Milestone 4 - Analysis

🎯 Objectives

By the end of this milestone, you should be able to:

• Clean and transform your variables for rigorous analysis.
• Produce analytical figures that explore relationships between variables and assess your theoretical expectations.
• Interpret patterns in relation to your research question.

🧠 Tips for Success

• Prepare your data. Document every transformation you make and justify your choices.
• Choose appropriate visualizations. Different relationships require different plot types (scatter plots for continuous variables, box plots for group comparisons, etc.).
• Connect figures to hypothesis. Every visualization should help you assess an aspect of your research question.

🧩 The Milestone

Building on Milestone 3's basic exploration, you will now conduct a more sophisticated analysis that goes beyond the exploration of single variables. This milestone emphasizes an initial hypothesis assessment through visualization.

You need to:

• Use your Milestone 3 (.typst file and .ipynb) as a starting point, incorporating any feedback received.
• Apply data cleaning, transformation, and analysis techniques learned in recent modules
  • 04-data-exploration-columns.ipynb
  • 05-data-exploration-rows.ipynb
  • 06-data-management-existing-values.ipynb
• Update your analysis with improved visualizations that examine relationships between variables and assess your initial theoretical expectations.

What is expected from a manuscript perspective

Your Milestone 4 typst file is an updated Milestone 3 typst file that:

• Does not exceed 3,000 words (excluding references, figures, and captions)
• Incorporates new figures: Integrate new figures with your analytical visualizations from M4
• Adding interpretation sections: Write paragraphs connecting each figure to your theoretical expectations
• Updating literature review: Expand insights gained from scale construction or variable relationships and how do they compare to the existing literature.
• Any analytical choices in your code should have support with academic literature. Cite methodological sources that support your approach to combining variables (e.g., Political knowledge is traditionally measured using questions such as ... @author2020scale)
• Typst syntax: Ensure proper citation format using @citationkey for all academic references

What is expected from a coding perspective

Similarly, your Milestone 4 ipynb file is an updated Milestone 3 ipynb file that:

1. Data Cleaning and Variable Preparation
   • Filter invalid responses: Remove or recode missing values, "don't know," and refusal responses
   • Handle outliers: Identify and address extreme values that may distort analysis
   • Create clean variables: Document all transformations and justify your choices
2. Scale Construction (when applicable)
   • Identify related variables: Find multiple questions measuring the same concept
   • Create additive scales: Combine variables into robust composite measures
3. Analytical Visualization
   • Scale Visualization: Create visual representations of your constructed scales when applicable
   • Test IV-DV relationship: Create figures that directly examine your main hypothesis
   • Control for key variables: Include subgroup analyses or control variables where relevant
   • Interpret patterns: Explain what the figures reveal about your theoretical expectations

💾 Submission Guidelines

1. Export final files from both Typst and your notebook:
   • da-milestone4-group0.typ
   • da-milestone4-group0.pdf
   • da-m4-notebook-group0.ipynb
2. Keep file names consistent for organization.
3. Submit three files in the General Chat.
4. Manuscript length must not exceed 3000 words (excluding references, figures, and captions).

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This milestone represents a step toward your final paper, marking an initial step towards bridging descriptive exploration with hypothesis testing.