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Abstract

The abstract is a short summary of the article and a roadmap guiding the reader. It gives readers a preview of what it is to come. It also showcases the unique contribution. A strong abstract helps readers decide whether to read the full article, and often determines its reach and influence. It should contain a broad statement about the topic, move towards your specific research question, method, finding/results, and discussion of the wider implication(s) of your finding. It should be about 250-300 words.

1 Introduction

This section sets out the real-world context for the paper and the motivation behind the research question. It describes the research question itself and the literature it draws on. It summarizes the data and methods, as well as the structure of the rest of the paper. Some overlap with the abstract, literature review, conclusion, and discussion is acceptable. The introduction should not include subheadings. It should start broad and then zoom in on the specific question. It may identify key analytical themes and the literature the paper engages with, and then emphasize the gap it is trying to fill: Are you building on existing work or pushing back against it?

Some helpful questions in this section include:

• What does your research demonstrate?
• What does your research teach us that we didn’t already know?
• Why do we need this information? How does it benefit the discipline or sub-discipline?

2 Literature review

The literature review is a synthesis of the research previously conducted on a topic. It provides background for your study and helps demonstrate what your research will add—particularly how it addresses the limitations or weaknesses of existing studies. It may also highlight areas of disagreement among scholars. The literature review is not just a summary; it is also a critical analysis that emphasizes what has not yet been done.

How to do a Literature Review

1. Search the Relevant Literature on Google Scholar: pick credible sources (peer-reviewed journals and academic books)
2. Evaluate sources
3. Organize: Identify themes, debates
4. Identify the gaps, what is missing or contradictory
5. Outline the structure of the literature review
6. Write your literature review in context

At the end of the literature review, you need to identify the gap. Here are some questions to help find the gap.

• What is the current literature missing?
• Why is it missing this important point?
• What are the consequences of this gap?
• Why does the gap matter?

The gap needs to be followed by the contribution.

• What does my reader now know that they didn’t know before?
• How am I changing their understanding?
• How do I want them to think differently after reading this article?

Your contribution should be new (not just a minor variation); relevant (to current debates or concerns); connected to the gap (not a standalone insight). To make your contribution clear, consider whether your project:

• Introduces overlooked data or cases
• Applies methods in novel ways or to new sources
• Offers a new interpretation of a theory
• Challenges dominant frameworks
• Bridges two literatures or concepts not usually in conversation

3 Theory and Argument

If the literature review is about what others have said, the theory and argument section is about what you are arguing. In this section, you need to include definitions for the main concepts in the paper. It should also present your argument—the central claim of your paper, or your answer to the research question. The argument must be situated within a theory or a theoretical framework. A typical framework addresses:

• What variables help explain the outcome?
  
• How are those variables defined (conceptually and operationally)?
  
• What are your hypotheses (testable expectations) with clear dependent and independent variables?
  
• What is the spatial/temporal domain of the study?
  

Another important aspect that should be included in this section is a DAG (directed acyclic graph) - a visual representation of the causal theory proposed in the paper. You can find below an example of a very simple DAG in Figure 1.

# Load the packages we need.
# These give us tools for working with Directed Acyclic Graphs (DAGs)
# and plotting them in a nice way.
library("ggdag")     # Helps plot DAGs with ggplot2
library("dagitty")   # Defines and manipulates DAGs mathematically
library("ggplot2")   # General plotting library

# --- STEP 1: Define the DAG (the causal model) ---
# A DAG is just a drawing that shows which variables cause which others.
# dagify() lets us say "X causes Y" using the formula format: Y ~ X
simple_dag <- dagify(
  outcome ~ mediator,         # Outcome is caused by the mediator
  mediator ~ treatment,       # Mediator is caused by the treatment
  exposure = "treatment",     # "Exposure" = our independent variable (IV)
  outcome = "outcome",        # "Outcome" = our dependent variable (DV)
  labels = c(                 # Nice readable labels for the plot
    treatment = "Treatment\n(IV)",     # IV = Independent Variable
    mediator = "Mechanism\n(Mediator)",# Mediator variable
    outcome = "Outcome\n(DV)"          # DV = Dependent Variable
  ),
  coords = list(              # Coordinates: where to place nodes on the plot
    x = c(treatment = 0, mediator = 1, outcome = 2),  # left-to-right
    y = c(treatment = 1, mediator = 1, outcome = 1)   # same height
  )
)

# --- STEP 2: Convert DAG into a tidy data frame for plotting ---
# tidy_dagitty() turns the DAG object into a format ggplot can use.
dag_df <- as.data.frame(tidy_dagitty(simple_dag, layout = "auto"))

# Set the limits of the plot window so everything fits nicely.
# min_x etc. are the smallest/largest x- and y-values of our nodes.
min_x <- min(dag_df$x)
max_x <- max(dag_df$x)
min_y <- min(dag_df$y)
max_y <- max(dag_df$y)
error <- 0.2   # Add a little space around the plot

# --- STEP 3: Draw the DAG using ggplot2 ---
ggplot(data = dag_df) +
  # Draw the variables (nodes) as large semi-transparent circles
  geom_dag_point(aes(x = x, y = y), size = 30, alpha = 0.5) +
  
