RM UNIT 5.pptx

Dr. Pallawi Bulakh
M.Sc. M.Phil.Ph.D.NET+JRF

Contents
 5.1 Surveys
 5.2 Design and Creation
 5.3 Experiments
 5.4 Case Studies
 5.5 Action Research
 5.6 Ethnography
 5.7 Interviews
 5.8 Observations
 5.9 Questionnaires
 5.10 Documents

3
Introduction.
Survey (noun).
 STATISTICS analysis of poll sample: a statistical analysis of
answers to a poll of a sample of a population, for example, to
determine opinions, preferences, or knowledge. [1].
 A method of gathering information from a sample of individuals. [2]
 A powerful, scientific tool for gathering accurate and useful
information. [8]
 Gathering information, asking questions, and sample of population.

4
Definitions
Census.
 Any count: any systematic count or survey.
 Count of population: an official count of a population carried
out at set intervals.
Sample.
 STATISTICS group selected for testing: a representative
selection of a population that is examined to gain statistical
information about the whole.[1]
Random
 STATISTICS equally likely: relating or belonging to a set in
which all the members have the same probability of
occurrence.[1]

5
Surveys are related to:
 Sampling. (From where/whom are we getting
the information)
 Interviewing. (How are we getting the
information?)
 Threats of validity. (What affect the validity of
the results)
 Ethics. (Respect people’s opinion and
confidential results.)

6
Why Are Surveys Conducted?
Surveys provide an important source of basic
scientific knowledge.
Who may conducts a survey?
Economists, psychologists, health professionals, political
scientists, and others who need to get some information may
conduct surveys to study such matters as income and
expenditure patterns among households, the roots of ethnic or
racial prejudice, the implications of health problems on people's
lives, comparative voting behavior, the effects on family life of
women working outside the home, etc.. [2].

Planning and Designing Surveys
 Activities included:
 Data Requirements,
 Data Generation Method,
 Sampling Frame,
 Sampling Technique,
 Response Rate And Non-responses,
 Sample Size.

Data requirements
 You need to decide what data you wish to generate (see
the case study below).
Research
Topic
Directly
Related data
Indirectly
related data

Data generation method
 Survey strategy is generally associated with
Questionnaire
 But, Interviews / observations / documents may also
work as data generation method.

Sampling
 Sampling is the process of selecting a representative group
from the population under study.
 The target population is the total group of individuals from
which the sample might be drawn.
 A sample is a group of people who take part in the
investigation. The people who take part are referred to as
“participants.”
 Generalizability refers to the extent to which we can apply
our research findings to the target population we are
interested in.
 This can only occur if the sample of participants is
representative of the population

Population Vs Sample.
 The population is the entire group that you want to
draw conclusions about.
 The sample is the specific group of individuals that
you will collect data from.

Sampling frame
 A sampling frame is some kind of list or collection of the whole
population of people (or events or documents) that could be
included in your survey, from which you will choose a sample.
 Sampling is a technique of selecting individual members or a
subset of the population to make statistical inferences from
them and estimate the characteristics of the whole population.
 Sampling techniques can be used in research survey software for
optimum derivation.
 The sampling frame is the actual list of individuals that the
sample will be drawn from. Ideally, it should include the entire
target population (and nobody who is not part of that
population).

Sample size
 The number of individuals you should include in your
sample depends on various factors, including the size
and variability of the population and your research
design.
 There are different sample size calculators and
formulas depending on what you want to achieve
with statistical analysis.

Types of Probabilistic Sampling

Random Sampling
 people or events are selected literally ‘at random’.
 Random sampling, or probability sampling, is a
sampling method that allows for the randomization of
sample selection
 each sample has the same probability as other samples
to be selected to serve as a representation of an entire
population.
 Random sampling is considered one of the most
popular and simple data collection methods
 It allows for unbiased data collection, which lets
studies arrive at unbiased conclusions.

Types of Random Sampling
Methods
 There are four primary, random (probability) sampling
methods. These methods are:

Simple random sampling
 Simple random sampling is the randomized selection
of a small segment of individuals or members from a
whole population.
 It provides each individual or member of a population
with an equal and fair probability of being chosen.
 The simple random sampling method is one of the
most convenient and simple sample selection
techniques.

Systematic sampling
 Systematic sampling is the selection of specific
individuals or members from an entire population.
 The selection often follows a predetermined interval
(k).

