This document discusses different types of scales used in measurement and sampling techniques. It defines population, sample, parameter, statistic, and different variables. It then explains probability sampling techniques like simple random sampling, stratified random sampling, and systematic sampling as well as non-probability sampling techniques like convenience sampling, judgement sampling, quota sampling, and snowball sampling. Finally, it compares different sampling techniques and discusses nominal, ordinal, interval, and ratio scales of measurement.

2. 
Population
Collection of all individuals or objects or items under study and
denoted by N

Sample
A part of a population and denoted by n

Variable
Characteristic of an individual or object.
◦ Qualitative and Quantitative variables

Parameter
Characteristic of the population

Statistic
Characteristic of the sample

3. Sampling Techniques
P ro b a b i l i t y
S a m p lin g
S i m p l e R a n d o m S tra ti f i e d R a n d o m
S a m p le
S a m p le
P ro p o rti o n a te
S y s te m a ti c
R andom
S a m p le
D i s p ro p o rti o n a te
O n e S ta g e
N o n -P ro b a b i l i t y
S a m p lin g
C l u s te r
S a m p lin g
C o n ve n i e n c e
s a m p lin g
T w o S ta g e
M u l t i S ta g e
Q u o ta
S a m p lin g
Judgem ent
S a m p lin g
S nowbal
S a m p lin g

4. COMPARISON OF PROBABILITY SAMPLE
Description
Cost and Degree
of use
Advantages
Disadvantages
Simple Random Sample:
Researcher assigns each
member of the sampling frame a
number, then selects sample
units by a random method
High cost
Most likely used
Only minimal advance
knowledge of population
needed; easy to analyse
data and compute error
Requires sampling frame to
work from; Does not use
knowledge of population; larger
errors for same sample size than
with stratified sampling.
Stratified Random sample:
Researcher divides the
population into groups and
randomly selects sub-samples
from each group
High cost
Moderately
used
Assures representation
of all groups in sample;
Reduces variability for
same sample size
Requires accurate information
on proportion in each stratum;
If stratified lists are not already
available, they can be costly to
prepare.
Systematic:
Researcher uses natural
ordering or order of sampling
frame, selects an arbitrary
staring point, then selects items
at a preselected intervals.
Moderate cost
Moderately
used
Simple to draw sample;
easy to check
If sampling interval is related to
a periodic ordering of the
population, may introduce
increased variability.
Cluster sampling:
Researcher selects sampling
units at random, then does
complete observations of all
units in the groups
Low cost
Frequently used
If clusters
geographically defined,
yields lowest field cost;
requires listing of all
clusters but of
individuals only within
clusters
Larger error for comparable
size than other probability
samples.

5. COMPARISON OF NON – PROBABILITY SAMPLE
Description
Cost and Degree of
use
Convenience:
Researcher uses most
convenient sample or most
economical sample
Very low cost
Judgement:
An export or experienced
researcher selects the sample to
fulfill a purpose
Moderate cost
Quota:
Researcher classifies population
by pertinent properties,
determines desired proportion
of sample from each class
Moderate cost
Snowball:
Initial respondents are selected
by probability samples;
additional respondents are
obtained by referral from initial
respondents.
Low cost
Advantages
Disadvantages
No need to list of
population
Variability and bias of
estimates cannot be measured
or controlled
Useful for certain
types of forecasting
Bias due to experts’ beliefs
Introduces some
stratification of
population; requires
no list of population
Bias in researcher’s
classification of subjects.
Useful in locating
members of rare
populations
High bias because sample
units are not independent.
Extensively Used
Average use
Very extensively
used
Used in special
situations

6. 
Types of measurement scales are




Nominal Scale
Ordinal Scale
Interval scale
Ratio Scale

7. Nominal
Ordinal
Interval
Ratio
People or objects People or objects Intervals between There is a
withNominalOrdinalIntervalRatioPeople or objects withscale scale value are the same
the same
with a higher
adjacent the same rationale zero
on some attribute. The values of the scale have no 'numeric' meaning in the way that
scale value are
scale value have values are equal point for the
you usually think about numbers.People or objects with a higher scale value have more
the same on some The intervals between adjacent scale values arescale.
with respect the
of some attribute. more of some
indeterminate. Scale
attribute.
attribute being
assignment is by attribute. of "greater than," "equal to," or "less than."Intervals
the property
between adjacent scale values are equal with respect the the attribute being measured.
measured.
E.g., the difference between 8 and 9 is the same as the difference between 76 and
The77.There ofathe The intervals the scale. Ratios are equivalent, e.g., are ratio of 2
values is rationale zero point for
E.g., the
Ratios the
to 1 is the same as the ratio of 8 to 4.
scale have no
'numeric'
meaning in the
way that you
usually think
about numbers.
between adjacent difference
scale values are
between 8 and 9
indeterminate.
is the same as the
difference
Scale assignment between 76 and
is by the property 77.
of "greater than,"
"equal to," or
"less than."
equivalent, e.g.,
the ratio of 2 to 1
is the same as the
ratio of 8 to 4.

8. Nominal
Classification data:
e.g. Male / Female
No ordering:
e.g. it makes no sense
to state that M >
F
Arbitrary labels:
e.g., M/F, 0/1, etc
Ordinal
Interval
Ratio
Ordered but differences
Ordered, constant
between values are not
scale, but no
important
natural zero
Ordered, constant
scale, natural
zero
e.g., Political parties on
left to right spectrum
given labels 0, 1, 2
e.g., Likert scales, rank on
a scale of 1..5 your
degree of satisfaction
e.g., Restaurant ratings
e.g., Height,
Weight,
Age,
Length
Differences make
sense, but
ratios do not
e.g. Temperature
(C,F),
Dates