SAMPLING DESIGNthe process of selecting a subset.ppt

the process of selecting a subset (a sample) from a larger group (the population) to make observations and inferences about that population.

1. SAMPLING

2. Introduction
•Sampling is a complex and technical process.
In course of daily activities we get
information, make decisions and develop
predictions through sampling. Sampling is the
critical part of research design.

4. It is the totality of persons, objects, items or
anything pertaining to certain
characteristics.

A population is the entire aggregation of cases
in which a researcher is interested.
Whatever the basic unit, the population always
comprises the entire aggregate of elements in
which the researcher is interested
 
Population

5. Populations are not restricted to human
subjects. It might consist of all the hospital
records on file, blood samples, other
parameters etc .
Population can be divided into
Target population
Accessible population
 
POPULATION

6. Target population is the aggregate
of cases about which the
researcher would like to make
generalizations.
E.g.:- All the Diabetic patients in
TamilNadu is the target population

7. Accessible population is the aggregate of
cases that conform to the designated
criteria and that are accessible as a pool of
subjects for a study.
e.g:- The Diabetic patients who are in the
selected area are accessible population
“Researchers sample from Accessible
population and generalize to the Target
population”

8. Population –In Research defined as
•It refers to a total category of persons or objects that
meets the criteria for study established by the
researcher ,any set of persons, objects or objects
having an observable characteristics in common
•The criteria that specifies that people in population
must possess are called as Inclusion or Eligibility
criteria
•The characteristics that the people should not
possess is called Exclusion criteria

9. ELIGIBILITY CRITERIA
Researchers should be specific about
the criteria that define who is included
in the population.
Inclusion criteria:
The criteria that specify population
characteristics are referred to as
inclusion criteria.
Exclusion criteria:
These are the characteristics that
people must not possess.

10. –An element is the object about which or from which the
information is desired, e.g., the respondent.
–A sampling unit is an element, or a unit containing the
element, that is available for selection at some stage of
the sampling process.
–Extent refers to the geographical boundaries.
–Time is the time period under consideration.
–Sample: A fraction of population selected in any
manner is known as sample
 - Sampling: It is a process of selecting subjects
who are representative of population events,
behaviours or other elements with which to
conduct study.

11. Sampling frame
• List of all the sampling units from which
sample is drawn
–e.g. children < 5 years of age, households, health
care units…
Sampling scheme
• Method of selecting sampling units from
sampling frame

12. Sampling unit (element)
•Subject under observation on which
information is collected
–Example: children <5 years attending balwadi,
Ayanambakkam, hospital discharges after DHF,
Sampling fraction
•Ratio between sample size and population
size
–Example: 100 out of 2000 (5%)

13. •Strata- Refers to a mutually exclusive segment
of a population established by one or more
characteristics
•It is used for representativeness of the
sample
E.g:- All registered Nurse population can be
divided into 2 strata's (males & females)
•Sampling bias- Refers to the systematic over
population or under population of some
segment of the population in terms of a
characteristics relevant to the research
•This can be prevented by homogeneity

14. Sampling: central concepts
Universe
(theoretical target
population)
Population
(empirical target
population)
Original
sample
Final
sample
(data
sometimes called
sampling frame Sampling elements
The aim of sampling is to produce a miniature copy of the population. Each
member of the population has an equal likelihood of being selected into the
sample. Hence we can make inferences about the larger population based on the
sample
Sampling
units

16. An investigation of all the individual element that make up
the population, a total enumeration rather than a sample
•When a population is sampled in its entirety.
•No statistical testing is necessary. All needed are
descriptive statistics.
•Census conclusions are the conclusions about the
population.
• Collect and analyze data from every possible case or group
member
Census

17. Sample vs. Census
Table 11.1
Conditions Favoring the Use of
Type of Study

Sample Census

1. Budget


Small

Large
2. Time available

Short Long
3. Population size

Large Small
4. Variance in the characteristic

Small Large
5. Cost of sampling errors

Low High
6. Cost of nonsampling errors

High Low
7. Nature of measurement

Destructive Nondestructive
8. Attention to individual cases Yes No

18. Sampling method and Census method
Sampling method
Sample refers to selecting a
typical or representative
fraction or part of the
elements in the ‘universe’.
Sampling method requires
only a representative part
or portion of the ‘universe’
for the research purposes
Census methodCensus method
census is a complete census is a complete
enumeration of all the enumeration of all the
items in the ‘universe’ of items in the ‘universe’ of
study.study.