Stratified sampling
 Stratified sampling, which includes the partitioning of
a population into subclasses with notable distinctions
and variances.
 The stratified sampling method is useful, as it allows
the researcher to make more reliable and informed
conclusions by confirming that each respective
subclass has been adequately represented in the
selected sample.

Cluster sampling
 Cluster sampling, which, similar to the stratified
sampling method, includes dividing a population into
subclasses.
 Each of the subclasses should portray comparable
characteristics to the entire selected sample.
 This method entails the random selection of a whole
subclass, as opposed to the sampling of members from
each subclass. This method is ideal for studies that
involve widely spread populations.

Types of Non-probability Sampling
Four main techniques used for a non-probability sample:
Convenience
Judgemental
Snowball
Quota

Convenience Sampling
 It is a non-probability sampling technique used to create
sample as per ease of access, readiness to be a part of the
sample, availability at a given time slot or any other practical
specifications of a particular element.
 Convenience sampling involves selecting haphazardly those
cases that are easiest to obtain for your sample, such as the
person interviewed at random in a shopping center for a
television program.

Judgmental Sampling
 In the judgmental sampling, also called purposive
sampling, the sample members are chosen only on the
basis of the researcher’s knowledge and judgment.
 It enables you to select cases that will best enable you to
answer your research question(s) and to meet your
objectives.

Snowball Sampling
 Snowball sampling method is purely based on referrals and
that is how a researcher is able to generate a sample.
Therefore this method is also called the chain-referral
sampling method.
 This sampling technique can go on and on, just like a
snowball increasing in size (in this case the sample size) till
the time a researcher has enough data to analyze, to draw
conclusive results that can help an organization make
informed decisions.

Quota Sampling
 Selection of members in this sampling technique happens
on basis of a pre-set standard. In this case, as a sample is
formed on basis of specific attributes, the created sample
will have the same attributes that are found in the total
population. It is an extremely quick method of collecting
samples.
 Quota sampling is therefore a type of stratified sample in
which selection of cases within strata is entirely non-
random.

Sample size
 Sample size is a research term used for defining the
number of individuals included in a research study to
represent a population.
 The sample size references the total number of
respondents included in a study, and the number is
often broken down into sub-groups by demographics
such as age, gender, and location so that the total
sample achieves represents the entire population.
 Determining the appropriate sample size is one of the
most important factors in statistical analysis.

Sample size
 If the sample size is too small, it will not yield valid
results or adequately represent the realities of the
population being studied.
 On the other hand, while larger sample sizes yield
smaller margins of error and are more representative, a
sample size that is too large may significantly increase
the cost and time taken to conduct the research.

Confidence Interval (Margin of
Error)
 Confidence intervals measure the degree of
uncertainty or certainty in a sampling method and
how much uncertainty there is with any particular
statistic.
 The confidence interval tells you how confident you
can be that the results from a study reflect what you
would expect to find if it were possible to survey the
entire population being studied

Confidence Level
 The confidence level refers to the percentage of
probability, or certainty that the confidence interval
would contain the true population parameter when
you draw a random sample many times.
 It is expressed as a percentage and represents how
often the percentage of the population who would pick
an answer lies within the confidence interval.

 Accuracy range and confidence level :
 The accuracy range (also called ‘margin of error’ and ‘confidence interval’) tells
us how close to the true population value we are. If it is reported that 70 per
cent of the population think the prime minister (or president) is doing a good
job, with an accuracy range of +/— 3 per cent, then the true value of people
who think that is somewhere in the range 67 per cent to 73 per cent of the
population. If you wanted perfect accuracy (that is, a range of +/-0 percentage
points), you would have to survey the whole population.
 A confidence level of 95 per cent means that we are 95 per cent sure that the
true population value falls within the range of values obtained from the
sample. Another way of expressing this is to say that if we took an infinite
number of samples from the target population, 95 per cent of the time the true
population value would fall within the range of values obtained from the

How to Calculate Sample Size
 Determine the population size (if known).
 Determine the confidence interval.
 Determine the confidence level.
 Determine the standard deviation (a standard
deviation of 0.5 is a safe choice where the figure is
unknown)
 Convert the confidence level into a Z-Score. This table
shows the z-scores for the most common confidence
levels:

Grounded Theory
 Grounded Theory is a qualitative research
methodology developed by sociologists Barney G.
Glaser and Anselm L. Strauss in the 1960s.
 It's used primarily in the social sciences to generate
theories based on empirical data.
 The central idea behind Grounded Theory is to
develop theories that emerge from the data itself
rather than being imposed on it beforehand.

