Process of Research Methodology

1. RESEARCH PROCESS
MODULE - 2

2. SYLLABUS
• Introduction to Qualitative and
Quantitative Research,
• Sampling Design – Census and
Sample survey, Characteristics of
good sample design,
• Sampling Methods – Random
sampling methods - Simple
Random Sampling,
• Stratified Random Sampling,
• Systematic Sampling, Cluster
Sampling
• Non-random Sampling methods -
Convenience, Judgmental,
• Quota Sampling, Snowball,
• Sampling and non-sampling Errors
• Sampling distribution, Central limit
theorem, (additional topic)
• Sample Size Determination
(additional topic)

3. Quantitative Research
• Quantitative research deals with numbers and statistics.
• Qualitative research deals with words and meanings.
• Both are important for gaining different kinds of knowledge.
• Quantitative research is expressed in numbers and graphs. It is used
to test or confirm theories and assumptions.
• This type of research can be used to establish generalizable facts
about a topic.
• Common quantitative methods include experiments, observations
recorded as numbers, and surveys with closed-ended questions.
• Quantitative research is at risk for research biases including
information bias, omitted variable bias, sampling bias, or selection
bias.

4. Qualitative Research
• Qualitative research is expressed in words. It is used to understand concepts,
thoughts or experiences.
• This type of research enables a researcher to gather in-depth insights on topics
that are not well understood.
• Common qualitative methods include interviews with open-ended questions,
focus group discussions, ethnography (Participating in a community or
organisation for an extended period of time to closely observe culture and
behaviour), observations described in words, and literature reviews that explore
concepts and theories.
• Qualitative research is also at risk for certain research biases including hawthorne
(It refers to people’s tendency to behave differently when they become
aware that they are being observed), observer bias, recall bias, and social
desirability bias.

5. Difference between Qualitative and Quantitative Research
• Quantitative and qualitative research use different research methods
to collect and analyse data, and they allow you to answer different
kinds of research questions.
• https://www.scribbr.com/methodology/qualitative-quantitative-
research/

7. When to use which type of research?
• Use quantitative research if you want to confirm or test
something (a theory or hypothesis)
• Use qualitative research if you want to understand
something (concepts, thoughts, experiences)
• For most research topics you can choose a qualitative,
quantitative or mixed methods approach.
• It is all depends on research approach one adopts, research
question(s), doing experimental, correlational, or
descriptive research, and practical considerations such as
time, money, availability of data, and access to
respondents.

8. Topic of research: How satisfied are students with their studies?
• Quantitative research approach
• Survey 300 students at your university and ask them questions such as:
“on a scale from 1-5, how satisfied are you with your professors?”
• Qualitative research approach
• You conduct in-depth interviews with 15 students and ask them open-
ended questions such as: “How satisfied are you with your studies?”,
“What is the most positive aspect of your study program?” and “What
can be done to improve the study program?”
• Mixed methods approach
• You conduct interviews to find out how satisfied students are with their
studies. Through open-ended questions you learn things you never
thought about before and gain new insights. Later, you use a survey to
test these insights on a larger scale.

9. Population
• Population is a set of all elements of interest in a study.
• For example,
(1) Set of all BBA students studying in MU.
(2) Set of all patients having blood cancer in a hospital.
(3) Set of all elephants in a particular forest.
(4) Set of all transactions occurring at a bank.
(5) Set of all telephone call arriving at customer care center.
(6) Items that are producing on a production line.
Population may be finite or infinite.
Find which one of the above is finite and infinite?

10. Census:
• The study of whole population is known as a Census.
Sample:
• A sample is a subset of the population.
• A researcher generally takes a small portion of the population for study,
which is referred to as sample.
Note: No. of units in a finite population (population size) is denoted by 𝑵.
No. of units in a sample (sample size) is denoted by 𝒏.
Sampling:
• Sampling is the process of selecting a sample from a population.
• The purpose of sampling is to select a good representative sample from
the population then draw conclusions about the population from
sample.

