Week 1
Key scienti c terminology (Article 1 A. Nikulina)
Construct: An abstract idea you want to study, like "trust" or "job satisfaction." These are usually
not directly measurable.
Variable: The measurable form of a construct. For example, measuring "job satisfaction" with a
score from 1 to 10.
Proposition: A general statement about how two or more constructs are related, for instance,
"More trust leads to better cooperation.”
Hypothesis: A speci c, testable prediction derived from a proposition. For example, "Employees
with a high trust score perform 15% better than others.”
Theory: A framework that connects constructs, variables, propositions, and hypotheses to
explain how, when, and why things work.
These elements form the foundation of scienti c research: you begin with abstract ideas
(constructs), make them measurable (variables), propose general relationships (propositions), and
test these relationships with concrete predictions (hypotheses) to build a theory.
Research Objective: Explains the main purpose of a study and its contribution. It tells the reader
what, why, and how the research will be done (often found in the introduction or abstract).
Research Question: The central question the study aims to answer, usually highlighted in the
introduction.
Research Strategy (Method): The plan of action for achieving the research objective. This
describes the method used (such as surveys or case studies) and is typically found in the method
section. Some studies may only include a research objective, a question, or both, depending on
the eld or journal preference.
Scienti c reasoning: Deduction, induction and abduction (Article 2
Saunders et al. )
fi fi fi fi fi
, Deduction
The six sequential steps are:
1. Put forward a tentative idea, a premise, a hypothesis or set of hypotheses to form a theory.
2. By using existing literature, or by specifying the conditions under which the theory is expected
to hold, deduce a testable proposition or number of propositions.
3. Examine the premises and the logic of the argument that produced them, comparing this
argument with existing theories to see if it o ers an advance in understanding. If it does, then
continue.
4. Test the premises by collecting appropriate data to measure the concepts or variables and
analysing them.
5. If the results of the analysis are not consistent with the premises (i.e. the test fail), the theory
is false and mus either be rejected or modi ed and the process restarted.
6. If the results of the analysis are consistent with the premises, then the theory is corroborated.
It thus has the following important characteristics:
1. There is the search to explain causal relationships between concepts and variables.
2. The concepts need to be operationalised in a way that enables facts to be measures, often
quantitatively. Reductionism implies that problems as a whole are better understood if they are
reduced to the simplest possible elements.
3. The nal characteristic of deduction is generalisation. In order to be able to generalise, it is
necessary to select our sample carefully and for it to be of su cient size.
START WITH THEORY/HYPOTHESIS AND TEST THIS SYSTEMATIC WITH DATA. FROM THEORY
TO OBSERVATION
—> APPROVE OR REJECT
Induction
Based on observations, you may end up with the same theory as originally hypothesised, but
your reasoning to product that theory is using an inductive approach: theory follows data rather
than vice versa, as with deduction. It is thus much more suitable for social studies as human
behaviour is very complex.
Research using an inductive approach to reasoning is likely to be particularly concerned with
the context in which such events take place. Therefore, the study of a small sample of subject
might be more appropriate than a large number as with deductive approach.
STARTS FROM DATA AND DEVELOPS A THEORY
—> PATTERNS ARE FOUND (BEST FOR COMPLEX AND SOCIAL SITUATIONS)
Abduction
Instead of moving from theory to data or data to theory, an abductive approach moves back and
forth, in e ect combining deduction and induction. Abduction begins with an observation of a
surprising fact; it then works out a plausible theory of how it could have occurred. This can
uncover more surprising fact at any stage in the research process, including when writing your
project report.
MOVES BETWEEN THEORY AND DATA. STARTED WITH A SURPRISING OBSERVATION
—> AN EARLY THEORY AS THE BEST EXPLANATION
fi ff fiff ffi
, Research cycle ( rst two)
1. Managerial problem
A managerial problem usually begins as a
performance issue—identifying which areas aren’t
meeting expectations. It often involves untangling a
mix of interrelated issues, each seen di erently by
various stakeholders. The key is to focus on one
important problem, ensure there’s support from
relevant parties, and then pinpoint the essential
questions behind it. By reviewing prior research and
conducting some exploratory studies (abductive
reasoning), you can combine these insights into a
clear research objective that guides your project.
2. Knowledge question
Begin by gathering literature related to the concepts in your research objective and initial
questions. Re ne these questions until you have one primary research question supported by a
few sub-questions, usually three to ve. If your objective involves testing a theory, you'll also
develop hypotheses.
Research questions can be categorised by the type of knowledge they generate:
• Descriptive: How things are.
• Explanatory: Why things are that way.
• Predictive: How things will be.
• Prescriptive: How things should be done.
In some elds, such as medicine, research questions may also generate evaluative knowledge—
assessing how successful something is.
fi fi fi fi ff
, Di erent classi cations of data
1. Data & Knowledge
• Data = Raw, unprocessed information.
• Knowledge = Processed data that is meaningful.
• Data Collection = Gathering info from di erent sources.
2. Primary vs. Secondary Data
• Primary Data ("New") = Collected rst-hand, speci c to research, high quality but time-
consuming.
• Secondary Data ("Old") = Pre-existing, easier to access, larger sets but may need
adjustments.
Key question: Did you develop the measurement approach?
3. Qualitative vs. Quantitative Data
• Qualitative = Words, pictures, subjective, answers What/How/Why?
• Quantitative = Numbers, measurable, objective, answers How many/how often?
• Qualitative analyzed by themes, Quantitative analyzed statistically
4. Simulated vs. Empirical Data
• Simulated = Modeled data (used for predictions, faster and cheaper)
• Empirical = Real-world data (historical or real-time, but can't predict the future)
• Simulated relies on real data characteristics but may have errors
ff
fi fi ff fi
Key scienti c terminology (Article 1 A. Nikulina)
Construct: An abstract idea you want to study, like "trust" or "job satisfaction." These are usually
not directly measurable.
