Chapter3-Methods_of_Research-Module.pdf

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MODULE IN METHODS OF RESEARCH
Objectives:
After completing this chapter, you will be able to:
• To discuss the available literature in the field of research
methodologies.
• To suggest directions for my research, and build a real
methodological approach for the studied problem.
• Describe the background of different perspectives and
define the research position.
• Discuss and represent the research problem into detailed
levels in order to formulate the research problem and
define the purpose and relevance of the developed model
• Finally, represent the managerial issues of the research,
mainly, the limitations and delimitations.
CHAPTER 3
PROBLEM IDENTIFICATION

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What are research methods?
What is the difference between Research Design and Reearch Method?
Research Design
Research methods are the strategies, processes or techniques utilized in the
collection of data or evidence for analysis in order to uncover new information or create
better understanding of a topic.
There are different types of research methods which use different tools for data
collection.
Research design is a plan to answer your research question. A research method
is a strategy used to implement that plan. Research design and methods are different
but closely related; because good research design ensures that the data you obtain will
help you answer your research question more effectively.
Refers to the overall strategy that you choose to integrate the different components
of the studyin a coherent and logical way, thereby, ensuring you will effectively address
the research problem and it constitutes the blueprint for the collection, measurement and
analysis of data.
How is the term “research design” to be used in this book? An analogy might help.
When constructing a building there is no point ordering materials or setting critical dates
for completion of project stages until we know what sort of building is being constructed.
The first decision is whether we need a high rise offce building, a factory for manufacturing
machinery, a school, a residential home or an apartment block. Until this is done we
cannot sketch a plan, obtain permits, work out a work schedule or order materials.

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Descriptive Research
Explanatory Research
Although some people dismiss descriptive research as “mere description”, good
description is fundamental to the research enterprise and it has added immeasurably to
our knowledge of the shape and nature of our society. Descriptive research encompasses
much government sponsored research including the population census, the collection of
a wide range of social indicators and economic information such as household
expenditure patterns, time use studies, employment and crime statistics and the like.
Descriptions can be concrete or abstract. A relatively concrete description might describe
the ethnic mix of a community, the changing age profile of a population or the gender mix
of a workplace.
Focuses on why questions. For example, it is one thing to describe the crime rate
in a country, to examine trends over time or to compare the rates in different countries. It
is quite a different thing to develop explanations about why the crime rate is as high as it
is, why some types of crime are increasing or why the rate is higher in some countries
than in others. The way in which researchers develop research designs is fundamentally

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affected by whether the research question is descriptive or explanatory. It affects what
information is collected. For example, if we want to explain why some people are more
likely to be apprehended and convicted of crimes we need to have hunches about why
this is so. We may have many possibly incompatible hunches and will need to collect
information that enables us to see which hunches work best empirically. Answering the
“why” questions involves developing causal explanations. Causal explanations argue that
phenomenon Y (e.g. income level) is affected by factor X (e.g. gender). Some causal
explanations will be simple while others will be more complex. For example, we might
argue that there is a direct effect of gender on income (i.e. simple gender discrimination)
(Figure 1.1a). We might argue for a causal chain, such as that gender affects choice of
training which in turn affects occupational options, which are linked to opportunities for
promotion, which in turn affect income level (Figure 1.1b). Or we could posit a more
complex model involving a number of interrelated causal chains (Figure 1.1c).
Figure 3.1 Three types of causal relationships

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Theory Testing and Theory Construction
Theory Building
Attempts to answer the “why” questions in social science are theories. These
theories vary in their complexity abstraction and scope. To understand the role of theory
in empirical research it is useful to distinguish between two different styles of research:
theory testing and theory building (Figure 1.2).
Theory building is a process in which research begins with observations and uses
inductive reasoning to derive a theory from these observations.
Figure 3.2 Theory building and theory testing approaches to research
These theories attempt to make sense of observations. Because the theory is
produced after observations are made it is often called post factum theory or ex post facto

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Theory Testing
theorizing. This form of theory building entails asking whether the observation is a
particular case of a more general factor, or how the observation into a pattern or a story.
In contrast, a theory testing approach begins with a theory and uses theory to guide
which observations to make it moves from the general to the particular. The observations
should provide a test of the worth of the theory. Using deductive reasoning to derive a set
of propositions from the theory does this. We need to develop these propositions so that
if the theory is true then certain things should follow in the real world. We then assess
whether these predictions are correct. If they are correct the theory is supported. If they
do not hold up then the theory needs to be either rejected or modified. For example, we
may wish to test the theory that it is not divorce itself that affects the wellbeing of children
but the level of conflict between parents. To test this idea we can make predictions about
the wellbeing of children under different family conditions. For the simple theory that it is
parental conflict rather than divorce that affects a child's wellbeing there are four basic
“conditions” (see Figure 1.3). For each “condition” the theory would make different
predictions about the level of children's wellbeing that we can examine.
Figure 3.3 the relationship between divorce and parental conflict
No single proposition would provide a compelling test of the original theory. Indeed,
taken on its own proposition 3, for example, would reveal nothing about the impact of
divorce. However, taken as a package, the set of propositions provides a stronger test of
the theory than any single proposition. Although theory tests and theory building are often

