Resources

Many scholars have generously shared their R tutorials on different topics. Their generosity has benefited my research. Here is a nonexhaustive list of these useful resources:

• R for Data Science (by Hadley Wickham, Mine Çetinkaya-Rundel, and Garrett Grolemund)
• Multilevel Modeling in R (by Sarah Schwartz and Tyson Barrett)
• Cross-National Research Using Multilevel Modeling: lme4 (by Alexandru Cernat)
• Multilevel Regression and Poststratification Case Studies (by Juan Lopez-Martin, Justin H. Phillips, and Andrew Gelman)
• Estimation of Heterogeneous Treatment Effects: interflex (by Jens Hainmueller, Jonathan Mummolo, Yiqing Xu, and Ziyi Liu)
• Estimation of Heterogeneous Treatment Effects: A Machine Learning Approach (by Susan Athey, Stefan Wager, Vitor Hadad, Sylvia Klosin, Nicolaj Mühlbach, Xinkun Nie, and Matthew Schaelling)
• Equivalence Testing for Frequentist Models (by Daniel Lüdecke, Dominique Makowski, Mattan S. Ben-Shachar, Indrajeet Patil, Søren Højsgaard, and Brenton M. Wiernik)
• Equivalence Testing for F-Tests: TOSTER (by Aaron Caldwell)
• Adjustments for Multiple Comparisons (by Alexander Coppock)
• Estimation of Minimum Detectable Effects (by Kaylyn Jackson Schiff, Daniel S. Schiff, and Natália S. Bueno)
• Implementing Counterfactual Estimators in Panel Fixed-Effect Settings: fect (by Licheng Liu, Ye Wang, Yiqing Xu, and Ziyi Liu)
• Estimating Mood from Existing Surveys (by Christopher Claassen)
• Quantitative Text Analysis in R (by Kohei Watanabe and Stefan Müller)
• Analysis of Conjoint Experiments (by Andrew Heiss)

Additional resources for research design:

• Programming Choice Experiments in Qualtrics (by Matt Graham)
• Programming Quadratic Voting in Qualtrics (by Charlotte Cavaillé)
• Research Design in the Social Sciences (by Graeme Blair, Alexander Coppock, and Macartan Humphreys)