Grounded theory
 The process of Grounded Theory involves systematic
data collection, coding, and analysis in order to
identify patterns, categories, and concepts.
 It's particularly well-suited for exploring complex
social phenomena and generating hypotheses or
theories to explain those phenomena.


 Data Collection: Researchers collect a wide range of data, often through
methods such as interviews, observations, surveys, or document analysis. This
data could be transcripts of interviews, field notes, or any other relevant
material.
 Open Coding: Researchers begin by "open coding," which involves breaking
down the data into smaller segments and labeling them with descriptive codes.
This step is about identifying key concepts and patterns in the data.
 Axial Coding: In this stage, researchers start to connect the codes together by
identifying relationships between them. This involves categorizing codes and
looking for links and relationships among different categories.
 Selective Coding: This step involves focusing on a central concept or category
that emerges from the data. Researchers try to develop a core category that
explains the main phenomenon being studied. This process may involve
refining and redefining codes and categories.
 Constant Comparison: Throughout the coding process, researchers
constantly compare new data with existing codes and categories to refine their
understanding and theories. This iterative process helps to ensure the accuracy
and reliability of the emerging theory.
 Theory Development: The final step involves synthesizing the codes,
categories, and relationships into a coherent theory that explains the
phenomenon under investigation. This theory is "grounded" in the data

Grounded Theory
 Grounded Theory is often associated with the concept of
"constant comparison," where researchers continually
compare new data with existing data and codes to refine
and expand their theories. This methodology is particularly
useful when researchers want to explore complex social
processes, behaviors, and interactions in a holistic and
comprehensive manner.
 Overall, Grounded Theory allows researchers to develop
new insights and theories that are firmly rooted in the data
they have collected, making it a valuable approach in
qualitative

Design and Creation
 The design and creation research strategy focuses on
developing new IT products, also called artifacts.

Artifacts:
 Constructs: the concepts or vocabulary used in a particular IT related domain. For
example, the notions of entities, objects or data flows.
 Models: combinations of constructs that represent a situation and are used to aid
problem understanding and solution development. For example, a data flow diagram, a
use case scenario or a storyboard.
 Methods (also called ‘methodologies’): guidance on the models to be produced and
process stages to be followed to solve problems using IT. They include formal.
mathematical algorithms, commercialized and published methodologies such as Soft
Systems Methodology (Checkland & Scholes, 1990) or Information Engineering (Martin,
1989), organizations’ in-house procedure manuals and informal descriptions of practice
derived from experience.
 Instantiations: a working system that demonstrates that constructs, models, methods,
ideas, genres or theories can be implemented in a computer-based system.

The design and creation process:
 Awareness
 Suggestion
 Development
 Evaluation
 Conclusion

Advantages of the design and
creation
 You have something tangible to show for your efforts
—- some kind of IT artifact — rather than just abstract
theories or other knowledge.
 It appeals to people who enjoy technical and creative
development work .
 It is the normally expected mode of research in some
computing areas such as computer science and
software engineering.
 Because the use of IT and computers is still relatively
new in many domains, and because the technology is
advancing rapidly, there is plenty of scope for
proposing and developing new IT artifacts and
therefore making a contribution to knowledge.

Disadvantages of the design and
creation :
 You may be challenged to justify why your work is not
just ‘normal’ design and creation.
 It is risky if you do not have the necessary technical or
artistic skills. Enthusiasm is no substitute.
 It can be difficult to generalize to different settings
from the use of an IT artifact in a single situation.
 The (apparent) success of an IT artifact may depend
on the researchers being present — once they’ve gone,
an IT method or system may not work so effectively.
 It may produce perishable research. Rapid advances in
technology can invalidate the research results before
they have been tried out in a real-life context or even
before they have been written up and published.

Experiment
 Experimental research is a scientific research method
that involves systematically manipulating one or more
independent variables to observe the effects on a
dependent variable, while controlling for potential
confounding factors.
 The goal of experimental research is to establish
cause-and-effect relationships between variables by
controlling the experimental conditions and observing
how changes in the independent variable(s) lead to
changes in the dependent variable.

characteristics of experimental
research
 Controlled Environment: Experimental research is
conducted in a controlled environment where
researchers can manipulate and control the conditions
under which the study takes place.
 This control allows for the isolation of the effects of
the independent variable(s) on the dependent
variable.

research
 Random Assignment: Participants in an
experimental study are typically randomly assigned to
different groups or conditions.
 This randomization helps minimize the impact of
pre-existing differences among participants, making
the groups more comparable.

