Business Research 1 summary chapter 2, 6, 9, 11, 12, 13, and 14
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Cours
Business Research 1
Établissement
Hogeschool Van Amsterdam (HvA)
Book
Research Methods for Business
The Business Research 1 summary and contains chapter 2, 6, 9, 11, 12, 13, and 14. Used for the (60% counted) exam. This is also helpful for the (40% counted) Business Report you need to write individually.
Business Research Techniques for Pre-master_320087-B-6_2021
Business Research Techniques for Pre-master_320087-B-6
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Hogeschool van Amsterdam (HvA)
International Business
Business Research 1
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Chapter 2
Scientific research: focuses on solving problems and pursues a step-by-
step logical, organized, and rigorous method to identify the problems,
gather data, analyse them, and draw valid conclusions from them.
Purposiveness:
The research has to have an aim or purpose
Research costs time & money, it’s a waste of resources if there is no
purpose.
Rigor:
Rigor: Carefulness, scrupulousness, and the degree of exactitude in
research investigations. The study has to have a good theoretical base
and a carefully thought-out methodology.
Lacks rigor if:
- The conclusions are incorrectly drawn because they are based on the
responses of just a few employees whose opinions may not be
representative of the entire workforce.
- The manner of framing and addressing the questions could have
introduced bias or incorrectness in the responses.
Testability:
A scientific hypotheses must be testable. You can test whether it is real
or not by researching.
Not all hypotheses can be tested (god created the world)
Replicability:
Replication is made possible by a clear description of the design
details, such as sampling method and data collection method used.
We will generally have more faith in results that have been repeated.
Precision and confidence:
Precision refers to the probability that our estimations are correct.
(statistics closer interval 35 instead of between 20 and 50)
Confidence refers to the probability that our estimations are correct.
(95% of the time our predictions are true, that is called confidence
level)
The narrower the limits within which we can estimate the range of our
predictions (the more precise our findings) and the greater the
confidence we have in our research results, the more useful and
scientific the findings become.
Objectivity:
Conclusions should be based on the facts derived from actual data, not
your own subjective or emotional values.
, The more objective the interpretation of data, the more scientific the
research investigation becomes.
Generalizability:
Generalizability: Refers to the scope of applicability of the research
findings in one organizational setting to other settings.
The more generalizable the research, the greater its usefulness and
value
However often applied research focusses on a particular problem and
the results, at best, are only generalizable to other identical situations.
Parsimony:
Simplicity in explaining the phenomena or problem that occur, and in
generating solutions for the problems, is always preferred to complex
research frameworks that consider an unmanageable number of
factors.
Simplest assumption adoption
The hypothetico-deductive method
1. Identify a broad problem 4. Determine measures
area 5. Data collection
2. Define the problem 6. Data analysis
statement 7. Interpretation of data
3. Develop hypotheses
Identify a broad problem area:
Identifying a problem
Define the problem statement:
To find solutions for identified problems, a problem statement that
includes the general objective and research question of the research
should be developed.
Gathering information on what is happening and why.
Determine hypotheses:
The hypotheses should be testable and falsifiable.
Falsifiable: It must be possible to disprove the hypothesis. According
to Karl Popper this is important, because a hypothesis cannot be
confirmed; there is always a possibility that future research will show
that it is false. It remains provisional until it is disproved.
Determine measures:
Unless the variables in the theoretical framework are measured in some
way, we will not be able to test our hypotheses. What will there be
measured.
Data collection:
After we have determined how to measure our variables, data with
respect to each variable in the hypothesis need to be obtained. These
data then form the basis for data analysis.
Data analysis:
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