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Samenvatting iDR course
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Designed Research 1Research Cycle
1. Problem analysis
- What is the problem? Reason of the research? Main research question?
2. Research design
- How to research this? Which tool to use? Which people to ask?
3. Data collection
- Gathering responses on the street or online?
4. Data analysis
- Summarising and analysing the responses, make graphs, calculate averages
5. Reporting
- Write down your conclusion and recommendations
Problem analysis
1. Direct cause
- What is the reason and the situation
2. Problem definition
- What is known already
3. Aim
- The aim of the research study
4. Research question
5. Hypotheses
Basic (fundamental) research:
Generates a body of knowledge by trying to comprehend how certain problems that occur in
organizations can be solved
Applied research (solve problems)
Solves a current problem faced by the manager in the work setting, demanding a timely solution.
Example: Lisa wants to develop an app to practice names
1. Direct cause: Lisa has observed that many people have difficulty remembering
names. She is thinking of building an app that would help people to
practise names.
2. Problem: Lisa does not know whether people actually want an app to practise
names
3. Aim: Finding out to what extent people consider forgetting names a real
problem. If only few people consider it a problem, there is probably no
need for a practise app and Lisa won’t have to develop one.
4. RQ: Are there suitable conditions for the development of a name-practise app? E.g.
How good are people at remembering names / How good do the people think they
are at remembering names / When people are bad at remembering names, do
they consider it a problem?
5. Hypotheses: Most people are not good at remembering names / People who are bad at
remembering names do not consider it a problem / Less than 1 in 10 people
consider forgetting names a real problem / Lisa does not need to develop the app.
Elements of research design
1. Operational of variables
2. Type of research and study
3. Population and sampling
4. Research method
5. Representativeness, biases and quality
,1. Operationalization
Every concept or term that you used in the problem analysis needs a detailed definition. What do you
mean exactly when you use term ‘X’ in the context of your research? What does ‘X’ refer to?
Example:
Say, you are planning to conduct a study with the following research question: “Is the weather forecast
good for tomorrow?”. You need to be more specific than this.
- When is the weather qualified as good?
- What do you mean with weather? Temperature? Wind? Rain?
- Whose forecast do you use?
- Which day do you exactly mean?
2. Type of research and study
Once you know exactly what it is that you would like to find out, it’s time to ask yourself how you are
going to do this. What type of research would be most suitable for answering your research question?
Different kinds of studies
1. Descriptive = presenting known information
2. Exploratory = trying to find new information
3. Examinatory/causal = checking if your conjectures (hypotheses) are true
Qualitative
Non-numerical data is gathered. Such as opinions, meanings & characteristics
Quantitative
Gathering quantifiable data in order to be able to analyse statistically or mathematically
3. Population and sampling
Population and sampling are the usual suspects in any description of a research design.
The population = the focus group of your research.
Sample = an even smaller group
Drawing a sample from a population is called sampling
Drawing conclusions about a population based on a sample is referred to as making inferences or
generalizing
4. Research method
The method section is the heart of your research design. There are three questions you need to ask
yourself here:
- How will you acquire information? Which research tool are you going to use?
- How will you measure that which is of interest to you research? Which instruments are you
going to use to tap into your constructs?
- How will you execute your research? What procedure will you follow?
4.1 Research tools
There are various different research tools to choose from. You could do an experiment, conduct a
survey, interview people, make observations etc.
Which tool would be most suitable for your research depends on your Research Question, the aim of
your study and the type of your research. In addition, practical considerations will tend to weigh into
your choice of tool, since you will often have limited time and resources available for your research.
4.2 Instruments
Remember this research question from earlier? “Is the weather forecast good for tomorrow?”
Let’s say you operationalised ‘weather’ as: amount of precipitation. How will you now measure the
variable ‘weather’? What instrument are you going to use to measure precipitation? You have several
options:
- Meteorological websites with a rain radar
- The opinion of one of your friends who’s an amateur meteorologist
- Your grandma who believes that pain in her legs is an excellent predictor of rain
- An air humidity meter
, Of course, some of these instruments are a lot more reliable than others. In addition, they vary
regarding the time and costs tied to using them. Your choice of which instruments to use in your
research is again both a theoretical and a pragmatic one.
What applies to measuring weather conditions, also holds true for concept in the social sciences. Take
‘customer satisfaction’ for instance. This can be measured by asking customers questions like:
- Are you satisfied? Yes/No
- How satisfy are you on a scale from 1 to 10?
- On a scale from 1 to 10, how likely are you to recommend our service to someone else?
- The number of received complaints (per week/month)
- Number of returning first-time buyers
Writing up your research method is a bit like writing a recipe for a cookbook.
First you choose a dish (research tool) that suits the theme of the book (your research) and then you
describe the ingredients that go into the dish (instruments). Lastly you spell out each step that needs
to be taken to make the dish (procedure).
5. Representativeness, biases and quality
How successful will my research design be at answering my research question?
To answer this question there are two things you definitely need to consider:
1. Representativeness
2. Quality of your measurements
5.1. Representativeness
When you have drawn a sample bases upon which you would like to come up with generalised
conclusions about the whole of a population, it is important to ask yourself whether your sample is
sufficiently representative of this population.
Example:
Say you would like to know how many of your fellow countrymen tend to travel by train. You happen to
be diving your car on the highway so you pull over at a gas station and survey a couple of people on
how often they travel by train. It shouldn’t come as a surprise that you would find a completely
different answer to your question this way than when you would have surveyed people on a train
station.
When your sample is not representative of the overall population you are not allowed to generalise
your conclusions and doing so would be considered a mistake.
5.2. Quality of the measurements
Besides the representativeness of your sample, you also need to check the quality of your
measurements. For instance, when predicting precipitation using an air humidity meter you do need to
pay attention to where you place the meter. There’s no point in choosing a suitable measurements
instrument if you then end up using the wrong way.
In discussions about the quality of measurements you often encounter the term bias.
This term was first used in radio technology where it refers to the degree to which a received radio
signal has been distorted. In a research context bias refers to the degree to which the objectivity of
measurements is affected by noise. Noise can, among other things, be caused by poorly chosen
instrument. For instance, the use of leading questions in a questionnaire.
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