11. Why we go for Sampling?
• Saves time.
• Saves money.
• Reduces effort.
• In most cases complete census is not possible and, hence,
sampling is the only option left. (??)
• Sampling broadens the scope of the study in light of the
scarcity of resources.
• It has been noticed that sampling provides more accurate
results, as compared to census because in sampling, non-
sampling errors can be controlled more easily.

12. Sampling frame:
Sampling frame is a list of units of the finite population
from which a sample is to be taken. (In reality, the frame
and the target population are often different)
Examples?

13. SAMPLE DESIGN
• A sample design is a definite plan for obtaining a sample from a
given population.
It refers to the procedure the researcher would adopt in selecting
items for the sample.
Procedure to tell the number of items to be included in the sample i.e.,
the size of the sample,
Choose type of sample technique,
To choose that samples that are reliable and appropriate.

14. • Steps in Sample Design
Stage 1: Clearly Define Target Population
Stage 2: Take decision on sampling unit:
Stage 3: Select sampling frame: representative
Stage 4: Choose Sampling Technique:
Stage 5: Determine sample size: The size should not be too large or too small rather it
should be optimum. An optimum sample is one which fulfils the requirements of
efficiency, representativeness, reliability and flexibility. The parameters of interest in a
research study must be kept in view, while deciding the size of the sample. Cost factor i.e.,
budgetary conditions should also be taken into consideration.
Stage 6: Collect data:
Stage 7: Assess response rate:

15. Characteristics of a Good Sample Design
1. Sample design must result in a truly representative sample,
2. Sample design must be such which results in a small sampling error,
3. Sampling design must be viable in the context of funds available for
the research study,
4. Sample design must be such that systematic bias can be controlled in
a better way, and
5. Sample should be such that the results of the sample study can be
applied, in general, for the universe with a reasonable level of
confidence.

16. Random sampling vs Non-random sampling
• In a random sampling, each unit of the population has the
same probability (chance) of being selected as a part of
the sample.
• There may be some cases where random sampling is not
feasible. In these cases, non-random sampling method is
the only alternative.
• In non-random sampling, every unit of the population
does not have the same probability (chance) of being
selected in the sample. Some other factors like familiarity,
convenience, availability, preference, etc. are the basis of
selection.

17. Random sampling vs Non-random sampling
Basis For Comparison Random Probability Sampling Non-Random Probability Sampling
Definition Probability sampling seems to
be one sampling approach in
which all members of the group
have an equal chance of being
chosen as a valid sample
Non-Random probability sampling
describes a sampling approach in
which this is unknown which
person from the group will be
chosen as the sample
Selection criteria Randomly Arbitrarily
Possibilities of choosing fixed and well-known Unknown and unspecified
Research Conclusive Exploratory
Result Impartial biased
Procedure Objective Subjective

18. Sampling Techniques
Probability sampling techniques

19. Simple Random Sampling
• This is the simplest methods among all the random sampling
techniques.
• In this method an equal probability of selection is assigned.
• If the number of units in the population is N, the probability of
selecting any unit of the first draw is 1/N and the probability of
selecting any unit from among the available (N–1) units at the
second draw is 1/(N–1) and so on.
• Simple random sampling may also be defined as a method of
selecting n units out of N units in the population such that each
possible sample among the total possible 𝑵
𝑪𝒏 samples has an
equal chance of its being selected.

20. • The successive draws may be made with or without replacing the
units selected in the preceding draw.
• In case of simple random sampling with replacement the
probability of a specified unit of the population being selected at
any given draw is equal.
• The basic assumption for simple random sampling is that the
population can be subdivided into a finite number of distinct and
identifiable units and sampling frame is available.

21. When should we use simple random sampling?
• If the population size is small or the size of the individual samples
and their number are relatively small, random sampling provides the
best results since all candidates have an equal chance of being
chosen.
Simple random sampling can be conducted by using:
• The lottery method. This method involves assigning a number to each
member of the dataset then choosing a prescribed set of numbers
from those members at random.1
• Technology. Using software programs like Excel makes it easier to
conduct random sampling.
• Using Random tables

22. Advantages of SRS
• Lack of bias
• Simplicity
• Less knowledge is required
Disadvantages of SRS
• Difficulty in accessing Lists of the
Full Population
• Time consuming
• Costs
• Sample selection bias
• Data Quality Is Reliant on
Researcher Qualify

23. (2) Stratified Random Sampling
• In a stratified sample, researchers divide a population
into homogeneous subpopulations called strata (the
plural of stratum) based on specific characteristics (e.g.,
race, gender identity, location, etc.). Every member of
the population studied should be in exactly one stratum.
• Each stratum is then sampled using another probability
sampling method, such as cluster sampling or simple
random sampling, allowing researchers to estimate
statistical measures for each sub-population.

