The lecture is based on Research Methodology Sample and its technique

1. Sample and Sample Technique
Dr. Md. Anisur Rahman (Anjum)
Professor & Head, Department of Ophthalmology
Dhaka Medical College, Dhaka

2. What is Cluster sampling?
Cluster sampling is defined as a sampling technique in which the
population is divided into already existing groupings (clusters), and then
a sample of the cluster is selected randomly from the population.

3. Cluster sampling
The term cluster refers to
a natural, but heterogeneous,
intact grouping of the members of the population.

4. Cluster sampling: variables used in the clustering
 population are the geographical area, buildings, school, etc.
 Heterogeneity of the cluster is an important feature of an ideal cluster
sample design.

5. Sub-types of cluster sampling
One-Stage Cluster Sample
One-stage cluster sample occurs when the researcher includes all the high
school students from all the randomly selected clusters as sample.
Two-Stage Cluster Sample
From the same example above, two-stage cluster sample is obtained when
the researcher only selects a number of students from each cluster by using
simple or systematic random sampling.

6. One-stage Cluster sampling with m=5
Two-Stage Cluster Sampling with m=5,
Ni= 3

7. Advantages of Cluster sampling
 Cluster sampling is less expensive and quicker
 Cluster Sample permits each accumulation of large samples.
 Cluster sample may combine the advantages of both random sampling
as well as stratified sampling.
 Cluster sampling procedure enables to obtain information from one or
more areas.

8. Disadvantages of cluster sampling
 In a cluster sample, each cluster may be composed of units that are like one
another. This may produce large sampling error and reduce the
representativeness of the sample.
 In Cluster sampling, when unequal size of some of the subsets is selected,
an element of sample bias will arise.
 This type of sampling may not be possible to apply its findings to another
area.

9. Basis for comparison Stratified Sampling Cluster Sampling
Meaning Stratified sampling is one, in
which the population is divided
into homogeneous segments, and
then the sample is randomly taken
from the segments
Cluster sampling refers to a
sampling method wherein the
members of the population are
selected at random, from naturally
occurring groups called 'cluster'.
Sample Randomly selected individuals are
taken from all the strata.
All the individuals are taken from
randomly selected clusters.
Selection of
population elements
Individually Collectively
Homogeneity Within group Between groups
Heterogeneity Between groups Within group
Bifurcation Imposed by the researcher Naturally occurring groups
Objective To increase precision and
representation.
To reduce cost and improve
efficiency.

10. Multi-stage sampling
Say we like to survey diabetic retinopathy among Bangladeshi. It is not
possible to survey all the population in Bangladesh. So what should we
do? According to the last census total population of Bangladesh is 150
million. Among the total population 25% is in urban area and 75% is in
village area. If our sample size is 100,000 populations in our study there
will be 25.000 from the city and 75,000 from the rural area.

11. Multi-stage sampling
• There are eight divisions in Bangladesh. Every division is again
divided into several districts. Then Thana, Union, village, Moholla and
Mouja (Mouja is a define area where population is present or not, but
Moholla is also Mouja but there have to some population. Example: A
certain part of a forest or river may be a Mouja but not Moholla
because no population lives there)

12. Multi-stage sampling
We select all the division and select two districts randomly from each
division. So, we have 16 districts, now select two Thana randomly from
each district, so 32 Thana is selected; from Thana we select two
Moholla from each Thana. So we select 64 Moholla which represent
75% of the population means rural population. In the same way we
select 25% urban population.

13. Multi-stage sampling
Bangladesh has some population of different ethnic group say they are
one percent of the total population. During the sampling some of the
ethnic population has to included randomly, otherwise the study will
not represent the whole population of the country.

