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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

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Research Methodology Sampling Scales

  • 1. 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
  • 2. 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
  • 5. Types of Data � Primary & Secondary � Quantitative & Qualitative Data Collection � Observation ,Survey ,Telephone, interview ,focus group(6-10). Questionnaire � Close and Open Ended
  • 6. Scales � For Measurement ,understanding,comparision . � Series of items arranged according to value for the purpose of quantification,numbers,symbols etc � A continuous spectrum
  • 7. 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)
  • 8. 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
  • 9. Primary Scales of Measurement
  • 10. 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
  • 11. 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
  • 12. � 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'.
  • 13. 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
  • 14. 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
  • 15. 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
  • 16. 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.
  • 17. 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.
  • 18. 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.
  • 19.
  • 20. 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
  • 21. 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).
  • 22. 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