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Design versus Methods
presented as alternative modes of research they should be part of one ongoing process.
Typically, theory building will produce a plausible account or explanation of a set of
observations. However, such explanations are frequently just one of a number of possible
explanations that at the data. While plausible they are not necessarily compelling. They
require systematic testing where data are collected to specifically evaluate how well the
explanation holds when subjected to a range of crucial tests.
Figure 3.4 the logic of the research process
Research design is different from the method by which data are collected. Many
research methods texts confuse research designs with methods. It is not uncommon to
see research design treated as a mode of data collection rather than as a logical structure
of the inquiry. But there is nothing intrinsic about any research design that requires a
particular method of data collection. Although cross-sectional surveys are frequently
equated with questionnaires and case studies are often equated with participant
observation. How the data are collected is irrelevant to the logic of the design. Failing to
distinguish between design and method leads to poor evaluation of designs. Equating
cross-sectional designs with questionnaires or case studies with participant observation,

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means that the designs are often evaluated against the strengths and weaknesses of the
method rather than their ability to draw relatively unambiguous conclusions or to select
between rival plausible hypotheses.
Figure 3.5 Relationship between research design and particular data collection methods

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Research Methods
Qualitative vs. Quantitative
Qualitative Data
Are specific procedures for collecting and analyzing data? Developing your
research methods is an integral part of your research design. When planning your
methods, there are two key decisions you will make.
First, decide how you will collect data. Your methods depend on what type of data you
need to answer your research question:
Will your data take the form of words or numbers?
Primary vs. secondary:
• Will you collect original data yourself, or will you use data that has already been
collected by someone else?
Descriptive vs. experimental:
• Will you take measurements of something as it is, or will you perform an
experiment?
• Second, decide how you will analyze the data.
Quantitative Data
You can use statistical analysis methods to test relationships between variables.
You can use methods such as thematic analysis to interpret patterns and meanings
in the data.

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Methods for collecting Data
Qualitative
Quantitative
Qualitative Data vs. Quantitive Data
Data is the information that you collect for the purposes of answering your research
question. The data collection methods you use depend on the type of data you need.
Your choice of qualitative or quantitative data collection depends on the type of
knowledge you want to develop. For questions about ideas, experiences and meanings,
or to study something that can’t be described numerically, collect qualitative data.
If you want to develop a more mechanistic understanding of a topic, or your research
involves hypothesis testing, collect quantitative data.
✓ You can often adjust your methods as you go to develop new knowledge.
✓ Can be conducted with small samples. Can’t be analyzed statistically or
generalized to broader populations.
✓ Difficult to standardize research.
✓ Can be used to systematically describe large collections of things.
✓ Generates reproducible knowledge.
✓ Requires statistical training to analyze data.
✓ Requires larger samples.
You can also take a mixed methods approach, where you use both qualitative and
quantitative research methods.

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Primary vs. Secondary data
Descriptive vs. Experimental data
Descriptive
Experimental
Primary data is any original information that you collect for the purposes of
answering your research question (through surveys, observations and experiments).
Secondary data is information that has already been collected by other researchers
(in a government census or previous scientific studies).
In descriptive research, you collect data about your study subject without
intervening. The validity of your research will depend on your sampling method.
In experimental research, you systematically intervene in a process and measure
the outcome. The validity of your research will depend on your experimental design.
To conduct an experiment, you need to be able to vary your independent variable,
precisely measure your dependent variable, and control for confounding variables. If it’s
practically and ethically possible, this method is the best choice for answering questions
about cause and effect.
✓ Allows you to describe your research subject without influencing it.
✓ Accessible – you can gather more data on a larger scale.
✓ No control over confounding variables.
✓ Can’t establish cause and effect relationships.
✓ More control over confounding variables.
✓ Can establish cause and effect relationships.
✓ You might influence your research subject in unexpected ways.
✓ Usually requires more expertise and resources to collect data.

