From Cleaned Data to Hypotheses¶

This module bridges the gap between transformed survey data and empirical hypothesis testing. Using grouped comparisons and cross-tabulations, we examine whether observed differences between groups are substantively meaningful.

Theory¶

Code: Live Demo & Hands-on Lab¶

Student Group Live Demo (Group 2)¶

Topic: Subgroup Analysis & Cross-Tabulations (groupby(), comparing group means, pd.crosstab(..., normalize='index')).
Review the Live Demo Guidelines & Schedule.

Hands-on Practice¶

Download and open 06_data_management_and_scales.py from the Course Materials to compute grouped means and two-way cross-tabulations on your project variables using pandas (.groupby(), pd.crosstab()).

Application¶

Milestone 4 Check-in & Review¶

Share initial bivariate patterns and receive peer and instructor feedback on your subgroup comparisons.

Get Ready for Next Session: Think. Explore. Practice.¶

Think¶

How does your research hypothesis translate into a formal linear regression model ($Y = \beta_0 + \beta_1 X + \epsilon$)? What is the expected sign (+ / -) of the slope coefficient $\beta_1$?
For categorical predictors (e.g., race, religion, region), what is your reference/baseline category, and how will you interpret differences relative to that baseline?
Suggested Reading: Argyle, L. P., Busby, E. C., Fulda, N., Gubler, J. R., Rytting, C., & Wingate, D. (2023). Out of one, many: Using language models to simulate human samples. Political Analysis, 31(3), 337-351. - Illustrates rigorous regression model specifications and how contemporary computational methods interface with empirical political behavior.

Explore¶

Live Demo (Group 3): Linear Regression & Categorical Predictors. Group 3 prepares a 10–15 min demonstration and shares the handout on WhatsApp before class (groups can book a meeting with the instructor to get direction).
The class should review the course materials (07_intro_to_modeling.py and 09_categorical_models.py) and the Statsmodels Formula API Guide to follow along and lead the peer discussion.

Practice¶

Start a modeling script (modeling.py) in your group project workspace and fit a baseline regression ($DV \sim IV$) with smf.ols.
Looking Ahead: Milestone 5 - Modeling will be due in Session 11 (Mar 05 at 08:00).