Variable: The measurable form of a construct. For example, measuring "job satisfaction" with a
score from 1 to 10.
Proposition: A general statement about how two or more constructs are related, for instance,
"More trust leads to better cooperation.”
Hypothesis: A speci c, testable prediction derived from a proposition. For example, "Employees
with a high trust score perform 15% better than others.”
Theory: A framework that connects constructs, variables, propositions, and hypotheses to
explain how, when, and why things work.
These elements form the foundation of scienti c research: you begin with abstract ideas
(constructs), make them measurable (variables), propose general relationships (propositions), and
test these relationships with concrete predictions (hypotheses) to build a theory.
Research Objective: Explains the main purpose of a study and its contribution. It tells the reader
what, why, and how the research will be done (often found in the introduction or abstract).
Research Question: The central question the study aims to answer, usually highlighted in the
introduction.
Research Strategy (Method): The plan of action for achieving the research objective. This
describes the method used (such as surveys or case studies) and is typically found in the method
section. Some studies may only include a research objective, a question, or both, depending on
the eld or journal preference.
Scienti c reasoning: Deduction, induction and abduction (Article 2
Saunders et al. )
fi fi fi fi fi
, Deduction
The six sequential steps are:
1. Put forward a tentative idea, a premise, a hypothesis or set of hypotheses to form a theory.
2. By using existing literature, or by specifying the conditions under which the theory is expected
to hold, deduce a testable proposition or number of propositions.
3. Examine the premises and the logic of the argument that produced them, comparing this
argument with existing theories to see if it o ers an advance in understanding. If it does, then
continue.
4. Test the premises by collecting appropriate data to measure the concepts or variables and
analysing them.
5. If the results of the analysis are not consistent with the premises (i.e. the test fail), the theory
is false and mus either be rejected or modi ed and the process restarted.
6. If the results of the analysis are consistent with the premises, then the theory is corroborated.
It thus has the following important characteristics:
1. There is the search to explain causal relationships between concepts and variables.
2. The concepts need to be operationalised in a way that enables facts to be measures, often
quantitatively. Reductionism implies that problems as a whole are better understood if they are
reduced to the simplest possible elements.
3. The nal characteristic of deduction is generalisation. In order to be able to generalise, it is
necessary to select our sample carefully and for it to be of su cient size.
START WITH THEORY/HYPOTHESIS AND TEST THIS SYSTEMATIC WITH DATA. FROM THEORY
TO OBSERVATION
—> APPROVE OR REJECT
Induction
Based on observations, you may end up with the same theory as originally hypothesised, but
your reasoning to product that theory is using an inductive approach: theory follows data rather
than vice versa, as with deduction. It is thus much more suitable for social studies as human
behaviour is very complex.
Research using an inductive approach to reasoning is likely to be particularly concerned with
the context in which such events take place. Therefore, the study of a small sample of subject
might be more appropriate than a large number as with deductive approach.
STARTS FROM DATA AND DEVELOPS A THEORY
—> PATTERNS ARE FOUND (BEST FOR COMPLEX AND SOCIAL SITUATIONS)
Abduction
Instead of moving from theory to data or data to theory, an abductive approach moves back and
forth, in e ect combining deduction and induction. Abduction begins with an observation of a
surprising fact; it then works out a plausible theory of how it could have occurred. This can
uncover more surprising fact at any stage in the research process, including when writing your
project report.
MOVES BETWEEN THEORY AND DATA. STARTED WITH A SURPRISING OBSERVATION
—> AN EARLY THEORY AS THE BEST EXPLANATION
fi ff fiff ffi
, Research cycle ( rst two)
1. Managerial problem
A managerial problem usually begins as a
performance issue—identifying which areas aren’t
meeting expectations. It often involves untangling a
mix of interrelated issues, each seen di erently by
various stakeholders. The key is to focus on one
important problem, ensure there’s support from
relevant parties, and then pinpoint the essential
questions behind it. By reviewing prior research and
conducting some exploratory studies (abductive
reasoning), you can combine these insights into a
clear research objective that guides your project.
2. Knowledge question
Begin by gathering literature related to the concepts in your research objective and initial
questions. Re ne these questions until you have one primary research question supported by a
few sub-questions, usually three to ve. If your objective involves testing a theory, you'll also
develop hypotheses.
Research questions can be categorised by the type of knowledge they generate:
• Descriptive: How things are.
• Explanatory: Why things are that way.
• Predictive: How things will be.
• Prescriptive: How things should be done.
In some elds, such as medicine, research questions may also generate evaluative knowledge—
assessing how successful something is.
fi fi fi fi ff
, Di erent classi cations of data
1. Data & Knowledge
• Data = Raw, unprocessed information.
• Knowledge = Processed data that is meaningful.
• Data Collection = Gathering info from di erent sources.
2. Primary vs. Secondary Data
• Primary Data ("New") = Collected rst-hand, speci c to research, high quality but time-
consuming.
• Secondary Data ("Old") = Pre-existing, easier to access, larger sets but may need
adjustments.
Key question: Did you develop the measurement approach?
3. Qualitative vs. Quantitative Data
• Qualitative = Words, pictures, subjective, answers What/How/Why?
• Quantitative = Numbers, measurable, objective, answers How many/how often?
• Qualitative analyzed by themes, Quantitative analyzed statistically
4. Simulated vs. Empirical Data
• Simulated = Modeled data (used for predictions, faster and cheaper)
• Empirical = Real-world data (historical or real-time, but can't predict the future)
• Simulated relies on real data characteristics but may have errors
ff
fi fi ff fi