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Which research method should I choose?
Mixed Method (combination of some of the above)
What other factor should I consider when choosing one method over another?
It depends on your research goal. It depends on what subjects you want to study.
Let's say you are interested in studying what makes people happy, or why some students
are more conscious about recycling on campus. To answer these questions, you need
to make a decision about how to collect your data. Most frequently used methods include:
• Observation / Participant Observation
• Surveys
• Interviews
• Focus Groups
• Experiments
• Secondary Data Analysis / Archival Study
One particular method could be better suited to your research goal than others,
because the data you collect from different methods will be different in quality and quantity.
For instance, surveys are usually designed to produce relatively short answers, rather
than the extensive responses expected in qualitative interviews.
Time for data collection and analysis is something you want to consider. An
observation or interview method, so-called qualitative approach, helps you collect richer
information, but it takes time. Using a survey helps you collect more data quickly, yet it
may lack details. So, you will need to consider the time you have for research and the
balance between strengths and weaknesses associated with each method of qualitative
vs. quantitative.

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Problem Discussion
Presentation of the Problem
When we returned to the definitions of operations management, we will find that
the total operations function is made up of individual processes, in manufacturing systems
there are production process which concerns about resources of whole operations and
not only to insure that the operation are working also if its effective, efficient and
productive, maintenance process which tries to insure that plant, systems, machines and
equipment are working, human management process which deals with employment
aspects. All of those processes are working as a chain within the whole operations, we
can find the whole operation as a summation of individual processes and the relational
processes between those processes, this relational process called integration process.
Manufacturing systems is far more complex today than few years ago and we believe that
it will be even more complex in the future. Due to the technological changes and its
complexity, companies should have a clear and systematic approach to understand the
integration process between the internal and external processes in proper way. In order
to integer the individual processes inside the working area, the company needs a strong
investigation technique to identify the probable faults which could be appeared within the
integration process mainly, when the operational process will be running.
Integration of the processes within one chain it is a process of arrangement
(planning and scheduling) all the operational resources that transforms inputs to outputs
that satisfy customer needs. Most of the operational management researches till us the
there are many difficulties and problems when the industries try to run one process.
Therefore they have definitely more difficulties in integrate multi processes or other plant
activities in one efficient operation. The difficulties it takes different types according to the
affects factors;
✓ Volume of those processes, variety between the processes, variation in demand
and visibility of each process.
While the most practical problems which happen always it could be due to human failures,
or organizational failures when they designing procedures and running the processes,

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moreover, the technology failures in machines, equipment’s and IT systems. All of the
practical problems are the core of the research.
All of those difficulties, problems and failures are results of integration process
failures, but the hidden causes are due to some missing parts inside the integration
process or one process overlaps other processes, furthermore, the frictional problems
between the processes.
Practically, integrating the individual processes it is not easy in real situation, for
example, the Swedish survey work done by where he was showed that effects of fully
production integrated maintenance and the status of maintenance management in
Swedish manufacturing firm’s summaries as follows:
• Presents a model of five linked maintenance management components (strategy,
human aspects, support mechanisms, tools/techniques and organization).
• Analyses the present status of these components in Swedish manufacturing firms
through a survey of 284 respondents was showed that fewer than half have written
maintenance strategies or computerized maintenance information systems and
several give maintenance low status. Jonsson .P, (1997) as mentioned before,
the reasons behind these results in terms of process integration that the fully
production-integrated maintenance approach is not simple to apply. That means
there is need to user friendly model which identify and integrate the operations
within the working situation.

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Problem Formulation
Purpose
The problem will theoretically and empirically investigate the case study, using both
quantitative and qualitative research methodologies and using systems methodological
approach to describe the process integrity. And can be formulated as:
How to identify systematically the integration losses; missing, overlapping and frictions
within working area between sub-processes such as operation, personnel competence,
maintenance, and quality, and to eliminate those losses ?
The authors argue that using the most suitable integration practices which
eliminate or reduce the overlapping and frictions within specific process will result in
increasing the utilization of the production systems, due to integrity effects on the
elements of overall process effectiveness. The integration between the sub-systems on
the basis of: How it is established? How it works? Internal frictions and if there are
overlapping between sub-processes and its impact on the operations effectiveness.
The purpose of this research work is to develop a model that deals with the
integration losses of production process through two phases: identification and treatment.
The objective of identification part will be achieved through the activities and tasks
modeling within the process, then by investigating the causes that reduce the
performance of those processes or even though which produce stoppages in those
processes or delays within processing time. Consequently, the treatment part treats the
process situation by build cost-effective solution which will be generated from the
industrial or company needs and will translate these needs into requirements,
activities, and factors to initiate the company implementation ability in order to achieve
the industrial satisfaction.

