Quantitative Research Methods - Summary of all Lectures and Book! 2024
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Course
Quantitative Research Methods (E_PM_QUANRM)
Institution
Vrije Universiteit Amsterdam (VU)
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Summary for premaster course Quantitative Research Methods of the premaster Business Administration.
Lecture 1
Quantitative research: What, why, when.
What is quantitative research: Quantitative research methods revolve around answering a
particular research question by collecting numerical data that are analyzed by the use of
mathematical methods (in particular,statistics).
Types of quantitative research.
Descriptive (what?).
● Interested in a quantitative answer: How many students are enrolled in the
premaster? Which AirBnB properties have the highest booking rate?
● Interested in a numerical change: Are the numbers of premaster students in our
university rising compared to last year?
Inferential (why?).
● Test relationships: What is the relation between students’ self-esteem and their
average grade in the premaster?
● Explain something: ‘What factors cause changes in student performance over time?’
Why do we need quantitative research:
● In essence, quantitative research methods provide us with a toolbox to study the
(social) world around us by the use of the scientific method.
● Helps in minimizing cognitive assumptions that may distort our interpretation.
● Depending on the state of prior theory and research on the topic, you have to use
quantitative methods to make a useful contribution to our understanding of the world.
● Only way to establish causal relationships.
Trust your intuition: the curse of belief bias.
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,When do we need quantitative research?
Good research questions:
● Can be answered and need answering (‘so what?’).
● Improve our understanding of how the world works.
● Inform theory.
What is a theory?
● A theory is an explanation of relationships among concepts or events within a set
of boundary conditions.
● A (good) theory simplifies and explains complex real-world phenomena.
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,Elements of a good theory.
● What: Constructs and variables that logically should be considered part of the
explanation of the phenomenon of interest.
● How: Propositions and hypotheses that indicate the “links” between constructs and
variables. (Typically) indicate causality.
● Why: The “glue” that justifies the selection of constructs/variables and their proposed
relationships.
● Who, where, when: The conditions under which the theory should hold. Set the
limitations of the generalizability of the theory wrt context, time, and space.
The data generating process (DGP).
● Our theoretical model is only a (small) part of the broader, more complex data
generating process (DGP).
● We need to make credible claims about the complete DGP so that we can identify
the variation in the data that answers our research question.
Promotion versus prevention focused questions.
Founders’ characteristics → Prevention and promotion-focused questions → Funding.
SEE EXAMPLE IN SLIDES.
A good research design helps us achieve this by:
1. Using theory, paint the most accurate picture possible of what the data generating
process looks like.
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, 2. Use that data generating process to figure out the reasons our data might look the
way it does that don’t answer our research question.
3. Find ways to block out those alternate reasons and so dig out the variation we need.
To do so, we want to optimize the validity and reliability of our study.
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,Chapter 1 of Huntington-Klein (2021)
Designing Research
A research question is a question that you have that you plan to answer, or at least try to
answer, by doing research. A good research question is well-defined, answerable, and
understandable. Well-designed research is research capable of answering the question it’s
trying to answer.
Empirical research is any research that uses structured observations from the real world to
attempt to answer questions. So instead of trying to reason our way through what drivers
would do if given an additional highway lane, we try to observe the choices that drivers take.
A problem with quantitative empirical research is that the numbers that we observe often
don’t tell us exactly what we want to know.
A good research design is essential because it helps prevent incorrect conclusions, ensures
the study addresses the right questions, and produces reliable results. This way, research
can provide trustworthy information that guides decisions effectively.
Chapter 2 of Huntington-Klein (2021)
Research Questions
A research question is a question that can be answered, and for which having that answer
will improve your understanding of how the world works. Theory just means that there’s a
why or a because around somewhere. A good research question takes us from theory to
hypothesis, where a hypothesis is a specific statement about what we will observe in the
world. Great research questions often come from the theory themselves - the line of thinking
being “if this is my explanation of how the world works, then what should I observe in the
world? Do I observe it?”. A good test for whether a research question informs theory is to
imagine that you find an unexpected result, and then wonder whether it would make you
change your understanding of the world.
