This document discusses sampling design and measurement of variables in research. It covers: - The definition and reasons for sampling, including reducing costs, time and errors compared to a full census. - Key considerations for sample size decisions including the population, elements, frame, sample, units and subject of study. Larger samples are needed for multivariate or experimental research. - Common sampling techniques like simple random, systematic, cluster and stratified sampling as well as sources of sampling error. - The importance of clearly defining variables through operationalization and use of appropriate scales like nominal, ordinal, interval and ratio scales for measurement.

1. CHAPTER 7
SAMPLING DESIGN

2. SAMPLING

3. SAMPLING
The process of selecting a
sufficient number of elements from
the population, so that results from
analyzing the sample are
generalizable to the population.

4. REASONS
FOR
SAMPLING

5. Less
cost
Less
time
Less
errors due
to less
fatigue
Destruction
of elements
avoided

6. SAMPLE SIZE DECISION
There are variety sample size decision that
available . The choice can be defend on the
following:
 Population
 The element
 Population frame
 Sample
 Sampling unit
 The subject

7. SAMPLE SIZE DECISION
a) Population
-Refer to the entire group of people, events or things
of interest that the population that the researches
wishes to investigate.
b) Element
- Single member of the population. The census is a
count of all elements in the human population.
c) Population frame
- the listing of all the element in the population
from which the sample is drawn. It is also known
as sampling frame.

8. SAMPLE SIZE DECISION
d) Sample
-Subset of the population. It is a subgroup of
the population selected using sampling
method or design.
e) Sampling unit
-the element or set of the elements that is
available for selection in some stage of the
sampling process.
f) Subject
-a subject is a single member of the sample.

9. The sampling
process
Define the
population
Execute the
sampling
process
Determine
the
appropriate
sample size
Determine
the sample
frame
Determine
the sampling
design

10. Sample Size
Most research
Sub-samples
• > 30 < 500
are
appropriate
• Min 30 for
each
category
Multivariate
research
Experimental
research
• At least
10 times
more than
the
number
of
variables
• Can be
low as
10 to 20

11. Sample size
Precision
• How close the estimate to
the true population
characteristics with low
margin of error
Confidence
• How certain the estimate will
really hold true for the
population.
• Commonly accepted confidence
level ≤0.05 (95% confidence)

12. Population
Geographical
Boundaries
&
Time
Elements
Defined
in
terms

13. Sample Frame
Physical
representation of
all the elements in
the population
from which the
sample is drawn
Make sure that
sample frame the
latest and most upto-date to avoid
coverage error

14. Sampling Design
Target population
of focus to the
study
The exact
parameters need
to be investigated
Availability of
sampling frame
Sample size
needed
Costs associated
to the sampling
design
Time frame
available for data
collection

15. Sampling Design
Probability
sampling
Nonprobability
sampling

16. SAMPLING TECHNIQUES
Probability Sampling
Simple Random Sampling
Systematic Sampling
Stratified Random Sampling
Cluster Sampling

17. Simple Random Sampling
PROCEDURE
– Each element has a known and
equal chance of being selected
CHARACTERISTICS
– Highly generalizable
– Easily understood
– Reliable
population
necessary
frame

18. Systematic Sampling
PROCEDURE
– Each nth element, starting
with random choice of an
element between 1 and n
CHARACTERISTICS
– Easier than simple random
sampling
– Systematic biases when

19. Cluster Sampling
PROCEDURE
– Divide of population in clusters
– Random selection of clusters
– Include all elements from selected clusters
CHARACTERISTICS
– Intercluster homogeneity
– Intracluster heterogeneity
– Easy and cost efficient
– Low correspondence with reality

20. Stratified Sampling
PROCEDURE
– The process of dividing members
of
the
population
into
homogeneous subgroups before
sampling
– There are two types Cof stratified
Stratum
A
B
random sampling:
Population size
100
200
300
1/2
1/2
•Sampling fraction
Proportionate 1/2
Final sample size
50
100
150

21. •Disproportionate
Stratum
A
B
C
Population size
100
200
300
Sampling fraction
1/2
¾
1/3
Final sample size
50
150
100
CHARACTERISTICS
– Interstrata heterogeneity
– Intrastratum homogeneity
– Includes all relevant
subpopulations

22. Nonprobability Sampling
Convenienc
e Sampling
Members of the population are
chosen based on their relative ease
of access.
Judgment
Sampling
The researcher chooses the sample
based on who they think would be
appropriate for the study.
Quota
Sampling
A quota is established (say 65%
women) and researchers are free to
choose any respondent they wish as
long as the quota is met.

23. 5 Common Sampling Errors
o POPULATION SPECIFICATION ERROR
o SAMPLE FRAME ERROR
o SELECTION ERROR
o NON-RESPONSE
o SAMPLING ERRORS

24. Measurement of
Variables
Operational definition
Scales

25. Measurement
the
assignment of
numbers or
other symbols
to
characteristics
(or attributes)
of objects
according to a
pre-specified
set of rules.
(Characteristics of)
Objects
Type of
variables
Object – house,
countries,
restaurants.
One lends itself
to objective and
precise
measurement;
Examples of
characteristics of
objects are
arousal seeking
tendency,
achievement
motivation,
organizational
effectiveness
The other is
more nebulous
and does not
lend itself to
accurate
measurement
because of its
abstract and
subjective
nature.

26. Operationalizing Concepts
Operationalizing
is done by looking
at the
behavioural
dimensions, facets,
or properties
denoted by the
concept.
Operationalizing
concepts:
reduction of
abstract concepts
to render them
measurable in a
tangible way.
26

27. Example
27

28. Scale
Tool or mechanism by
which individuals are
distinguished as to how
they differ from one
another on the variables
of interest to our study.
28

29. 4 TYPES
OF SCALES

30. Nominal Scale
•
A nominal scale is one that allows the researcher
to assign subjects to certain categories or
groups.
•
What is your department?
O Marketing
O Maintenance
O Finance
O Production
O Servicing
O Personnel
O Sales
O Public Relations O
Accounting
•
What is your gender?
O Male
O Female
30

31. Ordinal Scale
Ordinal scale: not only categorizes variables in
such a way as to denote differences among
various categories, it also rank-orders
categories in some meaningful way.
What is the highest level of education you
have completed?
O
Less than High School
O
High School
O
College Degree
O
Masters Degree
O
Doctoral Degree
31

32. Interval Scale
• Interval scale: whereas the nominal
scale allows us only to qualitatively
distinguish groups by categorizing
them into mutually exclusive and
collectively exhaustive sets, and the
ordinal scale to rank-order the
preferences, the interval scale lets us
measure the distance between any two
points on the scale.
32

33. • Circle the number that represents your feelings at this particular
moment best. There are no right or wrong answers. Please answer every
question.
1. I invest more in my work than I get out of it
I disagree completely
1 2 3 4 5 I agree completely
2. I exert myself too much considering what I get back in return
I disagree completely
1 2 3 4 5 I agree completely
3. For the efforts I put into the organization, I get much in return
I disagree completely
1 2 3 4 5 I agree completely
33

34. Ratio scale
• Indicates not only the
magnitude of the differences
but also their proportion.

35. THE END!!!
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