Census method refers to Census method refers to
selecting all the elements selecting all the elements
in the ‘universe’ as in the in the ‘universe’ as in the
case of national censuscase of national census

19. The need to sample
•Budget constraints prevent you from
surveying the entire population
•Time constraints prevent you from surveying
the entire population
•Impracticable to survey the entire population
•You have collected all the data but need the
results quickly

20. Purpose of sampling

21.  To draw conclusions about populations from
samples
 To determine population characteristics by
directly observing only a portion (or sample) of the
population.
 Population is so large and scattered.
 It offers high degree of accuracy.
 Results can be obtained shortly.
 Needs small portions.
 Economical one.
Purposes of sampling:

22. Principles of sampling: -
Sampling should be
Based on the objectives
Systematic
Clearly defined and easily identifiable
Used throughout the study
Based on sound criteria and avoid errors and bias.

23. Non-probability samples
Instances in which the
chances (probability) of
selecting members from the
population are unknown
Probability samples
One in which members of
the population have a
known chance (probability)
of being selected
Basic sampling classifications

26. PROBABILITY SAMPLING
•Hall mark- Is the random selection of elements from
the population
•Random assignment-Is the process of allocating the
subjects to different treatment conditions on a
random basis
•Random sampling-It involves a selection process in
which each element in the population has an equal
and independent chance of being selected

27. Process of probability sampling
•Identify a suitable sampling frame based on
your research questions or objectives
•Decide on a suitable sample size
•Select the most appropriate
sampling technique and select the sample
•check the sample is representative of the
population

28. •Simple random sampling: the
probability of being selected is “known
and equal” for all members of the
population
–Blind Draw Method (e.g. names “placed in
a hat” and then drawn randomly)
–Random Numbers Method (all items in the
sampling frame are given numbers,
numbers are then drawn using table or
computer program)
Probability sampling methods simple
random sampling

29. ADVANTAGES-SIMPLE RANDOM
SAMPLING
•The sample thus selected randomly is not subjected to the
bias of the researcher
•No chance for the operation of personal preferences
•Chance of selecting the markedly deviant sample is low ,but
the probability decreases as the size of the sample increases.
•It ensures that differences in the attributes of the sample and
the population are purely a function of chance.
•It requires minimum knowledge about the population in
advance

30. DIS- ADVANTAGE—SIMPLE RANDOM SAMPLING
•It tends to be a laborious process
•Time consuming-development of the frame, selection of the
elements etc.
•In actual practice , it is not used frequently since it is a
relatively inefficient procedure.
•It is also impossible to get a complete listing of every element
in the population, so other methods may be required.
•It does not involves the Knowledge of the Population which
researcher may have.
•No guarantee that a randomly selected sample will be
representative

32. SYSTEMATIC SAMPLING
•It involves the selection of every k th case
from some list or group such as every 10
th

person on the patient list or 100
th
person
listed in membership list.
•It is best used in situation ,unless the
population is narrowly defined , and the
sample is non probable in nature

33. SYSTEMATIC SAMPLING (cont)
•If the researcher has the sampling frame, the
following procedure can be used
•E .g:- the desired sample is ‘n’ & the total sample
is ’N’ then N/n gives the sampling interval k.
•For eg. 200 samples to be drawn from 40000
population, then
•K = 40000 / 200 = 200
•Note-The sampling interval is the standard
distance between the elements chosen for the
sample