 Experimental and Control Groups: In many
experimental designs, participants are divided into
experimental groups (exposed to the manipulated
independent variable) and control groups (not
exposed to the independent variable or exposed to a
different condition). The control group provides a
baseline against which the experimental group's
results are compared.
research

research
 Randomization and Counterbalancing:
Randomization ensures that participants are assigned
to different conditions without bias.
 Counterbalancing involves varying the order of
conditions across participants to control for sequence
effects.

research
 Independent and Dependent Variables: The
independent variable is the factor being manipulated
by the researcher.
 The dependent variable is the outcome or response
that is measured.
 The researcher is interested in whether changes in the
independent variable lead to changes in the dependent
variable.
 An experiment will be based on manipulation of the
independent variable to observe the changes in the
dependent variable.

 Manipulation: The researcher deliberately
manipulates the independent variable(s) to observe its
effect on the dependent variable. This manipulation
may involve changing the level, presence, or absence of
the variable.
research

 Replication: Replicating an experiment with different
samples or settings helps establish the generalizability
and reliability of the results.
research

 Causality: Experimental research aims to establish
causal relationships by controlling for confounding
variables and ensuring that changes in the dependent
variable can be attributed to changes in the
independent variable.
research

Experimental research
 Experimental research is commonly used in fields such
as psychology, medicine, biology, and social sciences
to test hypotheses and theories.
 However, it's important to note that while
experimental research is powerful in establishing
cause-and-effect relationships, it may not always be
feasible or ethical to manipulate certain variables in
real-world situations.
 In such cases, researchers might rely on other research
methods, such as observational studies or quasi-
experiments, to gather valuable insights.

Advantages of experiments as a
research strategy
 They are a well-established strategy, seen by many as the
most ‘scientific’ and therefore most acceptable approach.
Where people have not received any formal research
methods training, this is often the only research strategy
they know.
 They are the only research strategy that can prove causal
relationships.
 Laboratory experiments permit high levels of precision in
measuring outcomes and analyzing the data.
 Laboratory experiments allow researchers to remain at
their normal place of work, without the time and costs
incurred in visiting field sites.

Disadvantages of experiments
include:
 Laboratory experiments often create artificial situations,
which are not comparable with real-world situations.
 It is often difficult or impossible to control all the relevant
variables.
 It is often difficult to recruit a representative sample of
participants.
 It may be necessary to conceal from the participants the
purpose of the research, so that they do not skew the
results by, for example, performing in the way they think
you want them to. However, deception of participants is
normally viewed as unethical

Case studies
 Case study research is a qualitative research method
that involves in-depth exploration and analysis of a
specific instance, situation, or phenomenon within its
real-life context.
 It aims to provide a comprehensive and detailed
understanding of the complexities and dynamics of a
particular case.
 Case study research is particularly useful when the
researcher wants to examine a phenomenon in depth,
understand its nuances, and gain insights into
underlying processes.

characteristics of case study
research
 Focus on Context: Case studies are conducted within
a specific context, which can be an organization, a
community, an individual, an event, or any other
bounded system. The context is crucial for
understanding how the case functions and interacts
with its environment.

 Holistic Approach: Case studies aim to capture the
complexity of the case as a whole, rather than focusing
on isolated variables. This often involves considering
multiple factors and their interactions.

 In-Depth Data Collection: Researchers gather rich,
qualitative data through various methods such as
interviews, observations, documents, artifacts, and
archival records. This data collection aims to provide a
comprehensive view of the case under investigation.

 Exploratory Nature: Case study research is often
exploratory, allowing researchers to generate
hypotheses and theories based on their observations
and analyses. It's especially useful for generating new
insights in areas where little prior research exists.

 Multiple Data Sources: Researchers use multiple
sources of data to triangulate their findings and
enhance the validity of their interpretations.
Triangulation involves comparing data from different
sources or methods to ensure consistency.
 Validity and Reliability: Case study research places a
strong emphasis on ensuring validity and reliability by
using rigorous data collection methods, maintaining
consistency, and addressing potential biases.

