Advanced Quantitative Toolkit
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Logistic regression and discriminant analysis
Step-by-step guides for discriminant analysis and logistic regression procedures.
Machine Learning for Social Science: An Agnostic Approach
Outlines how social scientists can apply ML methods for causal inference and data analysis.
Method article for Bayesian multilevel modelling
This article offers a step-by-step guide on how to conduct a multilevel analysis, with an accompanying R script and dataset that make it possible to practice the concepts outlined.
Model-Based Recursive Partitioning in the Social Sciences
Introduces tree-based partitioning and key machine-learning terminology for social research.
Multidimensional Item Response Theory Diagnostics and Evaluation
Discusses how to assess and choose optimal MIRT models using diagnostics and evaluation criteria.
Multiple Correspondence Analysis - Introduction
Nontechnical introduction linking geometric data representations to statistical interpretation.
Naive Bayes, Text Classification, and Sentiment
Conceptual and practical overview of sentiment analysis and classification using Naive Bayes.
Natural language processing for the social sciences and humanities
Explores how NLP connects computational tools with social-scientific questions.
Panel Data
Explains how to estimate causal effects in longitudinal data using OLS and fixed-effects models.
Panel Data Models
Learn how to choose the best model for your panel data with this thorough introduction to panel data modelling.
Paper Comparing PCA and Factor Analysis
Clarifies the differences between PCA (descriptive) and factor analysis (modelling) and when to use each for robust results.
Parametric and nonparametric: demystifying the terms
Provides a clear explanation of when to use parametric vs. nonparametric methods for inference.
Principal Component Analysis Tutorial in R
Step-by-step guide to performing PCA in R for beginners.
Principal Components Analysis
Comprehensive overview of PCA with applied examples such as economic and crime data.
Quantitative content analysis and the measurement of collective identity
Introduces content analysis and its use in studying social identity formation.
Questionnaire data preparation in R
R is a powerful and efficient tool for survey question analysis—this tutorial explains how to harness it for your own research.
Regression discontinuity
Describes how regression discontinuity estimates causal effects in continuous variable designs.
Regression discontinuity designs
Explains how to minimize selection bias when estimating treatment effects using quasi-randomized thresholds.
Secondary analysis of national survey datasets
Discusses common biases and methodological issues in analyzing large-scale survey data.
Secondary data analysis: a method of which the time has come
Provides a step-by-step guide for conducting secondary data analysis in research.
