B.Sc NURSING III YEAR

1. PRESENTED BY:
MRS. DEVA PON PUSHPAM.I,
ASSISTANT PROFESSOR.

2. SAMPLING
TOPICS COVERED:
Definition of population
Sample
Sampling criteria
Factors influencing sampling process
Types of sampling techniques

3. INTRODUCTION
 Researchers almost always obtain data from
samples.
 The process of calculating sample size and a sample
using appropriate sampling method is crucial to all
scientific studies.
 A study can be done on entire population (census)
or on a sample of it.
 Depending on the study objectives, accessibility of
the study population, availability of resources and
necessary skills, entire population or part of the
population may be studied.

4. TERMINOLOGIES
 POPULATION: It is the entire aggregation of cases in
which a researcher is interested. Eg. If the study is on
Indian nurses with doctoral degrees, the population could
be defined as all Indian citizens who are registered nurses
and have a Ph.D.
 TARGET / THEORETICAL / REFERENCE
POPULATION: It is the aggregate of cases about which
the researcher would like to generalize. Eg. If the research
is on TB patients, the target population would be all the
TB patients in the world.
 ACCESSIBLE POPULATION: It is the aggregate of
cases that conform to designated criteria and that are
accessible for the study. Eg. All the TB patients registered
under RNTCP in India.

5. CONTD.,
 SAMPLE /STUDY POPULATION: It is defined as
representative unit of a target population. It is a subset of
the population elements. Eg. TB patients registered under
RNTCP in India who possess the characteristics mentioned
in the eligibility criteria.
 SAMPLING CRITERIA / ELIGIBILITY CRITERIA:
The criteria that specify the population characteristics are
the eligibility criteria or inclusion criteria. The
characteristics the population must not possess are the
exclusion criteria. Eg. Exclude people who cannot read
English.
 SAMPLING: It is the process of selecting a representative
segment or the subset of the population under study.

6. CONTD.,
 SAMPLING UNIT: It is a well defined, non-overlapping
collection of population of target or accessible population
that can be identified and traced or reached.
 SAMPLING FRAME: It is the list of all the elements or
subjects in the population from which the sample is drawn.
It could be prepared by the researcher or an existing frame
may be used. Eg. Prepare a list of all the households of a
locality which have pregnant women or may use a register
of pregnant women available with the local anganwadi
worker.
 SAMPLING ERROR: There may be fluctuations in the
values of the statistics of characteristics from one sample to
another, or even those drawn from the same population.

7. SAMPLING PROCESS
Identify the population of interest
Define target and accessible population
Construct sampling frame
Specify the sampling unit
Determine the sample size
Choose a sampling technique
Specify the sampling plan
Select a desired sample

8. FACTORS INFLUENCING
SAMPLING PROCESS
NATURE OF
THE
RESEARCHER
• Inexperienced investigator
• Lack of interest
• Lack of honesty
• Intensive workload
• Inadequate supervision
NATURE OF
THE SAMPLE
• Inappropriate sampling
technique
• Sample size
• Defective sampling frame
CIRCUMSTAN
CES
• Lack of time
• Large geographic area
• Lack of cooperation
• Natural calamities

9. TYPES OF SAMPLING TECHNIQUE
SAMPLING
TECHNIQUE
PROBABILITY
SAMPLING
TECHNIQUE
NON
PROBABILITY
SAMPLING
TECHNIQUE

10. PROBABILITY SAMPLING
TECHNIQUE
 It is based on the theory of probability.
 It involves the random selection of elements / members of
the population.
 In this, every subject in a population has equal chance to be
selected as study sample.
 It enhance the representativeness of the selected sample for
a study.
 The chances of systematic bias is relatively less.

11. TYPES OF PROBABILITY SAMPLING
TECHNIQUES
TYPES
Simple
random
Stratified
random
Systematic
random
Cluster /
multistage
Sequential

12. SIMPLE RANDOM SAMPLING
 This is the most basic probability sampling design.
 In this type of sampling design, every member of a
population has an equal chance of being selected as subject.
 Sampling error can be minimized or eliminated through
random selection of sampling units.
 The essential prerequisites are: the population must be
homogeneous and researcher must have list of the elements /
members of the accessible population.
 The samples are drawn using: lottery method, use of
table of random numbers and the use of computer.
Eg. If a sampling frame has 50 population and the sample size
is 20, then 20 subjects will be randomly picked up.

14. STRENGTHS:
 Give more representative sample.
 Reduce the chances of researcher / subjective bias.
 Helpful to draw sample from large population.
 Every member is given equal opportunity of being selected.
 The most unbiased method.
 Easily computed.
LIMITATIONS:
 Require up-to-date list of all the members of the population.
 Does not make of use of knowledge about a population.
 Researcher need to be computer friendly.
 Expensive, time consuming and lots of procedures need to
be done before sampling is accomplished.