34. •How to draw:
•1) Calculate SI,
• 2) Select a number between 1 and SI
randomly,
•3) Go to this number as the starting point and
the item on the list here is the first in the
sample,
•4) Add SI to the position number of this item
and the new position will be the second
sampled item,
•5) Continue this process until desired sample
size is reached.
Systematic sampling

35. SYSTEMATIC SAMPLING-
Advantages:-
•Identical to simple random sampling
•sample results are obtained in a more convenient
and efficient manner
Disadvantages:-
•Problem is that the list is arranged at intervals
coinciding with sampling interval
E .g:- if all the 5
th
element is
HeadNurse, she will either always or never be
included in the study

37. •Cluster sampling method by which the population
is divided into groups (clusters), any of which can be
considered a representative sample. These clusters
are mini-populations and therefore are
heterogeneous. Once clusters are established a
random draw is done to select one or more clusters
to represent the population. Area and systematic
sampling are two common methods.
Cluster sampling

38. CLUSTER SAMPLING
•Used for large scale survey
•It is used if it is impossible to obtain a listing of all the
element
•In cluster sampling – There is successive random
sampling of units
•The usual procedure is to sample successively such
as states ,cities ,districts, blocks, and their house
holds
•Also referred as multi- stage sampling
•The clusters can be selected either by simple or
stratified methods

39. CLUSTER SAMPLING-ADVANTAGES
•Considerably more economical and Practical.
•Used if the population is particularly large and widely
dispersed
•Considered ideal in large scale surveys
•Less field costs
•Less expensive and time consuming
•It does not requires a complete frame of the whole
population. It requires list of members of the selected clusters
only
DISADVANTAGES:-
•It contains more sampling errors than simple or stratified
sampling

40. –Divide the geographical area into sectors (sub areas) and
give them names/numbers, determine how many sectors
are to be sampled (typically a judgment call), randomly
select these sub areas. Do either a census or a systematic
draw within each area.
–To determine the total geo area estimate add the counts
in the sub areas together and multiply this number by the
ratio of the total number of sub areas divided by number
of sub areas.
Cluster sampling – Area method

41. Cluster Sample
Divide Population
into Clusters
–If Managers are
Elements then
Companies are
Clusters
Randomly Select
Clusters
Survey All or a
Random Sample of
Elements in Cluster
Companies (Clusters)
Sample

43. A two-step area cluster sample (sampling several
clusters) is preferable to a one-step (selecting only
one cluster) sample unless the clusters are
homogeneous

44. STRATIFIED SAMPLING
•It is a variant of simple random sampling in which the
population is divided into 2 or more strata's (sub
groups)
•Its aim is to enhance representativeness
•It subdivides the population into homogenous
subsets from where an appropriate number of
elements can be selected at a random
•Stratification can be based on the age ,gender ,etc
but the difficulty is the stratification of the attributes
will not be readily available.

45. •Stratified sampling: the population is separated
into homogeneous groups/segments/strata and a
sample is taken from each. The results are then
combined to get the picture of the total population.
•Sample stratum size determination
–Proportional method (stratum share of total sample is
stratum share of total population)
–Disproportionate method (variances among strata affect
sample size for each stratum)
. . .Cont

Cont . . .

46. STRATIFIED RANDOM SAMPLING- PROCEDURE
•1) To group to together those elements that belong to a
stratum and to select randomly the desired number of
elements.
•2)The researcher may take either equal or unequal number of
elements from each strata
•3)It guarantee the appropriate representation of different
segments of the population
•Sources of stratification- on InformationPatients listings,
Students roasters
•If the researcher might decide to select the subjects in
proportion to the size of the population then it is referred as
Proportionate stratified sampling

47. STRATIFIED RANDOM SAMPLING-
PROCEDURE (Cont)
•When the researcher is interested in understanding the
differences among the strata, Proportionate sampling may result
in an insufficient base for making comparison
•Disproportionate sampling designs may be used whenever an
intrastratum comparisons may be used between strata of greatly
unequal membership size
•When disproportionate sampling design is used it is necessary to
make an adjustment to the data to arrive at the best estimate of
overall population values which is referred as Weighing a simple
mathematical computation

48. MERITS- (STRATIFIED RANDOM SAMPLING)
•It offers the opportunity to sharpen the precision
and representative ness of the final sample
•In subpopulation whose membership is relatively
small , Stratification provides a means of including
sufficient number of cases in the sample by over
sampling of the stratum
•Characteristics of each stratum can be estimated
and hence comparisons can be made.

