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Summary Quantitative research methods
- Instelling
- Vrije Universiteit Amsterdam (VU)
Summary lectures and book for premaster Business Administration
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Quant + theoryWhat is quantitative
● Definitions: 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:
○ Descriptive: what? Numerical change.
○ Inferential: why? To test relationships and to explain something
● Why:
○ 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
What is a (good) 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
● 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
Research Design:
1. Using theory, paint the most accurate picture possible of what the data
generating process looks like
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
4. To do so, we want to optimize the validity and reliability of our study
What is a good research question:
● Question That Can Be Answered:
○ A research question should be framed in a way that it can feasibly be answered
through evidence or data. For instance, questions like "What is the best James Bond
movie?" are too subjective and ambiguous to yield definitive answers.
○ Conversely, questions such as "Which era of Bond movies had the highest ticket
sales?" can be answered by examining available data on ticket sales.
● Improving Understanding of How the World Works:
, ○ A good research question should contribute to broader understanding or theory,
providing insights into why things happen the way they do. For instance, in the
example provided, the research question about Bond movie ticket sales could
contribute to a theory about the popularity of action movies in different time periods.
● Theory and Hypothesis:
○ A theory provides an explanation for observed phenomena. Take germ theory, for
example. Germ theory says that microorganisms like bacteria and viruses can cause
disease. This explains why we have diseases, and also why disease can spread from
one person to another. We don’t call it “a theory” because we’re uncertain about
whether it’s true. We call it a theory because it tells us why. A good research question
stems from theory and leads to specific hypotheses that can be tested through
empirical research.
● Informing Theory:
○ A research question should provide evidence that informs or challenges existing
theories. If unexpected results arise from answering the question, it should prompt a
reevaluation of the underlying theory.
Why start with a question?
Starting with a research question is crucial despite the availability of vast amounts of data. While data
mining, or the process of extracting patterns from large datasets, can be tempting due to its potential
for revealing correlations and making predictions, it has limitations in contributing to theoretical
understanding. Here's why starting with a question is important:
● Focus on Causality: Data mining is adept at uncovering correlations but often fails to explain
why those correlations exist. For instance, it might identify a relationship between ice cream
sales and the proportion of people wearing shorts without understanding that both are driven
by hot weather rather than a direct causal relationship between shorts and ice cream
consumption.
● Limited Abstraction: Data mining deals with observed patterns in the data without
understanding the underlying concepts or theories. It might identify common features of
chairs, like a flat seat and legs, but miss the abstract concept of a chair designed for sitting.
● Risk of False Positives: Data mining involves examining numerous variables, increasing the
likelihood of finding spurious correlations purely by chance. These false positives can mislead
researchers if not carefully addressed.
Where do research questions come from?
● Theory-Driven Approach:
○ The process starts with a theory about how the world operates, which can stem from
observations, existing literature, or personal hypotheses.
○ The theory provides a framework for understanding phenomena and predicting
outcomes.
○ From the theory, researchers derive hypotheses and research questions that aim to
test or explore the underlying assumptions.
○ For example, a theory about student motivation based on incentives might lead to a
research question about the effectiveness of paying students for good grades.
● Question-Driven Approach:
○ Alternatively, the process can begin with a specific research question prompted by
curiosity or observation.
○ Researchers may then backtrack to consider the underlying theory or rationale
behind the question.
○ If the question lacks a clear theoretical basis or reasoning, it may not be a suitable
research question.
, ○ For instance, a question about the impact of paying students for good grades might
prompt reflection on theories of motivation and incentives.
● Opportunity-Based Approach:
○ Research questions can also emerge from available data sets, unexpected events, or
unique circumstances.
○ Researchers may explore potential research questions based on the data's
characteristics, relevance to existing theories, or intriguing patterns.
○ Likewise, unusual occurrences or interventions, such as schools implementing
incentive programs, can spark questions about their effects and underlying
mechanisms.
How do you know if you have a good one?
● Consider Potential Results: A valuable check is to envision potential outcomes and their
implications for the relationship between your research question and theory. If students
indeed work harder when paid for good grades, it supports theories of incentive-driven
behavior. Conversely, if payment doesn't influence their efforts, it may challenge the efficacy
of incentives in education.
● Consider Feasibility: It's essential to assess if the necessary data is accessible and if the
research question is realistically answerable. If obtaining the data is excessively challenging
or costly, it might be necessary to reconsider the feasibility of the research question.
● Consider Scale: Understanding the available resources and time is crucial. Questions should
be tailored to fit within the scope of these constraints. Attempting to tackle overly complex
questions without sufficient resources may lead to inefficiencies and compromised results.
● Consider Design: Evaluating the practicality of the research design is vital. A well-crafted
study design ensures that the research question can be adequately addressed. This involves
selecting appropriate methodologies and approaches to gather and analyze data effectively.
● Keep It Simple!: Avoid complicating matters by bundling multiple research questions into
one. Instead, focus on a single, manageable question. This approach allows for clearer
investigation and more meaningful insights without overwhelming complexity.
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