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Overview
Sampling and Sampling Process
Prof. Rafath Razia
An overview of sampling
What is the Meaning of Overview?
• According to Google, it is “a general review
or summary of a subject”
Is sampling used only in research?
• Sampling is part of everyday life.......
Population
• A complete set of persons or objects that
possess some common characteristic of
interest to the researcher
Population Groups
• Target Population: Entire group (people / objects) to
which the researcher wishes to generalize the
findings
• Accessible / Study Population: The available group
from which the researcher draws the sample
• Conclusions of the study are based on data obtained
from the accessible population, and statistical
inferences should be made only to the group from
which the sample was randomly selected
Population Groups and Sampling…….
7
Target Population
Accessible / Study Population
Sample
Samples
• Although researchers are always interested in
populations, an entire population is generally not
used in a research study
• In most nursing research studies, a sample or
subset of the population is selected to represent
the population
Samples
• When a sample is chosen properly, the researcher is
able to make claims about the population based on
data from the sample alone
• The method of sample selection and the sample size
determine how representative a sample is of the
population
Sampling- few terms
• Sampling is the process whereby a researcher
chooses a desired sample
• A single member of a population is called an
element
• The terms population member and population
element are used interchangeably
• Elements or members of a population are selected
from a sampling frame which is a listing of all
members of a population
• Examples: telephone directory, hospital census,
membership / voter list, etc,.
Inclusion and Exclusion Criteria
Inclusion criteria are a set of
predefined characteristics
used to identify subjects
who will be included in a
research study
Exclusion criteria are
characteristics that
eliminate a potential subject
from the study to avoid
extraneous effect
Uses of Sampling
Get information about large populations
Economical
More accuracy of results
High speed of data collection
Availability of population elements
Less field time
When it’s impossible to study the whole population
Few Disadvantages
Knowledge of the Researcher
Selection Bias posses threat to validity of study
Difficulty in getting truly representative sample
Non-cooperation
Inaccessibility
Drop-out
Reasons for collecting data from the
entire population
When population size is very small
When resources are extensive
When a very high response rate is
not expected
To Summarize Target
Population
Copyright ©2002, William M.K. Trochim
Types of Sampling Methods
Probability
Sampling Non-probability
Sampling
Probability Sampling
• Involves the use of a random selection process to
select sample from elements of population
• Without the use of random sampling procedures , the
ability to generalize the findings is greatly reduced
• The goal is to examine representative elements
• Inferential statistics may be used with greater
confidence
The Term- Random
• Can be confusing
• Dictionary definition suggests something that occurs
haphazardly or without direction
• Random sampling however is a very systematic,
scientific process
• Each population element has a known chance or
probability of being selected
• Selections are independent of each other
• Investigator’s bias does not enter into the selection
of the sample
Probability Sampling Methods
Random
Sampling
Process
Simple
Stratified
Cluster
Systematic
Simple Random Sampling
• The word “simple” does not mean easy or
uncomplicated
• Simple Random Sampling could be quite
complex and time consuming, especially if a
large sample is desired
Simple Random Sampling
• It is a type of probability sampling that
ensures each element of the population has
an equal and independent chance of being
chosen
• This method is generally used in at least one
phase of the other three types of random
sampling procedures
Steps of Simple Random Sampling
Identify the
accessible
population
Enumerate all
the elements
of the
population
(development
of sampling
frame)
Select a
method to
choose the
sample
(drawing
numbered
slips, use of
table of
random
numbers,
computer
generated list
of numbers)
Select sample
based on pre-
determined
size
Stratified Random Sampling
• Population is divided into subgroups, or strata,
according to some variable of importance to the
research study
• After the population is divided into two or more
strata, sample is selected from each strata using
simple random sampling method
• Example: marijuana usage among students
Stratified Random Sampling
Proportionate Disproportionate
A Sample Size of 1000
Proportionate
• High School 40%
• Intermediate 30%
• Degree 20%
• Post-graduation 10%
Disproportionate
• High School 25%
• Intermediate 25%
• Degree 25%
• Post-graduation 25%
Cluster Random Sampling
