Skill UI

A comprehensive framework for conducting rigorous empirical data analysis required for top-tier academic journals. It guides users through building transparent archival samples, specifying appropriate panel data estimators (like firm/year fixed effects), ensuring correct standard error clustering, and executing sophisticated identification strategies (e.g., Difference-in-Differences, Instrumental Variables). Essential for addressing endogeneity and passing deep reviewer scrutiny.

This guide helps structure the conceptual framework (Theory of Change or Logic Model) for policy research. It moves beyond simple correlation by explicitly mapping the causal chain: identifying the policy lever, the underlying mechanism, and the predicted outcome. It is crucial for establishing external validity and defining scope conditions so that empirical findings can be generalized and actionable by policymakers in different settings.

A comprehensive guide for structuring a multi-study manuscript, particularly for high-tier journals. This skill focuses on building a coherent set of related studies—rather than a single experiment—to robustly test a major theory. It guides users through sequencing studies, determining necessary power, selecting appropriate designs (longitudinal, dyadic, experimental), and implementing preregistration to ensure convergence and internal validity.

This guide outlines the rigorous data and transparency requirements for submitting academic manuscripts, particularly for social science journals. It details two core components: methodological disclosure via 'Appendix A: Disclosure Elements' and building a complete replication package. Authors must ensure all published tables and figures can be reproduced exactly using deposited data and code in a permanent archive (Dataverse) before the paper advances to typesetting.