24. Stratified Random Sampling ….
• Researchers rely on stratified sampling when a population’s
characteristics are diverse and they want to ensure that every
characteristic is properly represented in the sample.

25. When to use Stratified Random Sampling Technique:
• When you want to divide your population into
mutually exclusive and exhaustive subgroups.
• When subgroups will have different mean values

26. Advantages of using Stratified Random Sampling:
• Ensuring diversity of sample
• Ensuring similar variance
• Lowering the overall variance in the population
• Allowing a variety of data collection methods
For example, in order to lower the cost and difficulty of your study, you
may want to sample urban subjects by going door-to-door, but rural
subjects using mail.
Limitations:
• When classification is not possible
• When overlapping of subjects existing

27. (3) Cluster Sampling:
• In cluster sampling, researchers divide a population into smaller groups
known as clusters. Then randomly select among these clusters to form a
sample.
• Cluster sampling is a method of probability sampling that is often used to
study large populations, particularly those that are widely geographically
dispersed.

28. Cluster Sampling …
• Cluster sampling works best when each cluster provides a small-scale
representation of the whole population.
• The most common cluster sampling used in research is a geographical
cluster.
• For example, a researcher wants to survey academic performance of
University students in India. He can divide the entire population
(population of India) into different clusters (states). Then the researcher
selects a number of clusters (states) depending on his research through
simple random sampling and includes all the universities of selected state
in his sample.

29. Advantages of Cluster sampling
• Cluster sampling is time- and cost-efficient, especially for
samples that are widely geographically spread and would be
difficult to properly sample otherwise.
• Because cluster sampling uses randomization, if the
population is clustered properly, your study will have high
external validity because your sample will reflect the
characteristics of the larger population.

30. Disadvantages of Cluster sampling
• Internal validity is less strong than with simple random
sampling, particularly as you use more stages of clustering.
• If your clusters are not a good mini-representation of the
population as a whole, then it is more difficult to rely upon
your sample to provide valid results, and is very likely to be
biased.
• Cluster sampling is much more complex to plan than other
forms of sampling.

31. (4) Systematic Sampling
• Systematic sampling is a probability sampling method in which
researchers select members of the population at a regular interval (or
k) determined in advance.
• If the population order is random or random-like, then this method
will give you a representative sample that can be used to draw
conclusions about your population of interest.
When to use systematic sampling
• This method imitates many of the randomization benefits of simple
random sampling, and is slightly easier to conduct.
• This method is used when you’re unable to access a list of your
population in advance.

32. Systematic Sampling Procedure
• In this method sample elements are selected from the population at
uniform intervals in terms of time, order, or space.
• For obtaining samples in systematic sampling, first of all, a sampling
fraction is calculated.
For example, If the population contains 𝑁=1100 elements and we want to
select a sample of size 𝑛=100 elements. Calculate the sampling fraction 𝑁/𝑛.
𝑁/𝑛=1100/100=11 (Interval)
• Select any one element from the first 11 population elements
randomly using simple random sampling.
• Then go on adding an interval of 11 to that number till you get all the
100 elements
• Suppose 7th population element is selected from the first 11
population elements then the other sample elements will be 18th,
29th , 41th and so on up to 1096th (total = 100 numbers).

33. (5) Multi Stage Sampling
• In multistage sampling, (or multistage cluster sampling), you draw a
sample from a population using smaller and smaller groups (units) at
each stage. It’s often used to collect data from a large, geographically
spread group of people in national surveys.