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Relevance
The essential question here is “To what extent is this problem worth researching?”
The relevance of the research problem and problem formulation is to showing why it is
important to do research in this area.
According to a survey done by Jonsson P (1997) of 747 companies in Sweden
report firms own opinions whether they had an integrated IT system that included the
maintenance function. Less than 13% claimed any integration, and only 4% included
maintenance. As a result, authors shall describe the difference between existing system’s
capabilities with the lacks, overlaps and frictions and those of fully integrated system,
and give examples of how their proposal could enhance company performance. Also
Jonsson P (1997) had concluded that Production-integrated maintenance may affect the
competitive capabilities and long-term benefits of organizations. It is important as a value
adding activity in increasingly integrated business and manufacturing strategies. There is
a lack of empirical studies explaining of how should the maintenance integrating with
company systems and it not simple to apply discussed in detailed levels the difficulties of
maintenance integrated implementation process and he has founded and defined some
of the overlapping and frictions between company systems. The most interesting feature
of IP and Fung R (2000) paper the method of design the integrated system where they
had been used the systematic approach for the design. The authors try to ask two
questions; the first one is, if the company wants to build a maintenance integrated system
how it could be able to build it without overlapping and frictions, they means how to define
these losses in the design stage which is less costly than in operations – when the system
running- stage. The second question focus on the existing maintenance integrated
system, how companies will define the overlapping and frictions in their system, then how
they will generate, evaluate and select the most cost effective solution and how to design
this selected solution. Thus, the model should support the developing a capable structure
to solve the problem and feasible to apply, in summary, it characterize by the following
characteristics:

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Limitation and Delimitation
Theoretically crystallite, the model has developed as a scientific result of the
crystallization and structuring of theories in this area, its transformation into methods and
techniques, fitted and mediated for the user, and to implement, utilize and follow up in the
industrial enterprise.
• Working Ability for deep investigation, identify integration losses needs deep
investigation process, so it needs model which supporting the suitable methods
and techniques within the complicated conditions.
• Systematic, adequate and user-friendly model for identifying the integration losses
is one of the motivating reasons. Add to that the needs to investigate how the
industry treats the integration losses, and adaptive to IT systems.
• The feasibility to apply is one of powerful feature of the model to show that
increasing the recognition of integration role will keep and improve process
availability, performance, quality products, on-time deliveries, safety requirements
and overall plant productivity at high levels.
Every research model is a limited part of reality, real or imagined. Research
delimitation can be questioned. There are no absolute systems delimitations, only more
or less useful ones in relation to a certain research purpose. Nevertheless, no matter how
the research of this complexity is created, every research becomes relatively limited. The
authors was faced the two types of delimitations; external delimitations which delimit the
research object in relation to its environment, and internal delimitations which limit to how
much details can be considered in our research, based on that, they will always be
necessary to stop at same level of magnification.
Delimitations and limitations clarify the boundaries, exceptions, and reservations inherent
in every study. The two concepts are different in that:

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Where do delimitation and Limitation placed?
Examples of delimitation and Limitation
• Delimitations aim to narrow the scope of a study. For example, the scope may
focus on specific variables, specific participants, specific sites, or narrowed to one
type of research design.
• Limitations, however, aim to identify potential weaknesses of the study. For
example, all statistical procedures and research strategies, such as surveys or
grounded theory studies have limitations. In introductory discussions about these
strategies, authors typically mention both their strengths and their weaknesses.
In proposals, authors may include them in a separate section. They also may
separate them into two subsections, one on delimitations and the other on limitations.
Doctoral and master’s committees vary in the extent to which they require these sections
to be included in proposals.
However, in journal articles, researchers incorporate delimitations into the
methodology section, and they write limitations into the final section of their studies.
✓ Delimitation
Initially, this study will confine itself to interviewing and observing the psychiatric staff
nurse in a Midwest private psychiatric hospital.
✓ Limitation
The purposive sampling procedure decreases the generalizability of findings. This study
will not be generalizable to all areas of nursing. In this qualitative study, the findings could
be subject to other interpretations.

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Video links:
Identifying and Defining Problems
• https://m.youtube.com/watch?v=2m-xgti19-U
Identifying a Research Problem
• https://m.youtube.com/watch?v=nBtAoQrh1yE
References:
• Creswell, J.W. (2012). Educational Research:
Planning, Conducting, and Evaluating Quantitative
and Qualitative Research, 4th ed. Boston, MA:
Pearson Education.
• https://www.google.com/url?sa=t&source=web&rct=j
&url
• https://www.divaportal.org/smash/get/diva2:205386/F
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