Course Materials | Advanced Quantitative Research

# Module 01

Introduction

Learning objectives: Distinguish statistical modeling, machine learning, and artificial intelligence; compare prediction-focused and explanation-focused analysis; explain how analytical goals, assumptions, data, and evaluation criteria shape a quantitative research workflow.

Before class

Crash Course AI, “What Is Artificial Intelligence?” (about 12 min)Optional

During class

Josh Angrist, “What’s the Difference between Econometrics and Data Science?”Required

After class

# Module 02

Machine Learning for Classification

Learning objectives: Distinguish classification from regression and supervised from unsupervised learning; explain how decision trees use entropy and information gain to select splits; describe bagging and random forests; evaluate binary classifiers using a confusion matrix, accuracy, precision, recall, and F1.

Before class

ISLP, Chapter 1: “Introduction”Required
ISLP, Chapter 4: “Classification”Required
Crash Course AI, “Supervised Learning” (about 15 min)Required

During class

Koklu, Sarigil, and Ozbek (2021), “The Use of Machine Learning Methods in Classification of Pumpkin Seeds (Cucurbita pepo L.)”Required

After class

Kang, Croft, and Bichelmeyer (2021), “Predictors of Turnover Intention in U.S. Federal Government Workforce: Machine Learning Evidence That Perceived Comprehensive HR Practices Predict Turnover Intention”Required

# Module 03

Logistic Function

Learning objectives: Convert among probabilities, odds, and log-odds; explain how the logistic function maps a linear predictor to a probability; fit binary logistic regression and interpret coefficients, odds ratios, and predicted probabilities; explain maximum likelihood estimation and evaluate model predictions.

Before class

Odds and Log (Odds)Required
Logistic RegressionRequired
Harrell (2001), Regression Modeling Strategies, Ch 9. Maximum Likelihood EstimationOptional

During class

After class

# Module 04

Ordinal Logistic Regression

Learning objectives: Identify when an ordinal outcome calls for ordinal logistic regression; explain cumulative logits and the proportional-odds assumption; fit an ordinal logistic regression model and interpret its coefficients, odds ratios, and predicted probabilities; assess model assumptions and communicate results for ordered outcomes.

Before class

After class

“Using Social Network Analysis to Predict Early Collaboration Within Health Advocacy Coalitions”Required

# Module 05

Workshop 1: Proposal/Pitch

Learning objectives: Clarify research questions, assess project feasibility, and offer constructive feedback to prioritize proposal revisions.

Before class

Workshop 1: Proposal/Pitch preparation and discussion guidanceRequired

After class

Ellison et al. (2021), “Peace through superior firepower: Belief in supernatural evil and attitudes toward gun policy in the United States”Required

# Module 06

Multinomial and Count Models

Learning objectives: Distinguish multinomial logistic regression from binary and ordinal models; fit a multinomial logistic regression model using an appropriate reference category; interpret coefficients, relative risk ratios, and predicted probabilities for nominal outcomes.

Before class

After class

Casady, Petersen, and Brogaard (2023), “Public Procurement Failure: The Role of Transaction Costs and Government Capacity in Procurement Cancellations”Required

# Module 07

Text Classification I: Encoder Models

Learning objectives: Distinguish binary, multiclass, and multilabel classification; compare task-specific and embedding-based classifiers; evaluate predictions using suitable metrics and error analysis.

Before class

During class

Draft a sentiment codebook, label a small set of reviews, and compare disagreements.
Hugging Face, “Working with pipelines”: run the sentiment-analysis example and compare predictions with human labels. (opens in a new tab)
Compare task-specific and embedding-based classifiers using a confusion matrix, precision, recall, and F1.

After class

Write a short results memo comparing the two approaches and discussing misclassified examples.Required
Companion notebook: Text Classification (opens in a new tab)Optional

# Module 08

Text Classification II: Zero-Shot and Few-Shot Models

Learning objectives: Design a codebook for single-label or multilabel classification; compare zero-shot and few-shot methods; evaluate both on the same human-labeled test set.

Before class

Hugging Face, “Zero-shot classification” (opens in a new tab)Required
Prepare a codebook and examples for one classification task; bring your Module 7 results memo.Required
IBM Technology, “What is Zero-Shot Learning?” (9 min) (opens in a new tab)Optional

During class

Apply a zero-shot classifier and compare broad category names with descriptive labels.
Build a few-shot prompt using the codebook and human-labeled examples.
Evaluate both methods on the same held-out texts and inspect recurring errors.

After class

Write a short memo comparing the methods and recommending where human review is needed.Required
Companion notebook: Text Classification (opens in a new tab)Optional

# Module 09

Machine Learning for Regression

Learning objectives: To be added.

Before class

“A Gentle Introduction to Machine Learning”Required
“Machine Learning Fundamentals: Bias and Variance”Required
“Underfitting and Overfitting Explained”Optional

After class

Rachunok, Verma, and Fletcher (2024), “Predicting and Understanding Residential Water Use with Interpretable Machine Learning”Required

# Module 10

Workshop 2: Data & Methods

Learning objectives: To be added.

Before class

# Module 11

Factor Analysis and PCA

Learning objectives: To be added.

Before class

# Module 12

Structural Equation Modeling

Learning objectives: To be added.