34. • Multi-stage sampling involves the selection of units in more than one
stage.
• First, a sample is taken from the primary stage units and then a sample
is taken from the secondary stage units.
• For example, a researcher wants to select 200 villages from the entire
India and wants to use multi-stage sampling for this.
• In first stage, 28 states (of India) can be considered as primary stage
sampling units. From these 28 states 10 states may be selected
randomly.
• In second stage, districts within each selected state can be considered
as secondary stage sampling units. From each state he may select 5
districts randomly. So, at the end of second stage he will be having 50
districts.
• Finally in third stage, he can select 4 villages from each district.

35. Stage Sampling Unit Sample size
Units at the end of
stage
Stage-1 State (Total 28 states) 10 states 10 states
Stage-2 District
5 districts from
each state
10×5 = 50 districts
Stage-3 Village
4 villages from
each district
50×4 = 200 villages

36. What Is Non-Probability Sampling?
• Non-probability sampling is a sampling method that uses
non-random criteria like the availability, geographical
proximity, or expert knowledge of the individuals you want
to research in order to answer a research question.
• Non-probability sampling is used when the population
parameters are either unknown or not possible to
individually identify.
• Note that this type of sampling is at higher risk for research
biases than probability sampling, particularly sampling bias.

37. Types of non-probability sampling
• Convenience sampling
• Quota sampling (Proportional and Non-proportional)
• Self-selection (volunteer) sampling
• Snowball sampling
• Judgement (purposive) sampling

38. Non-Random Sampling Methods
(1) Convenience Sampling
• As the name indicates, in convenience sampling, sample elements are selected based on
the convenience of a researcher.
• The researcher typically chooses elements that are readily available, nearby, or willing to
participate.
• For example, a researcher wants to study about lifestyle of people shopping in malls. He will
select a mall near to his home or office and interview those people who visit the mall and
looking friendly.
• For door-to-door interviews researcher might include houses where people are at home,
houses with no dogs, houses near the street, first-floor apartments, and houses where
people are friendly and responsive.
• Convenience samples have the advantage of relatively easy sample selection and data
collection but may or may not be representative of the population.

39. (2) Quota Sampling
• In this sampling, you select a predetermined number or proportion of
units, called a quota. Your quota should comprise subgroups with specific
characteristics (e.g., individuals, cases, or organizations) and should be
selected in a non-random manner.
• There are two types of quota sampling: Proportional and Non-
proportional
• Proportional quota sampling is used when the size of the population is
known.
Example: Let’s say that in a certain company there are 1,000 employees.
They are split into 2 groups: 600 people who drive to work, and 400 who
take the train. You decide to draw a sample of 100 employees. You would
need to survey 60 drivers and 40 train-riders for your sample to reflect
the proportion seen in the company.

40. Quota Sampling …
• Non-proportional quota sampling is used when the size of the population
is unknown.
Example: Let’s say you are seeking opinions about the design choices on a website,
but do not know how many people use it. You may decide to draw a sample of 100
people, including a quota of 50 people under 40 and a quota of 50 people over 40.
This way, you get the perspective of both age groups.

41. (3) Self-selection (volunteer) Sampling
• Self-selection sampling relies on participants who voluntarily agree to be part of your
research. This is common for samples that need people who meet specific criteria, as is
often the case for medical or psychological research.
• Suppose that you want to set up an experiment to see if mindfulness exercises can
increase the performance of long-distance runners. First, you need to recruit your
participants. You can do so by placing posters near locations where people go running,
such as parks or stadiums.
• Your ad should follow ethical guidelines, making it clear what the study involves. It
should also include more practical information, such as the types of participants
required. In this case, you decide to focus on runners who can run at least 5 km and
have no prior training or experience in mindfulness.
• Keep in mind that not all people who apply will be eligible for your research. There is a
high chance that many applicants will not fully read or understand what your study is
about, or may possess disqualifying factors. It’s important to double-check eligibility
carefully before inviting any volunteers to form part of your sample.

42. (4) Snowball Sampling
• Snowball sampling is used when the population you want to research is
hard to reach, or there is no existing database or other sampling frame to
help you find them. Research about socially marginalized groups such as
drug addicts, homeless people, or sex workers often uses snowball
sampling.
• To conduct a snowball sample, you start by finding one person who is
willing to participate in your research. You then ask them to introduce you
to others.
• Alternatively, your research may involve finding people who use a certain
product or have experience in the area you are interested in. In these
cases, you can also use networks of people to gain access to your
population of interest.