14. Advantages of Multi-stage sampling
 Cost and speed that the survey can be done in
 Convenience of finding the survey sample
 Normally more accurate than cluster sampling for the same size
sample

15. Disadvantages of Multi-stage sampling
 Not as accurate as Simple Random Sample[ambiguous] if the sample
is the same size
 More testing is difficult to do

16. NON-PROBABILITY SAMPLE

17. What is Non-Probability Sampling?
 Non-probability sampling is a sampling technique where the samples are
gathered in a process that does not give all the individuals in the population
equal chances of being selected.
 In any form of research, true random sampling is always difficult to
achieve.
 Most researchers are bounded by time, money and workforce and because of these
limitations, it is almost impossible to randomly sample the entire population and it
is often necessary to employ another sampling technique, the non-probability
sampling

18. What is Non-Probability Sampling?
• In contrast with probability sampling, non-probability sample is not a product of a
randomized selection processes. Subjects in a non-probability sample are usually
selected on the basis of their accessibility or by the purposive personal judgment
of the researcher.
• The downside of the non-probability sampling method is that an unknown
proportion of the entire population was not sampled. This entails that the sample
may or may not represent the entire population accurately. Therefore, the results of
the research cannot be used in generalizations pertaining to the entire population.

19. When to Use Non-Probability Sampling
• This type of sampling can be used when demonstrating that a particular trait
exists in the population.
• It can also be used when the researcher aims to do a qualitative or pilot study.
• It can be used when randomization is impossible like when the population is
almost limitless.
• It can be used when the research does not aim to generate results that will be
used to create generalizations pertaining to the entire population.
• It is also useful when the researcher has limited budget, time and workforce.
• This technique can also be used in an initial study which will be carried out again
using a randomized, probability sampling.

20. There are five types of non-probability:
i. Convenience Sampling
ii. Sequential sampling
iii. Quota Sampling
iv. Judgmental Sampling
v. Snowball sampling

21. Convenience Sampling
• Convenience sampling (also known as availability sampling) is a non-
probability sampling technique where subjects are selected because
of their convenient accessibility and proximity to the researcher.
• The subjects are selected just because they are easiest to
recruit for the study and the researcher did not consider selecting
subjects that are representative of the entire population.

22. In all forms of research, it would be ideal to test the entire population,
but in most cases, the population is just too large that it is impossible
to include every individual. This is the reason why most researchers rely
on sampling techniques like convenience sampling, the most common
of all sampling techniques. Many researchers prefer this sampling
technique because it is fast, inexpensive, easy and the subjects are
readily available.

23. Application of convenience sampling
Application of convenience sampling is the easiest compared to other
sampling methods. Suppose, your topic is: A study into the
sustainability of viral marketing as a marketing tool in the future.

24.  Using convenience sampling method, you can send a link to the online
questionnaire to individuals on your mobile phone’s contact list, to
individuals you are connected to via social networking websites such
as Face book, LinkedIn, Google+ and to individuals whom you know
in person. This would be the easiest and the most convenient way of
recruiting the sources of the primary data for your research.

25. Advantages of convenience sampling
 Simplicity of sampling and ease of research
 Helpful for pilot studies and for hypothesis generation
 Data collection can be facilitated in short duration of time
 Cost effectiveness

26. Disadvantages of Convenience Sampling
 Highly vulnerable to selection bias.
 Generalizability unclear
 High level of sampling error

27. Notes
When using convenience sampling, it is necessary to describe how
sample would differ from an ideal sample that was randomly selected.
It is also necessary to describe the individuals who might be left out
during the selection process or the individuals who are
overrepresented in the sample.

28. Notes
• In connection to this, it is better if we can describe the possible
effects of the people who were left out or the subjects that are
overrepresented to results. This will allow the readers of researcher
to get a good grasp of the sample that we were testing. It will also
enable them to estimate the possible difference between results and
the results from the entire population.

29. Sequential sampling: How it is conducted?
Sequential sampling is a non-probability sampling technique wherein
the researcher picks a single or a group of subjects in a given time
interval, conducts his study, analyzes the results then picks another
group of subjects if needed and so on.

30. Sequential sampling: How it is conducted?
Initially developed as a tool for product quality control the sample
size, n, is not fixed in advanced, nor is the timeframe of data
collection. The process begins, first, with the sampling of a single
observation or a group of observations. These are then tested to see
whether or not the null hypothesis can be rejected. If the null is not
rejected, then another observation or group of observations is sampled
and the test is run again. In this way the test continues until the researcher
is confident in his or her results.

31. Where it is conducted?
Sequential modeling is best used to test quality control – food reserves,
water purity, and industrial products, for example. It does not require
sampling at the same time point. Nor does it require large samples at
any particular time.

