Copyright © 2003 John Wiley & Sons, Inc. Sekaran/RESEARCH 4E
TABLE 11.1
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Copyright © 2003 John Wiley & Sons, Inc. Sekaran/RESEARCH 4E
11I
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Copyright © 2003 John Wiley & Sons, Inc. Sekaran/RESEARCH 4E
11J
Non-Probability Sampling

Convenience samples
 the
researcher’s convenience –
unrestricted

Purposive samples
sampling – expert selection of
respondents
 Quota sampling – ensuring representation
of certain groups, individuals
 Judgment

Snowball sampling – initially selected
respondents (by probability or not)
refer later ones
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Copyright © 2003 John Wiley & Sons, Inc. Sekaran/RESEARCH 4E
11K
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Copyright © 2003 John Wiley & Sons, Inc. Sekaran/RESEARCH 4E
11L
Precision
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Copyright © 2003 John Wiley & Sons, Inc. Sekaran/RESEARCH 4E
11M
Sampling considerations
 What
is the relevant population?
 What type of sample should be
drawn?
 What sampling frame should be
used?
 What are the parameters of
interest?
 How much accuracy and
precision are desired?
 What is the sample size needed?
 What are the sampling costs?
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Copyright © 2003 John Wiley & Sons, Inc. Sekaran/RESEARCH 4E
11N
Sample size considerations
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Copyright © 2003 John Wiley & Sons, Inc. Sekaran/RESEARCH 4E
11O
Sampling Efficiency
Using n = sample size,
S = standard error
Efficiency is achieved when:
Keeping n constant,
you achieve a smaller S
Reduce n
keep the same level of S
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Copyright © 2003 John Wiley & Sons, Inc. Sekaran/RESEARCH 4E
11P
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Copyright © 2003 John Wiley & Sons, Inc. Sekaran/RESEARCH 4E
FIGURE 11.3
Precision vs. Confidence
More Precision
Less Confidence
More Confidence
Less Precision
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Copyright © 2003 John Wiley & Sons, Inc. Sekaran/RESEARCH 4E
FIGURE 11.4
Pg. 294
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Copyright © 2003 John Wiley & Sons, Inc. Sekaran/RESEARCH 4E
TABLE 11.3
Relevance of sample size
Refer back to diagram on page 175

Purpose of Research?
 Exploratory
 Discovery
 Hypothesis

testing?
Types of investigation?
 Differences?
 Correlations?
 Causality?
Unit of analysis?
 Data Collection method?

 Qualitative?
 Quantitative?

Measurement / Measures?
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Sampling exercises
“What kinds of sampling designs for….”
A
study to get a quick idea of the
medical acceptability of a new
aspirin substitute which cannot
be dispensed over the counter
without prescription.
Purposive
judgment sampling
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Sampling exercises
“What kinds of sampling designs for….”
A
study involving a sample of 325
students in a university where
2,000 students are enrolled.
A
systematic sampling design (using a
university listing of students
 An
investigation of the career
salience of professionals in the
fields of medicine, engineering,
business, and law.
A
stratified random sampling with
stratification along profession, gender,
age, etc.
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Sampling exercises
“What kinds of sampling designs for….”
 The
generalizability of the
attitudes of blue collar workers
from a sample of 184, to the total
population of 350 blue collar
workers in the entire factory of a
particular company.
Simple
random sampling (because
of the high importance attached to
generalizability
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Sampling exercise (problem)
You want to estimate the production days that
would be lost during the next three months by
sampling the vacation intentions of a few
employees. You randomly select 36 employees in
the organization and find that the average number of
days they intend taking off is 16 during the coming
three Summer months, with a standard deviation of
seven (7) days. Based on these sample statistics,
you want to estimate at a 99 percent confidence
level, the days that will be lost due to the entire
population of workers taking vacation time during
the next three months, so that the plant manager
knows how much temporary help he should plan on
hiring during the summer months in order for work
to proceed smoothly.
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Exercise calculation
Solution:
μ=
±zS
S = S/√n = 7/6 = 1.167
μ = 16 ± (2.576 x 1.167)
= 16 ± 3.01
= 12.99 to 19.01
= Sample Mean
μ = Population Mean
S = Std. Deviation
S = Standard Error
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
If there are 100 employees in the
organization expected to take vacation
during Summer, then, the most
optimistic estimation of the days lost
through vacation time during the
summer would be (13 x 100 =) 1m300
days and the most pessimistic would be
(19 x 100 =) 1900 days. This would
mean that temporary help would be
needed anywhere between 1,300 and
1,900 days worth of labor for production
to proceed smoothly.

To narrow the gap – (increase
precision) requires sacrificing
confidence – choose your risk.
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