Academic writing
Is formulaic. It is about research conversations.
Finding literature - backwards
Looking at the reference list identifies important studies on which the focal study builds - cited refs.
Finding literature - forward
identifies cutting-edge work (times cited).
Structuring
- Sections (intro, theory, methods, discussion)
- Subsections, e.g. what we know, what we do not know, what I do, what it solves, and why it is important
- Paragraphs (number, main idea, logical sequencing)
- First and last sentence of each paragraph
- Evidence/arguments within paragraph (making sure they contribute to only one idea, i.e. one paragraph, one thought)
Common structure Qualitative research
Introduction - methods - findings - theory - interpretation - conclusion
Common structure Quantitative research
introduction - Theoretical background - methods and data - results - discussion - conclusion
Research Setting
- In what social setting is the research conducted?
- Why is this setting appropriate for your study?
Research Design
- What data do you use to answer your question?
- How do you analyze the data?
Qualitative research
- data are typically not in numerical form, but textual, visual, audio. Analyses are more verbal/rhetorical in nature.
- tend to be process-focused
- there is a focus on the why or how; deep, embedded understanding of the phenomenon of interest.
Quantitative research
- data are typically in numerical form. Analyses are inferential in nature (regression- based, explaining variation)
- research obtain statistical evidence to support or reject hypotheses.
- the focus is on the strength of relationships between concepts. The mechanisms are theorized, but often not explicitly tested.
Different ideas of replication and transparency
- In quantitative work, you need to report your steps in such a way that a person with the same data would get the same results. Reproducibility is at the heart of the scientific enterprise.
- In qualitative work, one might even argue that the same person with the same data does not need to come to the same results.
- For both, there is a common aim for trustworthiness and integrity.
Dependability
Explain and justify methodological choices and/or interpretations in detail
Confirmability
Neutrality: Results should not be caused by researcher bias, motivation or interest.
Credibility
Confidence in the truth of the result, i.e. not simply wrong
population
The total set of observations of interest to your study. E.g., all firms.
sample
The subset of the population that you empirically study. E.g., Dutch firms in the IT industry, active right now.
Sampling
The process of selecting units (e.g., people, organizations) from a population
Unit of analysis
The major entity that is being analyzed. E.g., firm, individual, group, dyad.
Ecological Fallacy
Drawing conclusions about individuals based on group data
Exception fallacy
Drawing conclusions about a group based on exceptional cases.
Primary Data
refers to data that is hand-collected specifically for your research:
- Qualitative example: Interviews
- Quantitative example: Survey data
Secondary data
Refers to pre-existing data that can be used for your research:
- Qualitative example: Internal memos - Quantitative example: Archival data
Cross-sectional data
Refers to a sample taken at a single point in time.
Longitudinal data
refers to observations over time:
- Repeated/pooled cross-sections: New cross- sections every time.
- Time series: Observations of a variable over time (underlying sample may change, common in finance).
- Panel data: Time-series for each cross- sectional member. This is often seen as the gold standard.