• When the population is geographically spread out,
sampling procedures may be difficult, impossible,
time consuming and expensive
• Hence, large groups or clusters become the sampling
units
Cluster Random Sampling
• The sample is selected from
clusters in two or more separate
stages
• The approach is also referred to
as multi-stage sampling
• During each phase of sampling
from the clusters, either simple,
stratified or systematic random
sampling may be used
Cluster Random Sampling
States
• Regions
• North, North-East, Central, East, West, South
Districts
• Regions / Zones / Hilly / Plain /Coastal / Border
• North, East, West, South
Hospitals
• Public Sector / Private Sector
• Teaching / Non-Teaching / Number of Beds
Cluster Random Sampling
• Although cluster sampling may be necessary for large
scale survey studies, the likelihood of sampling error
increases with each stage of sampling
• To compensate for the sampling error when cluster
sampling is used, larger samples should be selected
Systematic Random Sampling
• This involves selecting every kth element of the
population, such as every fifth, eighth, or twenty-
first element
Steps of Systematic Random Sampling
1
Obtain list
of total
population
(N)
2
Determine
sample size
(n)
3
Determine
the sample
width
(k)
4
Calculate
(k) by N/n
500/50=10
(k)
Systematic Random Sampling
• Controversial type of random sampling procedure
• May be classified either as probability or non-
probability sampling method
Systematic Random Sampling
• Two criteria are needed to be classified as
probability sampling:
1. Listing of the
population (sampling
frame) must be random
with respect to the
variable of interest
2. The first element or
member of the sample
must be elected
randomly
Non-probability Sampling Methods
• Here the sample elements are chosen from
the population by non-random methods
which is likely to produce a biased sample
• The investigator cannot estimate the
probability that each element of the
population will be included in the sample
• This restricts the generalizations that can
be made about the study findings
Non-probability Sampling Methods
Convenience
PurposiveQuota
Convenience Sampling
• Also referred to as accidental or incidental sampling
• Involves choosing readily available people or objects
for study
• This is probably the most frequently used sampling
method
• Saves time and money
Convenience Sampling
Convenience
Familiar
Group
Snowball /
Networking
Quota Sampling
• Similar to stratified random sampling
• Involves dividing the population into
homogenous strata
• Selecting sample elements from each of these
strata
Quota Sampling
Quota Sampling
• Obtains members through convenience
samples
Stratified Random Sampling
• Involves a random sampling method of
obtaining sample members
Purposive Sampling
• Involves handpicking of subjects
• Also called as judgemental sampling
• Subjects are chosen that the researcher believes are
typical or representative, of the accessible population
• Many qualitative research studies use purposive
sampling
Nieswiadomy RS., Foundations of Nursing Research
5th Edition Pearson Education 2009
Thanks

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An overview of sampling

  • 1. Overview Sampling and Sampling Process Prof. Rafath Razia
  • 3. What is the Meaning of Overview? • According to Google, it is “a general review or summary of a subject”
  • 4. Is sampling used only in research? • Sampling is part of everyday life.......
  • 5. Population • A complete set of persons or objects that possess some common characteristic of interest to the researcher
  • 6. Population Groups • Target Population: Entire group (people / objects) to which the researcher wishes to generalize the findings • Accessible / Study Population: The available group from which the researcher draws the sample • Conclusions of the study are based on data obtained from the accessible population, and statistical inferences should be made only to the group from which the sample was randomly selected
  • 7. Population Groups and Sampling……. 7 Target Population Accessible / Study Population Sample
  • 8. Samples • Although researchers are always interested in populations, an entire population is generally not used in a research study • In most nursing research studies, a sample or subset of the population is selected to represent the population
  • 9. Samples • When a sample is chosen properly, the researcher is able to make claims about the population based on data from the sample alone • The method of sample selection and the sample size determine how representative a sample is of the population
  • 10. Sampling- few terms • Sampling is the process whereby a researcher chooses a desired sample • A single member of a population is called an element • The terms population member and population element are used interchangeably • Elements or members of a population are selected from a sampling frame which is a listing of all members of a population • Examples: telephone directory, hospital census, membership / voter list, etc,.
