CMU 17-803 Empirical Methods — Worked Solutions & Reproducible Analyses

Written research-methods assignments and reproducible data-analysis scripts — an independent, from-scratch implementation of 17-803 — Empirical Methods (Carnegie Mellon University, instructor Bogdan Vasilescu), part of a csdiy.wiki full-catalog build.

Overview

17-803 is CMU's graduate course on empirical research methods for software engineering: formulating research questions and theory, qualitative interviewing and coding, survey and experiment design, statistics, regression and mixed-effects modeling, and quasi-experimental causal inference (interrupted time series, difference-in-differences, synthetic control). It is a research-methods course, so this repository is a solution portfolio with two halves:

1. Reproducible data-analysis scripts (analyses/) that port the course's hands-on R solution notebooks to Python (pandas + statsmodels) and run on the course's real datasets, reproducing the R reference numbers.
2. Rigorous written assignments (writeups/) covering the non-computational deliverables (study design, sampling, validity, qualitative coding, surveys, meta-analysis), grounded in the course's actual lecture topics and reading list.

The course publishes worked solutions as rendered R notebooks (e.g. typingSpeed.pdf, GaltonFams.pdf, zscore.pdf, its.pdf); this repo treats those as the ground truth and pins every Python port to their exact numbers with a pytest suite.

Results (measured on this machine — Windows, CPU, Python 3.11)

Every number below is produced by a script in analyses/ and saved under results/. Values match the course's R solution notebooks (verified by tests/).

Analysis	What it demonstrates	Key measured result (matches R)
typing_speed	Simpson's paradox; fixed- & mixed-effects	Pooled slope −0.63823, R² 0.4253, RSE 0.6605 (5 sig figs vs R); within-person slope flips positive
regression_galton	OLS build-up, standardized β, family LMM	childHeight~gender+father+mother: father 0.39284, mother 0.31761, R² 0.6354; family LMM R²m 0.630 / R²c 0.701
zscore_standardization	standardized coefficients + VIF	scale(YearsEdu) $103.84 vs scale(Knowledge) $63.18/SD; VIFs all < 1.8; collinear-toy VIF 113.70
interrupted_time_series	segmented regression + DiD	Durbin–Watson ≈0.20 (autocorrelation flagged); ITS-with-control treated effect ≈ −0.25, R² 0.988

Figures (in results/): Simpson's-paradox by-participant scatter, Galton regression-to-the-mean, z-score distributions & standardized β bar chart, ITS single-series segmented fit, and ITS treated-vs-control series.

Within each participant (colored lines) faster typing → more typos, yet pooling across participants the relationship reverses — the paradox a naive regression would get backwards.

Implemented assignments

Data-analysis scripts (verified reproductions of the course R notebooks):

• Typing Speed — Simpson's paradox, fixed-effects, and random-intercept mixed models (analyses/typing_speed/).
• Galton Families regression — intercept-only → gender → multiple → standardized → family random-effects LMM (analyses/regression_galton/).
• Standardized regression & multicollinearity — z-scored coefficients and VIF on the Wooldridge WAGE2 data (analyses/zscore_standardization/).
• Interrupted time series — segmented regression, Durbin–Watson autocorrelation, and ITS-with-control / difference-in-differences (analyses/interrupted_time_series/).

Written research-methods assignments (writeups/):

• 01 — Research questions & the role of theory.
• 02 — Interviewing & qualitative coding / thematic analysis (with a full worked thematic analysis).
• 03 — Survey design & mixed methods.
• 04 — Designing experiments, power analysis, & the four validity types.
• 05 — Regression, time series & causal inference (analytical companion to the code).
• 06 — Literature review, systematic review & meta-analysis.