49. DIS- ADVANTAGE—STRATIFIED RANDOM
SAMPLING
•It is impossible if the information on the critical variables is
unavailable
•It requires even more labor and effort than simple random
sampling because the sample must be drawn from multiple
enumerated listings
•It is very costly to prepare stratified lists of all members
•There is always a probability of faulty classification and hence
increase in variability.

50. Stratified Sample
Divide Population into
Subgroups
–Mutually Exclusive
–Exhaustive
–At Least 1 Common
Characteristic of
Interest
Select Simple Random
Samples from
Subgroups
All Students
Part-time Full-time
Sample

52. Multistage sampling technique
•Primary area selection (towns)
•Sample location selection
•Chunk selection
•Segment selection
•Housing unit selection

53. Multistage sampling technique
Housing unit
Sample location
Thiruverkadu
Primary Area
kanchipuram
Segment - Streets
Chunk
Ayanambakkam
Centre - Chennai

54. NON PROBABILITY SAMPLING

55. Types –Non Probability sampling
Non –Probability
sampling
Convenience
sampling
Quota
sampling
Purposive
sampling

56. Convenient sampling
•Convenience samples: samples drawn at the
convenience of the interviewer. People tend to
make the selection at familiar locations and to
choose respondents who are like themselves.
•It uses the most conveniently available samples in
the study
Eg:- Teachers distributing Questionnaires
in the class
•Also referred as accidental sample
•Subjects may be atypical of the population with
regard to the critical variables being measured
•Individuals may not be known to the researchers

57. Convenient sampling (cont)
•Another type is Snow ball sampling or Network
sampling- it refers to that the early sample members
are asked to identify other people who meet the
eligibility criteria who must be otherwise difficult to
identify
E.g. :- Girls who have attained puberty in a year
period
• It is often considered as expedient but runs with
the risk of sampling bias

58. MERITS – (CONVINIENT SAMPLING )
•Samples are conveniently available
•Sampling is expedient
•Samples with specific traits are ruled through
referral by the participants
•Most often method used in Nursing discipline

59. DEMERITS- (CONVENIENT SAMPLING)
•It is the weakest form of sampling
•Bias are minimal if the sample are
homogenous within the population
•But in heterogeneous population ,there is no
other sampling approach in which the risk of
bias is greater.

60. QUOTA SAMPLING
•It uses strata of the population established by one or more
characteristics, where the researcher determine the
proportion of the element needed from the various segments
of the population
•It ensures that diverse segments of the population are
represented (establishing quotas)
•Stratas are determined by researchers judgement which
reflects important difference on the dependent variable
•Eg:- Out of 1000 samples only 200 is sample size, if male
proportion is high then there will be too many in the sample

61. MERITS – (QUOTA SAMPLING)
•The investigator can ensure that the diverse segments are
represented in the sample
•It avoids over representation or under representation of
stratas from the population
•It determines the correct number of samples from each strata
•It is less costly
•Can be considered in quantitative researches whose
resources prevent the use of probability sampling plan.