  • 11. Inclusion and Exclusion Criteria Inclusion criteria are a set of predefined characteristics used to identify subjects who will be included in a research study Exclusion criteria are characteristics that eliminate a potential subject from the study to avoid extraneous effect
  • 12. Uses of Sampling Get information about large populations Economical More accuracy of results High speed of data collection Availability of population elements Less field time When it’s impossible to study the whole population
  • 13. Few Disadvantages Knowledge of the Researcher Selection Bias posses threat to validity of study Difficulty in getting truly representative sample Non-cooperation Inaccessibility Drop-out
  • 14. Reasons for collecting data from the entire population When population size is very small When resources are extensive When a very high response rate is not expected
  • 15. To Summarize Target Population Copyright ©2002, William M.K. Trochim
  • 16. Types of Sampling Methods Probability Sampling Non-probability Sampling
  • 17. Probability Sampling • Involves the use of a random selection process to select sample from elements of population • Without the use of random sampling procedures , the ability to generalize the findings is greatly reduced • The goal is to examine representative elements • Inferential statistics may be used with greater confidence
  • 18. The Term- Random • Can be confusing • Dictionary definition suggests something that occurs haphazardly or without direction • Random sampling however is a very systematic, scientific process • Each population element has a known chance or probability of being selected • Selections are independent of each other • Investigator’s bias does not enter into the selection of the sample
  • 20. Simple Random Sampling • The word “simple” does not mean easy or uncomplicated • Simple Random Sampling could be quite complex and time consuming, especially if a large sample is desired
  • 21. Simple Random Sampling • It is a type of probability sampling that ensures each element of the population has an equal and independent chance of being chosen • This method is generally used in at least one phase of the other three types of random sampling procedures
  • 22. Steps of Simple Random Sampling Identify the accessible population Enumerate all the elements of the population (development of sampling frame) Select a method to choose the sample (drawing numbered slips, use of table of random numbers, computer generated list of numbers) Select sample based on pre- determined size
  • 23. Stratified Random Sampling • Population is divided into subgroups, or strata, according to some variable of importance to the research study • After the population is divided into two or more strata, sample is selected from each strata using simple random sampling method • Example: marijuana usage among students
  • 25. A Sample Size of 1000 Proportionate • High School 40% • Intermediate 30% • Degree 20% • Post-graduation 10% Disproportionate • High School 25% • Intermediate 25% • Degree 25% • Post-graduation 25%
  • 26. Cluster Random Sampling • When the population is geographically spread out, sampling procedures may be difficult, impossible, time consuming and expensive • Hence, large groups or clusters become the sampling units
  • 27. Cluster Random Sampling • The sample is selected from clusters in two or more separate stages • The approach is also referred to as multi-stage sampling • During each phase of sampling from the clusters, either simple, stratified or systematic random sampling may be used
  • 28. Cluster Random Sampling States • Regions • North, North-East, Central, East, West, South Districts • Regions / Zones / Hilly / Plain /Coastal / Border • North, East, West, South Hospitals • Public Sector / Private Sector • Teaching / Non-Teaching / Number of Beds
  • 29. Cluster Random Sampling • Although cluster sampling may be necessary for large scale survey studies, the likelihood of sampling error increases with each stage of sampling • To compensate for the sampling error when cluster sampling is used, larger samples should be selected
  • 30. Systematic Random Sampling • This involves selecting every kth element of the population, such as every fifth, eighth, or twenty- first element
  • 31. Steps of Systematic Random Sampling 1 Obtain list of total population (N) 2 Determine sample size (n) 3 Determine the sample width (k) 4 Calculate (k) by N/n 500/50=10 (k)
  • 32. Systematic Random Sampling • Controversial type of random sampling procedure • May be classified either as probability or non- probability sampling method
  • 33. Systematic Random Sampling • Two criteria are needed to be classified as probability sampling: 1. Listing of the population (sampling frame) must be random with respect to the variable of interest 2. The first element or member of the sample must be elected randomly
  • 34. Non-probability Sampling Methods • Here the sample elements are chosen from the population by non-random methods which is likely to produce a biased sample • The investigator cannot estimate the probability that each element of the population will be included in the sample • This restricts the generalizations that can be made about the study findings
  • 36. Convenience Sampling • Also referred to as accidental or incidental sampling • Involves choosing readily available people or objects for study • This is probably the most frequently used sampling method • Saves time and money
  • 38. Quota Sampling • Similar to stratified random sampling • Involves dividing the population into homogenous strata • Selecting sample elements from each of these strata
  • 39. Quota Sampling Quota Sampling • Obtains members through convenience samples Stratified Random Sampling • Involves a random sampling method of obtaining sample members
  • 40. Purposive Sampling • Involves handpicking of subjects • Also called as judgemental sampling • Subjects are chosen that the researcher believes are typical or representative, of the accessible population • Many qualitative research studies use purposive sampling
  • 41. Nieswiadomy RS., Foundations of Nursing Research 5th Edition Pearson Education 2009