43. Snowball Sampling …
• Example: You are studying homeless people living in your city. You start by
attending a housing advocacy meeting, striking up a conversation with a
homeless woman. You explain the purpose of your research and she agrees to
participate. She invites you to a parking lot serving as temporary housing and
offers to introduce you around.
• In this way, the process of snowball sampling begins. You started by attending
the meeting, where you met someone who could then put you in touch with
others in the group.
• Example: a researcher wishes to interview families of Kashmiri Pundits,
migrated from Kashmir. In this case he initially identifies one or two families
for interview then asks them for the other families and locations.
• This method is called snowball sampling because once you have the ball
rolling, it picks up more “snow” along the way and becomes larger and larger.
• When studying vulnerable populations, be sure to follow ethical
considerations and guidelines.

44. (5) Judgement (purposive) Sampling
• Purposive sampling is a blanket term for several sampling techniques that choose
participants deliberately due to qualities they possess. It is also called judgmental sampling,
because it relies on the judgment of the researcher to select the units (e.g., people, cases,
or organizations studied).
• Purposive sampling is common in qualitative and mixed methods research designs,
especially when considering specific issues with unique cases.
• For example, a news reporter wants a quick sample of opinions from each political party
about a political announcement. He will select one or two members from each political
party such that whose opinions reflect the general opinion of all members of that party.

45. Techniques of Purposive Sampling (additional topic)
1. Maximum variation (heterogeneous) sampling
2. Homogeneous sampling
3. Typical case sampling
4. Extreme (or deviant) case sampling
5. Critical case sampling
6. Expert sampling
Maximum variation sampling:
• You are researching what first-year students think of their study program. You are
more interested in nuance than generalizable findings, so you decide to pursue a
qualitative approach.
• You draw your sample using maximum variation sampling, including students who
performed poorly, students who excelled, and students in the middle. You recruit
and interview students until you have reached a saturation point.
Homogeneous sampling:
• You are researching the long-term side effects of working with asbestos. You
determine “long-term” to mean 20 years or longer. Using homogeneous sampling,
only people who worked with asbestos for 20 years or longer are included in your
sample.

46. Techniques of Purposive Sampling (additional topic)
Typical case sampling:
• Suppose you want to evaluate the level of care provided by physiotherapists to
clients at a certain clinic. To develop a typical case sample, you interact closely with
both therapists and clients in order to develop a set of criteria of what is “typical,” or
average.
• For physiotherapists, this could include years of professional experience, educational
background, etc. For patients, criteria can include their age, or how often they have
visited the clinic in the past year. By comparing the two typical case samples, you can
conclude whether the average physiotherapist has the expertise needed to meet the
average client’s needs.
Extreme case sampling:
• You are studying serial killers. You identify a few cases where the serial killer was
female. These cases are outliers, i.e., cases that stand out in your sample. In an effort
to develop a richer, more in-depth understanding of the phenomenon, you decide to
select these outliers and analyse them further.

47. Techniques of Purposive Sampling (additional topic)
Critical case sampling:
• Critical case sampling is used where a single case (or a small number of cases) can
be critical or decisive in explaining the phenomenon of interest. It is often used in
exploratory research, or in research with limited resources.
• There are a few cues that can help show you whether or not a case is critical, such
as:
• “If it happens here, it will happen anywhere”
• “If that group is having problems, then all groups are having problems”
Example:
• You want to know how well people understand a new tax law. If you ask tax
professionals and they do not understand it, then it’s likely laypeople won’t either.
Alternatively, if you ask people from other professional fields, irrelevant to taxes or
law, and they do understand it, then it’s safe to assume most people will.

48. Techniques of Purposive Sampling (additional topic)
Expert sampling:
• It involves selecting a sample based on demonstrable experience,
knowledge, or expertise of participants.
Example:
• You are interested in teaching methods for children with special needs in your
district, and you want to conduct some exploratory research. Using expert
sampling, you can contact special education teachers who work at schools in your
district, gathering your data via surveys or interviews.