  # Add labels on top of each node so we know what it is
  geom_label(
    data = subset(dag_df, !duplicated(label)),  # one label per node
    aes(x = x, y = y, label = label),
    fill = alpha("white", 0.8)                  # white background, slightly transparent
  ) +
  
  # Draw arrows to show the causal direction
  geom_dag_edges_arc(
    aes(x = x, y = y, xend = xend, yend = yend),
    curvature = 0.0,  # straight arrows (set higher for curves)
    arrow = grid::arrow(length = grid::unit(10, "pt"), type = "closed")
  ) +
  
  # Adjust the view so nothing is cut off
  coord_sf(xlim = c(min_x - error, max_x + error),
           ylim = c(min_y - error, max_y + error)) +
  
  # Remove background, axes, and gridlines (just show DAG)
  theme_void()

Figure 1: Causal Framework (DAG)

Finally, this section should also include scope conditions - a statement about where your theory is expected to hold.

4 Methods

The methods section should include answers to the following questions:

• How did you do your research?
• What was the research design?
• Was it quantitative, qualitative or mixed?
• What is the unit of analysis (e.g. individuals, districts, countries, etc.)?

Research Design

The research design is the framework, the backbone of the methods section. Within this section, you should answer questions like:

• What data are you using?
• Where is it from?
• How novel is it?
• How are you analyzing the data?
• How do the results help answer the question?
• Any limitations?
• What variables are analyzed?
• Why were they chosen?

For a quantitative paper with observational data, the ideal research design is either differences-in-differences or regressions discontinuity design.

5 Findings

The findings section focuses on what the data shows. It can be both descriptive and evidence-based. Some examples include survey statistics, quotes, or observed patterns. To help write the findings section, it is also helpful to answer the following questions:

• Does each finding directly help answer the research question?
  • Clearly state how it does so.
• Are the findings relevant and thematically cohesive?
  • Organize them by theme, not just by chronology or data source.
• Are your findings situated in existing research?
  • Compare them to 2–3 similar studies using the same method or tackling a similar problem.

Label figures and refer to them in the text.

6 Discussion

The discussion section interprets your findings—this is where you explain the “so what?” It connects your results back to the research question, the existing literature, and the broader theoretical or practical implications. Some important questions that should be addressed in this section include:

1. Why do the findings matter?

• What contribution does it make to key debates or theories?
• Does it challenge, confirm, or reframe existing literature?
• Does it reveal previously overlooked relationships or offer a new perspective?

2. What are the broader implications?

• How should readers now think differently about the literature or topic?
• What paths does it open for future research?

3. What are the limitations?

• What can’t this article answer?
• What future work could address these gaps?

7 Conclusion

The conclusion section should restate the key argument in parallel to the introduction (e.g., “I have shown that gender quotas are a key factor in…”). It should also emphasize—similar to the discussion—why the findings matter, what the implications are for the literature and a general audience, and suggest opportunities for future research (e.g., unresolved questions). The following questions can be helpful to address in the conclusion:

• What does your research demonstrate?
• What does your research teach us that we didn’t already know?
• Why do we need this information? How does it benefit the discipline or sub-discipline?
• Why do your findings matter? What should the reader remember?
• How are your findings important for a general audience?
• What questions are left to answer?

8 Appendix

Cross Referencing

To reference a figure with example label “plot”, use @fig-plot. Analogously, to reference a table with example label “data”, use @tbl-data. To reference a section, such as the Introduction (Section 1), use @sec-intro.

For complete information on cross referencing with Quarto, see https://quarto.org/docs/authoring/cross-references.html.

Citations

Quarto formats citations and references automatically¹ using the bibliography records in your .bib file. For a citation in parentheses use (Simpser 2012). Simpser (2012) suggests this.

Multiple citations can be given as Simpser (2012); Acemoglu and Robinson (2012); Tiliouine, Renima, and Estes (2016) or (Simpser 2012; Acemoglu and Robinson 2012; Tiliouine, Renima, and Estes 2016). See https://quarto.org/docs/authoring/footnotes-and-citations.html#sec-biblatex for more information.

Tables and Figures

To include figures, you can use Quarto syntax. See for example figure Figure 2.

Figure 2: An example figure

For both figures and tables, you can use LaTeX syntax if you need additional customizability. You can also use footnotes in tables like in Table 1.

Table 1: Example Table

Column 1	Column 2	Column 3	Column 4
row 1	data 1	data 2	data 3
row 2	data 4	data 5¹	data 6
row 3	data 7	data 8	data 9²

¹ Example for a first table footnote.
² Example for a second table footnote.
Source: This is an example of table footnote.

9 References

Acemoglu, Daron, and James Robinson. 2012. Why Nations Fail: The Origins of Power, Prosperity and Poverty. New York: Crown.

Simpser, Alberto. 2012. “Does Electoral Manipulation Discourage Voter Turnout? Evidence from Mexico.” The Journal of Politics 74: 782–95.

Tiliouine, Habib, Renima Ahmed, and Richard J. Estes. 2016. “The Changing Map of the Islamic World: From the Abbasid Era to the Ottoman Empire of the Twentieth Century.” In The State of Social Progress of Islamic Societies, edited by Habib Tiliouine and Richard J. Estes, 53–68. Routledge.

Footnotes

1. This is an example of a footnote.