32. Where it is not conducted?
Most of the standard social, economic, political, and health related
questions, on the other hand, do require large sample sizes over the
same timeframe. If interested in any of the standard social or health
related questions – income disparity, household savings, health
inequalities, political processes, racial/ethnic or urban/rural
differences, then sequential modeling is probably not the correct
sampling option.

33. Advantages of Sequential Sampling Method
This sampling technique gives the researcher limitless chances of fine tuning
his research methods and gaining a vital insight into the study that he is
currently pursuing.
• This technique can reduce sampling costs by reducing the number
of observations needed. If a whole batch of light bulbs is defective,
sequential sampling can allow us to learn this much more quickly and
inexpensively than simple random sampling. However, it is not a random
sample and has other issues with making statistical inference.

34. Disadvantages of Sequential Sampling
This sampling method is hardly representative of the entire population.
Its only hope of approaching representativeness is when the researcher
chose to use a very large sample size significant enough to represent a
big fraction of the entire population.
• The sampling technique is also hardly randomized. This
contributes to the very little degree representativeness of the
sampling technique.
• Due to the aforementioned disadvantages, results from this
sampling technique cannot be used to create conclusions and
interpretations pertaining to the entire population

35. Difference of Sequential Sampling from All Other Sampling
Techniques
If we are to consider all the other sampling techniques in research, we
will all come to a conclusion that the experiment and the data analysis
will either boil down to accepting the null hypothesis or disproving the
null hypothesis while accepting the alternative hypothesis.

36. Difference of Sequential Sampling from All Other
Sampling Techniques
 In sequential sampling technique, there exists another step, a third
option. The researcher can accept the null hypothesis, accept his
alternative hypothesis, or select another pool of subjects and conduct
the experiment once again. This entails that the researcher can obtain
limitless number of subjects before finally making a decision whether
to accept his null or alternative hypothesis.

37. What is quota sample?
Quota sampling is a method for selecting survey participants that is a non-
probabilistic version of stratified sampling
• How to create a quota sample?
• To create a quota sample, there are three steps:
a) Choosing the relevant stratification and dividing the population
accordingly;
b) Calculating a quota for each stratum; and
c) Continuing to invite cases until the quota for each stratum is met.

38. Quota sampling
 Let’s say that we want to understand more about the career goals of
students at Dhaka College. In particular, we want to look at the
differences in career goals among the students of “Arts” Science” and
“Commerce” groups at Dhaka College. If Dhaka College has 2,000
students, which is our population

39. Quota sampling
• Step: 1.
choose the relevant stratification and divide the population accordingly.
• So, we have to divide the student according to their group (Here are three
groups: science, arts, commerce)
• Step: 2.
Calculate a quota for each stratum

40. Quota sampling
• The number of cases that should be included in each stratum will vary
depending on the actual number of students in three different groups. If
we find that there are 800 science students (40%), 600 commerce students
(30%) and 600 (30%) arts students in Dhaka College. This means that our
sample must also meet these proportions. If we want to sample 1,000
students, this means that we must survey 400 science students, 300
commerce students, and 300 arts students. If our sample is 100, it means
40 sciences, 30 arts and 30 commerce students will be at there.

41. Quota sampling
• Step: 3.
Continue to invite cases until the quota for each stratum is met
• Once we have selected the number of cases that we need in each
stratum, we simply need to keep inviting participants to take part in
our research until each of these quotas are filled.

42. Quota sampling: Uses
Quota sampling is useful when time is limited, a sampling frame is not
available, the research budget is very tight or when detailed accuracy is
not important. Subsets are chosen and then either convenience or
judgment sampling is used to choose people from each subset. The
researcher decides how many of each category is selected

43. Quota sampling: Connection to stratified sampling
Quota sampling is the non-probability version of stratified sampling. In
stratified sampling, subsets of the population are created so that each
subset has a common characteristic, such as gender. Random sampling
chooses a number of subjects from each subset with, unlike a quota
sample, each potential subject having a known probability of being
selected.

44. Judgmental or Purposive Sampling
• Judgmental sampling, also called purposive sampling or authoritative sampling, is
a non-probability sampling technique in which the sample members are chosen
only on the basis of the researcher’s knowledge and judgment. As the
researcher’s knowledge is instrumental in creating a sample in this sampling
technique, there are chances that the results obtained will be highly accurate
with a minimum margin of error.