Project structure

cmu-17803-empirical/
├── analyses/
│   ├── typing_speed/typing_speed.py
│   ├── regression_galton/galton_regression.py
│   ├── zscore_standardization/standardization.py
│   └── interrupted_time_series/its_analysis.py
├── writeups/                     # six written research-methods assignments (md)
├── src/empirical_common.py       # shared data/results/plotting helpers
├── scripts/download_data.py      # fetches course datasets at runtime
├── tests/test_analyses.py        # pins every port to the R reference numbers
├── results/                      # measured outputs: *.txt logs + *.png figures
├── data/                         # datasets land here (gitignored, not redistributed)
├── requirements.txt
└── LICENSE

How to run

# Python repos use the shared csdiy env (Python 3.11):
#   D:\Project\_csdiy\.venv-ml\Scripts\python.exe
python -m pip install -r requirements.txt        # or reuse the shared venv

# 1. fetch the real course datasets (not redistributed in this repo)
python scripts/download_data.py

# 2. run each analysis (writes logs + figures to results/)
python analyses/typing_speed/typing_speed.py
python analyses/regression_galton/galton_regression.py
python analyses/zscore_standardization/standardization.py
python analyses/interrupted_time_series/its_analysis.py

# 3. verify every result against the course's R solution numbers
python -m pytest tests/ -v

Verification

There is no autograder for this research-methods course; the course's own rendered R solution notebooks are the ground truth. Verification here is threefold:

1. Numeric reproduction. Each analysis prints numbers that match the R notebooks — e.g. the typing-speed pooled fit matches typingSpeed.pdf to five significant figures (intercept −0.09850, slope −0.63823, R² 0.4253); the Galton multiple regression matches GaltonFams.pdf exactly (father 0.39284, mother 0.31761, R² 0.6354); WAGE2 standardized/VIF values match zscore.pdf (VIF collinear toy = 113.6997).
2. A pytest suite (tests/test_analyses.py, 9 tests passing) that asserts these reference values, so any drift fails CI.
3. Saved evidence in results/ — text logs of every model summary plus the generated figures.

$ python -m pytest tests/ -v
tests/test_analyses.py::test_typing_pooled_matches_R          PASSED
tests/test_analyses.py::test_typing_within_between_signs      PASSED
tests/test_analyses.py::test_galton_gender_model             PASSED
tests/test_analyses.py::test_galton_multiple_regression      PASSED
tests/test_analyses.py::test_galton_standardized_betas       PASSED
tests/test_analyses.py::test_wage_unstandardized             PASSED
tests/test_analyses.py::test_wage_standardized_edu_vs_exp    PASSED
tests/test_analyses.py::test_wage_vif                        PASSED
tests/test_analyses.py::test_its_with_control_effect         PASSED
======================= 9 passed =======================

Tech stack

Python 3.11 · pandas · numpy · scipy · statsmodels (OLS, MixedLM, VIF, Durbin–Watson) · matplotlib · pytest. The original course solutions are in R (lm, lme4::lmer, car::vif, lmtest::dwtest); this repo re-derives the same results with the Python scientific stack.

Key ideas / what I learned

• Simpson's paradox is real and dangerous: pooling clustered data can reverse the sign of an effect; mixed-effects models (speed_M between vs. speed_E within) recover the truth.
• Standardization makes coefficients comparable across scales, and VIF diagnoses when multicollinearity makes a coefficient split meaningless.
• Regression is descriptive, not causal, unless the design earns causal language — which is why the four validity types and randomization matter.
• Quasi-experiments (segmented ITS, difference-in-differences, synthetic control) enable causal inference from observational time series when randomization is impossible — and autocorrelation is their signature threat (Durbin–Watson, Newey–West).
• The same inverse-variance / within-plus-between-variance structure recurs across mixed-effects models and random-effects meta-analysis — one idea, many guises.

Datasets & credits

Datasets are downloaded at runtime by scripts/download_data.py and are not redistributed in this repository:

• typingSpeed.csv, its_with_control.csv — from the official course repo bvasiles/empirical-methods.
• GaltonFamilies.csv — public-domain HistData dataset (Galton, 1886), via the Rdatasets mirror.
• wage2.csv — the Wooldridge WAGE2 econometrics teaching dataset, from the exact source (murraylax.org) the course notebook downloads from.

Based on the assignments and R solution notebooks of 17-803 Empirical Methods by Bogdan Vasilescu (Carnegie Mellon University). Course site: https://bvasiles.github.io/empirical-methods/. This repository is an independent educational reimplementation; all course materials, datasets, and specifications belong to their original authors. Original code in this repo is released under the MIT License.