Research Methodology
Sampling and Scales
By
PRADEEPKUMAR CHOKKANNAN
06.05.2021
Teaching Demonstration for
Recruitment for Assistant Professor ,Management
Sardar Vallabhbhai Patel International School Of Textiles & Management
Coimbatore

Types of Research Variables
• Independent variables (IV)
• Dependent variables (DV)
• Intervening variables
1. Mediating variable
• Moderating Variable
• Extraneous variables/Control variables
In real-life situations, there can be many factors or variables that
may affect the outcome. These variables are termed as
extraneous variables
• Confounding variable
If an extraneous variable is the real reason for an outcome
instead of independent variables

Mediation

Moderation

Types of Data
� Primary & Secondary
� Quantitative & Qualitative
Data Collection
� Observation ,Survey ,Telephone, interview
,focus group(6-10).
Questionnaire
� Close and Open Ended

Scales
� For Measurement ,understanding,comparision .
� Series of items arranged according to value for the purpose of
quantification,numbers,symbols etc
� A continuous spectrum

Scale characteristics
� Description/classification/label (attendendence register)
� Order or rank or > or < or = (100mtr Dash ranking)
� Distance (Age difference btw 1st brother and 2nd brother is 2
years,2nd brother and 3rd brother is 2 years )
� Origin has a fixed beginning or true zero not assumed
zero.(Income)

Primary Scales of Measurement
73 35
81
Scale
Nominal Numbers
Assigned
to Runners
Ordinal Rank Order
of Winners
Interval Performance
Rating on a
0 to 10 Scale
Ratio Time to Finish
in Seconds
Third
place
Second
place
First
place
8.2 9.1 9.6
15.2 14.1 13.4

Primary Scales of Measurement

The Sampling Design Process
A. Define the Population
B. Determine the Sampling Frame
C. Select Sampling Technique(s)
D. Determine the Sample Size
E. Execute the Sampling Process

Classification of Sampling Techniques
Sampling Techniques
Nonprobability
Sampling Techniques
Probability
Sampling Techniques
Convenience
Sampling
Judgmental
Sampling
Quota
Samplin
g
Snowball
Samplin
g
Systematic
Sampling
Stratified
Samplin
g
Cluster
Samplin
g
Simple Random
Sampling

� A probability sampling scheme is
one in which every unit in the
population has a chance (greater
than zero) of being selected in the
sample.
� Nonprobability sampling is any sampling method where
some elements of the population have no chance of
selection
� these are sometimes referred to as 'out of
coverage'/'undercovered'.

Convenience sampling attempts
to obtain a sample of
convenient elements.
Often, respondents are selected
because they happen to be in
the right place at the right
time.
Examples:
� Use of student
respondents
� Members of social
organizations
� “People on the street”
interviews
• Judgmental sampling is a
form of nonprobability
sampling in which the
population elements are
selected based on the
judgment of the research
• Eg:This is the area of
particular political party
favours

Quota sampling may be
viewed as two-stage
restricted judgmental
sampling.
1. The first stage consists
of developing quotas
of population
elements.
2. In the second stage,
sample elements are
selected based on
convenience or
judgment.
• In snowball sampling, an
initial group of
respondents is selected,
usually at random.
• Eg: Alcoholics

Simple Random
Sample:
�Each element in the
population has a
known and equal
probability of
selection
Systematic Sampling
�The sample is
chosen by selecting a
random starting point
and then picking
every ith (e.g. 5th,
10th) element in
succession from the
sampling frame

Stratified Sampling:
1.A two-step process in which the population is partitioned into
subpopulations, or strata.
� Every person in the population should be assigned to one and
only one stratum and no population elements should be
omitted.
2.Next, elements are selected from each stratum by a random
procedure, usually simple random sampling.
�A major objective of stratified sampling is to increase precision
without increasing cost.

Cont..
� The elements/people within a stratum (e.g. male) should be as
homogeneous as possible.
� The elements/people across strata (e.g. male, female) should be as
heterogeneous as possible.
• The stratification
variables should
also be closely
related to the
characteristic of
interest.

Cluster Sampling:
1.The target population is first divided into mutually exclusive and
collectively exhaustive subpopulations, or clusters.
2.Then a random sample of clusters is selected, based on a
probability sampling technique such as simple random sampling.
3.For each selected cluster, all the elements are generally included
in the sample.

Technique Strengths Weaknesses
Nonprobability Sampling
Convenience sampling
Least expensive, least
time-consuming, most
convenient
Selection bias, sample not
representative, not recommended for
descriptive or causal research
Judgmental sampling Low cost, convenient,
not time-consuming
Does not allow generalization,
Subjective, selection bias
Quota sampling Sample can be controlled
for certain characteristics
Selection bias, no assurance of
representativeness
Snowball sampling Can estimate rare
Characteristics, convenient
Selection bias, time-consuming
Probability Sampling
Simple random sampling
Representative,
results, projectable
Difficult to construct sampling
frame, expensive, lower precision
Systematic sampling Can increase
representativeness,
easier to implement than
SRS, sampling frame not
necessary
Can accidentally decrease
representativeness
Stratified sampling Include all important
subpopulations,
representative
Difficult to select relevant
stratification variables, not feasible to
stratify on many variables, expensive
Cluster sampling Easy to implement, cost
effective, more representative
Imprecise, difficult to compute and
interpret results
Choosing Nonprobability Vs. Probability Sampling

Validity and Reliability
• The ability of a scale to measure what was intended to be measured
is Validity
� Reliability of measure indicates extent to which it is without bias and
hence ensures consistent measurement across time (stability) and
across the various items in the instrument (internal consistency).

Old Rifle New Rifle New Rifle
Sun glare
Low Reliability High Reliability Reliable but Not
Valid Valid
(Target A) (Target B) (Target C)
Reliability and Validity on
Target