r-analyst

To run a difference-in-differences analysis in R for panel data, this workflow guides you through phased steps from data preparation to TWFE specification, robustness checks, and publication-ready reporting.

The best way to design a sociology causal inference workflow in R is using a phased approach covering research design, data preparation, specification, estimation, and reporting to ensure reproducible sociological analysis.

Yes, you can use synthetic control and instrumental variable methods in R. This workflow supports both techniques alongside matching and regression discontinuity within a unified phase-based sociological research framework.

This R workflow supports event studies and nonlinear models, providing estimation guidance and robustness checks to validate causal impacts across cross-sectional and panel datasets.

To create publication-ready outputs for sociological research in R, follow the structured reporting phase which synthesizes estimation results, robustness checks, and specifications into reproducible deliverables.

Use matching versus regression discontinuity for causal impact estimation based on your data structure; this workflow provides specification guidance for both methods to ensure rigorous sociological research design.