15. STRATIFIED RANDOM SAMPLING
 This method is used for heterogeneous population.
 The population is first divided into two or more strata, with
the goal of enhancing representativeness.
 The population will be subdivided into homogeneous subsets
from which elements are selected at random.
 The strata formation may be based on any characteristics of
the population (age, gender, education, religion, etc).
PROPORTIONATE STRATIFIED RANDOM SAMPLING:
In this the researcher select a pre-specified and equal
percentage (portion) of sample selected from each strata.
Eg. Researcher has 3 strata with 100, 200 and 300 population
sizes respectively. The researcher decided 50% from each
strata. The researcher must select 50, 100 and 150 subjects
from each stratum respectively.

16. CONTD.,
DISPROPORTIONATE STRATIFIED RANDOM
SAMPLING:
 In this subtype, the sample chosen from each stratum
are not in proportion to size of total population in
that stratum.
 Different strata has different sampling fractions.
 If the researcher commits mistakes in allotting
sampling fractions, a stratum may either be
overrepresented or underrepresented, which will
result in skewed results.
Eg. Researcher has 3 strata with 100, 200 and 300
population sizes respectively. The researcher decided
50 subjects from each strata.

18. STRENGTHS:
 Good approach to study a large proportion of population.
 Ensures representation of all groups in a population.
 There is higher statistical precision.
 Inexpensive in terms of money, efforts and time.
LIMITATIONS:
 Researcher should have prior knowledge about proportion
of population in each stratum.
 More efforts required to prepare strata.
 Possibility of faulty classification and hence increase in
variability.
 Different sampling technique should be used for small size
population.

19. SYSTEMATIC RANDOM SAMPLING
 Systematic sampling is helpful to draw a sample from an
ordered list of population.
 This involves the selection of every kth case from the list of
population.
 The sampling interval (k)is the standard distance between
sampled elements.
 The desired sample size is established at some number (n).
 The size of the population must be known or estimated (N).
 By dividing N by n, a sampling interval k is established.
 k = Population size (N) / Desired sample size (n)
Eg. If 200 sample must be drawn from a population of 40,000,
then sampling interval would be:
k = 40,000 / 200 = 200 (every 200th population)

21. STRENGTHS:
 More efficient and convenient.
 Easy and time efficient and appropriate for manual
selection of sample.
 In homogeneous population, a more representative
sample can be expected.
LIMITATIONS:
 Does not give equal opportunity for sample selection,
hence, bias is possible.
 Researcher need to have complete list of element to
calculate sample interval.
 Laborious and time consuming.

22. CLUSTER / MULTISTAGE SAMPLING
 Cluster / multistage sampling is an appropriate option to
choose sample from a large geographical distributed
population.
 This is successive in nature and proceed from large to small
sample.
 It involves selecting broad groups (clusters) rather than
selecting individuals.
 Clusters can be selected by simple or stratified methods.
 The resulting design can be described in terms of the
number of stages (eg. Three stage sampling)
Eg. For a sample of nursing students, first draw a random
sample of nursing colleges and then draw a sample of
students from the selected colleges.

24. STRENGTHS:
 This is appropriate to study large and wide scattered
population.
 Cheap, quick and easy for large population.
 Helpful to develop insight of different region / zone.
LIMITATIONS:
 Will give least representative sample.
 Possibility of high sampling error.

25. SEQUENTIAL SAMPLING
 In this method, the sample size is not fixed.
 The investigator initially selects small sample and tries out
to make inferences; if not able to draw results, then add
more subjects until clear-cut inferences can be drawn.
Eg. To study the association between smoking and lung
cancer, initially researcher takes a smallest sample and tries
to draw inferences. If unable to draw any inferences then the
researcher continues to draw the sample until meaningful
inferences are drawn.

27. STRENGTHS:
 Facilitates to conduct study on the best possible smallest
representative sample.
 Helping in ultimately finding the inferences of the study.
LIMITATIONS:
 Not possible to study a phenomenon which needs to be
studied at one point of time.
 Requires repeated entries into the field to collect the
sample.

28. NONPROBABILITY SAMPLING
TECHNIQUE
 Nonprobability sampling is less likely to produce accurate
and representative samples.
 This does not give all the individuals in the population an
equal chances of being selected because elements are
chosen by choice not by chance.
 Despite this fact, most studies in nursing and other health
disciplines rely on nonprobability samples.

29. TYPES OF NONPROBABILITY
SAMPLING TECHNIQUE
Purposive
Convenience
ConsecutiveQuota
Snowball

30. PURPOSIVE SAMPLING TECHNIQUE
 Purposive sampling is most commonly known as ‘judgmental’
or ‘authoritative’ sampling.
 This uses researcher’s knowledge about the population to
make selections.
 Researchers might decide purposely to select people who are
judged to be particularly knowledgeable about the issues
under study.
 This is often based upon factors such as participant’s
knowledge, experience and role.
Eg. A research about the lived experiences of post disaster
depression among people living in earthquake affected areas.
The samples should be the victims of the earthquake disaster
and have suffered post disaster depression living in those
areas.