49. ERRORS IN SAMPLING
• ERRORS: Statistical error is the difference between a value obtained from a
data collection process and the true value for the population.
• Data can be affected by two types of error:
1. Sampling Error
2. Non-Sampling Error
Sampling Errors:
• Is one which occurs due to UNREPRESENTATIVENESS OF THE
SAMPLE selected for research.
Non-Sampling Errors:
• Is an error arise from HUMAN ERROR such as errors in PROBLRM
IDENTIFICATION, METHODS, or PROCEDURES USED etc.
• Both the errors can be controlled but cannot be made
zero.

50. SAMPLING ERROR
• “Sampling error is the error that arises in a data collection
process as a result of taking a sample from a population
rather than using the whole population”.
• Even after taking care in selecting sample, there may be
chances that true value is not equal to the observed value
because estimation is based on the part of the population
not on the whole. Hence sampling give rise to certain errors
known as SAMPLING ERRORS.

51. Types of Sampling Errors
• Biased Sampling Error:
When the selection of sample is based on the personal prejudice or bias
of the investigator then the results are prone to bias errors. Such as, if the
investigator is required to collect the sample using the simple random
sampling and instead he used the non-random sampling, then personal
prejudice is introduced in the research process that will lead to the biased
errors.
• Unbiased Sampling Error:
The Unbiased Errors arise due to a chance, i.e. the investigator has not
intentionally tampered with the sample and that the difference between
the population and sample have occurred by chance. These errors arise
due to chance differences between the members of population included
in the sample and those not included.

52. Reasons for Sampling Errors
• Faulty selection of sampling method.
• Faulty demarcation of sampling units.
• Variability of the population which has different characteristic.
• Substituting one sample for other sample due to difficulties in collecting
the sample.
Steps for minimizing Sampling Error
• You can simply increase the sample size. A larger sample size generally
leads to a more precise result because the study gets closer to actual
population size and the results are obtained are more accurate.
• Dividing the population into groups.
• Random selection results in the elimination of bias.
• Performing an external record check.

53. NON-SAMPLING ERROR
• Non-Sampling Error is an error arise from human error, such as error
in problem identification, method or procedure used etc.
• “Non-Sampling Error is the error that arises in a data collection
process as a result of factors other than taking a sample”.
• Types:
• RESPONSE ERROR
• RESPONDENT ERROR
• INTERVIEWER ERROR

54. RESPONSE ERROR
• Response errors represent lack of accuracy in responses to
questions. They can be attributed to different factors,
including a questionnaire that requires improvement,
misinterpretation of questions by interviewers or
respondent, and errors in respondents statements.
• Response bias causes survey participants to answer
inaccurately, which skews data and makes meaningful
analysis difficult for your business.
• Factors in sampling method influence the data obtained.
• EXAMPLE: A respondent may answer questions in the way
she thinks the questioner wants her to answer or a man may
respond differently to a woman when asked questions about
domestic violence.

55. NON- SAMPLING ERRORS ARISES DUE TO
FOLLOWING REASONS
• Lack of trained and qualified investigators.
• Due to wrong answers to the questions.
• Due to incomplete coverage.
• Biasness of the investigators.
• Vague questionnaire.
• Faulty list of population.
• Wrong method of asking questions.
• Wrong calculations while processing the data.
• Failure of respondent memory to recall past events.

56. Steps for minimizing Non-Sampling Error
• Randomize selection to eliminate bias.
• Train your team
• Perform an external record check.

57. Questions for discussion
• Distinguish between qualitative and quantitative research
approaches?
• What is the difference between a sample and a census and why is
sampling is so important for a researcher?
• Explain the sampling design process.
• What are sampling and non-sampling errors and how can a
researcher control them?
• Explain the types of probability or random sampling techniques?
• Explain the types of non-probability or non-random sampling
techniques?
• Distinguish between probability and non-probability sampling
techniques.

58. Questions for discussion
• What is sampling? What are the characteristics of a good sample?
• Explain the following methods of sampling
• Simple random sampling
• Stratified random sampling
• Cluster sampling
• Systematic sampling
• Multi-stage sampling
• Quota sampling
• Convenience sampling
• Judgement sampling
• Snowball sampling