Data mining involves looking through a large amount of data to find patterns or relationships
without initially trying to understand why those patterns exist. It focuses on discovering
correlations from data and is often used for making predictions, but it doesn't necessarily
explain the reasons behind these patterns.
A false positive in the context of data mining occurs when the data suggests a relationship or
pattern that does not actually exist. This can happen by chance when a lot of different
variables are checked; some will appear to be related just because of random variation, not
because there is a true underlying connection.
Starting with a research question is crucial because it guides the research process, helping
to focus on what's important and avoid irrelevant or random findings. Without a solid
research question, research can lead to confusing or misleading results, such as false
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,positives. A well-defined question helps in structuring the investigation and making sense of
the data in a meaningful way.
Research questions can arise from many sources, mainly our curiosity about how the world
works. This curiosity often leads us to form theories about why things happen a certain way,
like why plants survive without food or why CD sales might be declining. These theories then
guide us to create specific research questions that help us test these ideas.
The process of coming up with research questions can start with a theory or a direct
question. For instance, you might have a theory and then develop a question to test it, or you
might start with a question and then think about the theories behind it. If a question doesn't
connect well with a theory, it might not be a strong research question.
Sometimes, the opportunity to ask a question comes from having access to unique data or
learning about something new happening in the world. For example, if schools start paying
students for good grades, this could lead to new questions and theories about education and
motivation.
Check if your research question is good with this points:
● Consider potential results: If you can’t say something interesting about your potential
results, that probably means your research question and your theory aren’t as closely
linked as you think.
● Consider feasibility: A research question should be a question that can be answered
using the right data, if the right data is available.
● Consider scale: What kind of resources and time can you dedicate to answering the
research question?
● Consider design.
● Keep it simple.
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,Week 2
Lecture 2
Validity, do we measure what we want to measure?
Construct validity.
A measure is valid to the degree that it represents what you are trying to measure.
Internal vs. external validity.
● Internal validity is the extent to which you are able draw the correct conclusions
about the causal relationships between variables.
● External validity is the extent to which your findings are generalizable to the broader
population (of individuals, firms, ...) and different settings.
Omitted variable bias.
Omitted variable bias occurs when you leave out (omit) an independent variable that is a
determinant of the dependent variable and correlated with one or more of the included
independent variables.
In this case, leaving out this independent variable will lead to an over- or underestimation of
the relation between your variables of interest.
In our analysis we need to control or adjust for these variables!
Example: In our example, startup characteristics are omitted variables:
● It is positively correlated with funding success.
● It positively affects the likelihood that investors invest and/or the amount.
In this case, ignoring startup level variables, will likely make us overestimate
the true relation between founder variables and funding success
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, Reverse causality.
Reverse causality occurs when the direction of the arrow in our theoretical model goes the
other way. E.g. Funding success increases the likelihood that entrepreneurs are
experienced.
Very difficult to empirically rule this out (beyond the scope of this course).
Sometimes logical reasoning can help us.
Example:
Sample selection.
It is almost never possible to obtain data from the full population of interest.
● E.g., you do not have data on all pitches.
● E.g., you do not have data on all startups and accelerators worldwide.
You need to make sure that your sample is representative of your population
of interest.
● E.g., that you do not only have large, innovative startups,
● E.g., women-founded startups.
(Sample) selection bias = the selection of data for analysis in such a way that proper
randomization is not achieved, leading to an unrepresentative sample of the population
intended to be analyzed.
To avoid these issues, we need to draw an independent and identically distributed (i.i.d.)
sample from the population.
= Random sample.
● Identically Distributed means that there are no overall trends–the distribution
doesn’t fluctuate and all items in the sample are taken from the same probability
distribution.
● Independent means that the sample items are all independent events. In other
words, they aren’t connected to each other in any way.
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