31. PURPOSIVE SAMPLING

32. STRENGTHS:
 Simple to draw samples and useful in explorative studies.
 Saves resources and requires less fieldwork.
LIMITATIONS:
 Requires knowledge about the population.
 Conscious biases may exist.
 Sampling are with the authority.
 No way to evaluate the reliability of the expert or the
authority.
 May have misrepresentation of the entire population and
limit generalization of the results.

33. CONVENIENCE SAMPLING
TECHNIQUE
 Convenience sampling is otherwise called as ‘incidental’ or
‘accidental’ sampling.
 This is the weakest form of sampling.
 This entails using the most readily or conveniently available
people as participants.
 This is the most preferred sampling in nursing and social
sciences.
Eg. Researchers seeking people with certain characteristics
uses convenient approach and place an advertisement in a
newspaper, put up signs in clinic or post messages on online
social media.

35. STRENGTHS:
 Easy, cheapest and least time consuming.
 Helpful to draw desired number of samples from big
population.
 Appropriate for homogeneous population.
LIMITATIONS:
 Not appropriate for heterogeneous population.
 More bound to researcher’s bias.
 Weak sampling approach because of not using any method
to select sample.

36. CONSECUTIVE SAMPLING
TECHNIQUE
 It is also known as ‘total enumerative’ sampling.
 It involves recruiting all of the people from an accessible
population who meet the eligibility criteria over a specific
time interval, or for a specified sample size.
 This makes the better representation of the entire
population.
 This is a good approach for ‘rolling enrollment’ into an
accessible population.
Eg. In a study of Ventilator Associated Pneumonia in ICU
patients, if the accessible population were patients in an ICU
of a specified hospital, a consecutive sample might consist of
all eligible patients admitted to that ICU over a 6 month
period. Or, it might be the first 250 eligible patients
admitted to the ICU, if 250 were the targeted sample size.

38. STRENGTHS:
 Ensures more representativeness of the selected sample.
 Appropriate approach when the data collection period is
sufficiently long.
 Easy, not expensive and not workforce intensive.
 Less opportunity for subjective bias in sample selection.
LIMITATIONS:
 No set plans about sample size and sampling schedule.
 There may be variation in selecting of sample over a
different time / period of interval.

39. QUOTA SAMPLING
 A quota sample is one in which the researcher identifies
population strata and determines how many participants are
needed from each stratum.
 By using information about population characteristics (age,
gender, education, religion, race, etc.), researchers can ensure
that diverse segments are represented in the sample, in the
sample, in the proportion in which they occur in the
population.
 It is a derived version of stratified sampling without
randomization of the subjects.
Eg. If the researcher needs 100 samples from B.Sc Nursing
course, then 25 from each year of the course will be selected.

41. STRENGTHS:
 Easy, inexpensive and time efficient.
 Appropriate for large population.
 Helpful to draw representative sample from a
homogeneous population.
LIMITATIONS:
 Researcher bias is more frequent.
 Not suitable for heterogeneous population.
 Generalization is questionable.

42. SNOWBALL SAMPLING
 Otherwise called as ‘network sampling’ or ‘chain sampling’.
 With this approach, early sample members (called ‘seeds’)
are asked to refer other people who meet the eligibility
criteria.
 This is an appropriate approach to study the population
difficult to locate (substance abusers, commercial sex
workers, etc.,).
Eg. If a researcher is interested to know the extent of
substance abuse in a particular district, then snowball
sampling will be used to locate the substance abusers.

43. TYPES OF SNOWBALL SAMPLING
 LINEAR / SINGLE CHAIN SNOWBALL SAMPLING: In
this type, the early sample refer or register only one next
sample for study and at the end of completion a single chain
will be formed.
 EXPONENTIAL NONDISCRIMINATIVE SNOWBALL
SAMPLING: In this type, the early sample is requested to
refer at least two next samples for study. Later on these two
samples will register more samples and the chain will keep
continuing till the sample size is reached.
 EXPONENTIAL DISCRIMINATIVE SNOWBALL
SAMPLING: in this, initially one sample is selected and
asked for two references of similar subjects, out of which at
least one subject must be active to provide further references
and another could be non active in providing references.

45. STRENGTHS:
 Helps to locate extreme and rare case or phenomenon.
 Easy, economic and convenient method to identify and
recruit difficult population.
LIMITATIONS:
 Researcher has little control over the sampling method.
 Gives less representative samples.
 Researcher has no idea of the distribution of the population.
 Difficult to complete desired sample size if the initial
samples fail to register new samples.
 Sampling bias is possible as the subjects may share the
subjects with same traits and characteristics.

46. PROBLEMS IN SAMPLING
 Sample representativeness
 Sample size analysis problem
 Lack of resources
 Lack of knowledge of sampling process
 Lack of support
 Sampling bias