62. MERITS – (QUOTA SAMPLING) CONT
•It does not require any sophisticated skills
•Administratively easy
•Reduces sampling error
•Distortions in convenient sampling may be larger
as in the example of attitude of senior students
on AIDS which differs with the junior students.
•More preferable than convenient sampling

63. DEMERITS- (QUOTA SAMPLING)
•Demerits are same as convenient sampling
except samples are selected from strata.
•Selection bias is present

64. PURPOSIVE SAMPLING
•It is based on the belief that the researchers knowledge about
the population can be used to hand pick the cases to be
included in the sample
•The researcher might decide purposively to select the widest
possible variety of respondents or might choose subjects who
are judged to be atypical of the population in question
•E g:- Studying family members involved in the Nursing homes
or
•A newly developed instrument can be tested with the
purposive sampling of diverse types of people
•It is used when the researchers wants a sample of experts

65. MERITS AND DEMERITS
(PURPOSIVE SAMPLING)
Merits:-
•Simple to draw
•Less costly and less field work
•Convenient and economy
Demerits:-
•Always not reliable
•Human minds have difficulty in recognizing typical items
•Requires knowledge about population which he usually does
not posses
•Sampling in this subjective manner, provides no external,
objective method for assessing the typicalness of the selected
subjects

66. •Judgment samples: samples that require a
judgment or an “educated guess” on the part
of the interviewer as to who should
represent the population. Also, “judges”
(informed individuals) may be asked to
suggest who should be in the sample.
Subjectivity enters in here, and certain
members of the population will have a
smaller or no chance of selection compared
to others

67. •Referral samples (snowball samples) samples which
require respondents to provide the names of
additional respondents
–Members of the population who are less known, disliked,
or whose opinions conflict with the respondent have a
low probability of being selected.
•Quota samples samples that set a specific number
of certain types of individuals to be interviewed
–Often used to ensure that convenience samples will have
desired proportion of different respondent classes

68. EVALUATION OF NON-PROBABILITY SAMPLING
•Can be used and accepted for Pilot, exploratory, or
indepth qualitative study , the use of Non- Probabilty
samples is problematic
•Non-Probabilty samples are rarely representative of
the researchers target Population
•Not every element in the Population has a chance of
being included in the sample
•It does not requires skill, resources, time and
oppurtunity as reqired in probabilty designs

69. EVALUATION OF NON-PROBABILITY SAMPLING
(CONT)
•Must be cautious about the inferences and
conclusions drawn from the Non-Probability
designs
•With care in the selection of the sample,
conservative interpretation of the results and
replication of the study with new samples ,
researchers may find that Non-Probability
samples work reasonably well.

70. 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
Quota sampling Sample can be controlled
for certain characteristics
Selection bias, no assurance of
representativeness
Snowball sampling Can estimate rare
characteristics
Time-consuming
Probability sampling
Simple random sampling
(SRS)
Easily understood,
results projectable
Difficult to construct sampling
frame, expensive, lower precision,
no assurance of representativeness.
Systematic sampling Can increase
representativeness,
easier to implement than
SRS, sampling frame not
necessary
Can decrease representativeness
Stratified sampling Include all important
subpopulations,
precision
Difficult to select relevant
stratification variables, not feasible to
stratify on many variables, expensive
Cluster sampling Easy to implement, cost
effective
Imprecise, difficult to compute and
interpret results
Table 11.3
Strengths and Weaknesses of
Basic Sampling Techniques

71. The Sampling Design Process
Fig. 11.1
Define the Population
Determine the Sampling Frame
Select Sampling Technique(s)
Determine the Sample Size
Execute the Sampling Process

73.  
Define the target population

Select sampling frame

Determine whether probability or non probability sampling
method will be chosen

Plan procedure for selecting sampling units

Determine sample size and actual sampling units

Conduct field work
Sampling process (Steps in Selection
of a sample): -

74. 1. Defining the target population
 
Once the decision to sample has been made, the first
question related to sample, concerns identifying the target
population, that is the complete group of specific population
elements related to research project it is important to carefully
define the target population.
 
2. Sampling frame
 
This is the list of elements from which a sample may be
drawn.
E.g. Class attendance register.
 

75. 3. Method of sampling
Method should be important because the total student
body is geographically concentrated and their
reasonably accurate list of their population.
4. Procedure for sampling
 
During the actual sampling process the elements of
the population according to the certain procedure
sample units are selected. The sampling units is a
single element or group of elements.