 Longitudinal and Retrospective Studies: Case studies
can be conducted over time (longitudinal) or by examining
past events (retrospective) to understand the development
and evolution of the case.
 Generalization of Insights: While case study research
doesn't aim for statistical generalization (as in quantitative
research), it does seek to achieve theoretical generalization.
This means that the insights gained from the case study
can inform broader theoretical frameworks or contribute to
a deeper understanding of similar phenomena.

 Thick Descriptions: Case study reports typically
include detailed and contextualized descriptions of
the case, its participants, settings, and relevant events.
These "thick descriptions" help readers understand
the case's complexity.
 Inductive Analysis: Researchers often use inductive
analysis to identify patterns, themes, and relationships
within the collected data. This analysis can lead to the
development of theories grounded in the case's unique
characteristics.

 Case study research is commonly used in social
sciences, psychology, business, education, and other
fields where an in-depth exploration of specific cases
can provide valuable insights.
 It's important to note that case study research is not
intended to establish cause-and-effect relationships as
experimental research does; rather, it aims to provide a
detailed, context-rich understanding of the case at
hand.

Types of case studies
 An exploratory study is used to define the questions or
hypotheses to be used in a subsequent study. It is used
to help a researcher understand a research problem.
 It might be used, for example, where there is little in
the literature about a topic, so a real-life instance is
investigated, in order to identify the topics to be
covered in a subsequent research project. For example,
an exploratory case study might help you work out
what questions to pose in a questionnaire to be used in
a survey.

 descriptive study leads to a rich, detailed analysis of a
particular phenomenon
 and its context. The analysis tells a story, including
discussion of what occurred
 and how different people perceive what occurred.
Types of case studies

Action research
 Action research can be defined as “an approach in
which the action researcher and a client collaborate in
the diagnosis of the problem and in the development
of a solution based on the diagnosis”[1].
 In other words, one of the main characteristic traits of
action research relates to collaboration between
researcher and member of organization in order to
solve organizational problems.

Action Research
 Action study assumes social world to be constantly
changing, both, researcher and research being one
part of that change.
 Generally, action researches can be divided into three
categories: positivist, interpretive and critical.


Types of action research
 Positivist approach to action research, also known as
‘classical action research’ perceives research as a social
experiment. Accordingly, action research is accepted as a
method to test hypotheses in a real world environment.
 Interpretive action research, also known as
‘contemporary action research’ perceives business reality as
socially constructed and focuses on specifications of local
and organisational factors when conducting the action
research.
 Critical action research is a specific type of action
research that adopts critical approach towards business
processes and aims for improvement

Advantages of Action Research
 High level of practical relevance of the business
research;
 Can be used with quantitative, as well as, qualitative
data;
 Possibility to gain in-depth knowledge about the
problem.

Disadvantages of Action Research
 Difficulties in distinguishing between action and
research and ensure the application of both;
 Delays in completion of action research due to a wide
range of reasons are not rare occurrences
 Lack of repeatability and rigour

Ethnography
 Ethnographic research is a qualitative method
where researchers observe and/or interact with a
study’s participants in their real-life environment.
 Ethnography was popularised by anthropology, but is used
across a wide range of social sciences.
 Ethnography is a type of qualitative research that involves
immersing yourself in a particular community or
organization to observe their behavior and interactions up
close.
 The word “ethnography” also refers to the written report of
the research that the ethnographer produces afterwards.

Advantages of ethnographic
research
 Ethnographic methods allow the participant to contact
the research subjects directly, have personal
experience with them in their natural environment
and collect first-hand data.
 Ethnographic research provides detailed and authentic
information about the research subjects, including a
detailed account of their behavior and why they occur.
 Ethnographic research can uncover the qualities of a
group’s culture or experience in a way that other
qualitative research methods cannot.

Disadvantages
 First, ethnographic research is time-consuming and
requires some level of expertise.
 Conducting an ethnographic study is expensive as it
requires the researcher to travel to the participants'
natural environment and live with them for weeks or
months to learn about their ways.
 The researcher's presence may affect the participants'
behavior, thereby affecting the validity and
authenticity of the research results.
 The researcher’s bias may affect the design and
implementation of an ethnographic study.

References
 https://research-methodology.net/research-
methods/action-research/
 https://www.scribbr.com/methodology/ethnography/
 https://delvetool.com/blog/ethnography

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