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Analyze Data

Explore our core topics and start using code snippets and workflows that help you integrate open science practices in your work.

Regression Analysis

Linear Regression

Run a Regression Analysis ^{3 mins} Interaction Terms ^{15 mins} Easy Standard Error Adjustment with `estimatr` ^{3 mins} Doing Calculations with Regression Coefficients Using deltaMethod ^{2 mins} Compute Marginal Effects ^{6 mins} Fixed Effects Regression Assumptions ^{4 mins} Mediation Analysis Tutorial in R ^{12 mins} Interpreting Regression Models in R ^{10 mins} Understanding Linear Regression Summary Output in R ^{8 mins}

Reporting

Evaluate Model Assumptions ^{2 mins}

Tests

Non-parametric Tests

Understanding the binomial test ^{7 mins} Chi-Squared Test ^{4 mins} Wilcoxon test ^{6 mins}

Causal Inference

Introduction

Introduction to Causal Inference ^{4 mins}

Regression Discontinuity Designs (RDD)

Validation and Falsification Analysis for RDD ^{9 mins} Fuzzy RD Designs ^{7 mins} Fuzzy RDD in Practice ^{3 mins} Sharp RD Designs ^{6 mins} Local Randomization Approach ^{9 mins} Continuity-Based Approach ^{5 mins} Regression Discontinuity Plots ^{7 mins}

Panel Data

An Introduction to Panel Data ^{11 mins} The Fixed Effects Model (Within Estimator) ^{5 mins} Fixed-Effects Estimation in R With the `fixest` Package ^{7 mins} The Pooled OLS Model ^{2 mins} The First-Difference Estimator ^{2 mins} The Between Estimator ^{1 mins} The Random Effects Model ^{6 mins}

Difference in Difference

An Introduction to Difference-in-Difference Analysis ^{6 mins} Difference-in-Difference as a Regression ^{7 mins} Impact evaluation: RD vs DID ^{4 mins} Synthetic Control for Impact Evaluation ^{4 mins} Staggered Difference-in-Difference Estimation ^{8 mins}

Instrumental Variables

Intro to IV Estimation ^{5 mins} The Shift-Share Instrumental Variable ^{6 mins}

Matching

An Introduction to (Exact) Matching ^{8 mins} Propensity Score Matching: A Method for Approximate Matching ^{8 mins}

Machine Learning and Predictive Modeling

Machine Learning 101

Introduction to Deep Learning ^{10 mins} In-depth Introduction to Machine Learning and R ^{1 mins} Get Introduced to Machine Learning ^{6 mins} In-depth Introduction to Supervised Machine Learning ^{11 mins}

Supervised Machine Learning

Extreme gradient boosting with XGBoost in R ^{12 mins} Extreme Gradient Boosting with XGBoost in Python ^{4 mins} Linear Regression for Prediction in Python ^{5 mins} Introduction to Random Forests in Python ^{9 mins} Support Vector Machines in Python ^{6 mins}

Loss Functions and Cost functions in Machine Learning