76.  
5. Select actual sampling Units
 
Sample of 30 and more are consider as large
sample, less than 30 is known as small sample.
 
After determining the sample size the actual
sampling units are selected for the study.
 
6. Conduct field work

After selecting actual sampling units field work are
carried out.

77. •Random online intercept sampling: relies on a
random selection of Web site visitors
•Invitation online sampling: is when potential
respondents are alerted that they may fill out a
questionnaire that is hosted at a specific Web site
•Online panel sampling: refers to consumer or other
respondent panels that are set up by marketing
research companies for the explicit purpose of
conducting online surveys with representative
samples
Online sampling techniques

78. Guidelines for selecting a sample size (Gay and Airasian
2003)
•For small populations with fewer than 100 people or
other units, there is little point in sampling. Survey
the entire population.
•If the population size is around 500,50% of the
population should be sampled.
•If the population size is around 1,500,20% should be
sampled.
•Beyond a certain at about 5,000 units or more, the
population size is almost irrelevant, and a sample
size of 400 should be adequate.

79. •Sample plan: definite sequence of steps that
the researcher goes through in order to draw
and ultimately arrive at the final sample
Developing a sample plan

80. •Step 1 Define the relevant population.
»Specify the descriptors, geographic locations, and time
for the sampling units.
•Step 2 Obtain a population list, if possible;
may only be some type of sample frame
»List brokers, government units, customer lists,
competitors’ lists, association lists, directories, etc.

81. Step 2
–Incidence rate (occurrence of certain
types in the population, the lower the
incidence the larger the required list
needed to draw sample from)

82. •Step 3: Design the sample method (size and
method).
•Determine specific sampling method to be
used. All necessary steps must be specified
(sample frame, sample size, recontacts, and
replacements)
•Step 4 Draw the sample.
»Select the sample unit and gain the information

83. •Step 4
»Drop-down substitution
»Over sampling
»Resampling
•Step 5:Assess the sample.
»Sample validation – compare
sample profile with population
profile; check non-responders
•Step 6:Resample if necessary.

84. •Errors in Sampling
Sampling frame error: error that occurs when
certain sample elements are excluded or
when the total population is not accurately
represented in the sampling frame
Random sampling error: the difference
between the sample result and the result of a
census conducted using identical procedures;
a statistical fluctuation that occurs because
of chance variation in the elements selected
for the sample

85. Random sampling error is a function of sample size.
As sample size increase, sampling error decreases.
•Non sampling error (systematic error): error
resulting from some imperfect aspect of the
research design that causes response error or from
a mistake in the execution of research; error that
comes from such sources as sample bias, mistakes
in recording responses, and non responses from
persons who were not contacted or who refused to
participate.

86.  
When the sample is so chosen that some
elements are more likely to be represented their other
elements, it is called biased sample.
 
Sampling error: -
 
Type – I (alpha)- Rejection of null hypothesis if its
true
Type – II (beta)- Acceptance of a null hypothesis
if its actually false.
 
Biased Sample: -

87. Type 1 error
•The probability of finding a difference with our
sample compared to population, and there
really isn’t one….
•Known as the α (or “type 1 error”)
•Usually set at 5% (or 0.05)

88. Representativeness (validity)
•A sample should accurately reflect distribution of
•relevant variable in population
•Person e.g. age, sex
•Place e.g. urban vs. rural
•Time e.g. seasonality
•Representativeness essential to generalise
•Ensure representativeness before starting,
•Confirm once completed

89. Problems with process of sampling
•Subject withdrawal from the study
•Lost follow up
•Exclusion / inclusion criteria limit applicability
of the sample
•Incomplete data
Problem with samples
•Bias in subject recruitment
•Selectivity
•Response rate

90. Conclusion
It can be said that using a sample in research
saves mainly money and time if a suitable
sampling strategy is used, appropriate sample
size selected and necessary precautions taken
to reduce on sampling and measurement
errors, then a sample should yield valid and
reliable information.

91. THANK YOU