45. What is snowball sampling?
Snowball sampling (or chain sampling, chain-referral sampling, referral
sampling) is a non-probability sampling a technique where existing study
subjects recruit future subjects from among their acquaintances. Thus the
sample group is said to grow like a rolling snowball. As the sample builds up,
enough data are gathered to be useful for research. This sampling technique is
often used in hidden populations which are difficult for researchers to access;
example populations would be drug users or sex workers.

46. To create a snowball sample, there are two steps: Step: 1
Try to identify one or more units in the desired population:
• Imagine that the populations we are interested in are students that
download pirate music over the Internet or that take drugs. Let's go with
the latter: students that take drugs. Each student is referred to as a unit.
Collectively, all student drug users make up our population. However, we
are only interested in examining a sample of these student drug users.

47. Step : 1
 First, we need to try and find one or more units from the population
we are studying (i.e., student that take drugs). Finding just a small
number of individuals willing to identify themselves and take part in
the research may be quite difficult, so the aim is to start with just one
or two students (i.e., one or two units)

48. Step: 2
Use these units to find further units and so on until the sample size is met:
• Due to the sensitivity of the study, the researcher should ask the initial
students who agreed to take part in the research to help identify other students
that may be willing to take part. For ethical reasons, these new research
participants should come forward themselves rather than being identified by the
initial students. In this respect, the initial students help to identify additional
units that will make up our sample. The process continues until sufficient units
have been identified to meet the desired sample size.

49. Advantages of Snowball Sampling
• It allows for studies to take place where otherwise it might be
impossible to conduct because of a lack of participants.
• Snowball sampling may help you discover characteristics about a
population that you weren’t aware existed. For example, the casual
illegal downloader vs. the for-profit downloader.

50. Disadvantages of Snowball Sampling
It is usually impossible to determine the sampling error or make
inferences about populations based on the obtained sample.

51. Type of
Sampling
When to use it Advantages Disadvantages
Simple
Random
Sampling
When the population
members are similar to
one another on important
variables
Ensures a high degree of
representativeness
Time consuming and
tedious
Systematic
Sampling
When the population
members are similar to
one another on important
variables
Ensures a high degree of
representativeness, and
no need to use a table of
random numbers
Less random than
simple random
sampling
Stratified
Random
Sampling
When the population is
heterogeneous and
contains several different
groups, some of which are
related to the topic of the
study
Ensures a high degree of
representativeness of all
the strata or layers in the
population
Time consuming and
tedious

52. Type of
Sampling
When to use it Advantages Disadvantages
Cluster
Sampling
When the population
consists of units rather
than individuals
Easy and
convenient
Possibly, members of units
are different from one
another, decreasing the
techniques effectiveness

53. 1) Reducing sampling error is the major goal of any selection technique.
2) A sample should be big enough to answer the research question, but not
so big that the process of sampling becomes uneconomical.
3) Estimating sample size in general, you need a larger sample to accurately
represent the population when:
• The amount of variability within groups is greater, and
• The difference between the two groups gets smaller.

54. 4) In general, the larger the sample, the smaller the sampling error and the better job you
can do.
5) If you are going to use several subgroups in your work (such as males and females who
are both 10 years of age, and healthy and unhealthy urban residents), be sure your initial
selection of subjects is large enough to account for the eventual breaking down of subject
groups.
6)If you are mailing out surveys or questionnaire, count on increasing your sample size by
40% to 50% to account for lost mail and uncooperative subjects.
7) Remember that big is good, but appropriate is better. Do not waste your hard-earned
money or valuable time generating samples that are larger than you need law of
diminishing returns will set in!

55. • In general, the larger the sample, the smaller the sampling error and the
better job you can do.
• If you are going to use several subgroups in your work (such as males and
females who are both 10 years of age, and healthy and unhealthy urban
residents), be sure your initial selection of subjects is large enough to
account for the eventual breaking down of subject groups.
• If you are mailing out surveys or questionnaire, count on increasing your
sample size by 40% to 50% to account for lost mail and uncooperative
subjects.
• Remember that big is good, but appropriate is better. Do not waste your
hard-earned money or valuable time generating samples that are larger
than you need law of diminishing returns will set in!