Skill UI

This guide provides best practices for creating clear and legible computational exhibits for academic manuscripts, particularly for INFORMS journals. It covers advanced techniques like performance profiling, visualizing scaling behavior on log axes, and presenting stochastic results with full dispersion. The goal is to ensure that the presented figures and tables strongly support the computational claims while maintaining rigorous methodological standards and reproducibility.

This framework provides a comprehensive methodology for designing, auditing, and reporting empirical measurements about large-scale systems like the Internet. It guides users to ensure measurements are scientifically rigorous, by matching claims to evidence, quantifying coverage bias, designing for temporal changes, and adhering to strict ethical guidelines and data provenance standards.

This guide outlines best practices for ensuring the scientific reproducibility and artifact availability of network measurement research. It distinguishes between analysis reproducibility (recreating figures from released data) and method reproducibility (re-running the measurement). It mandates thorough documentation of provenance, including vantage points, timing, and tool versions, ensuring research claims are verifiable and robust.

A comprehensive framework for strengthening International Relations (IR) manuscripts, particularly for top-tier journals. It guides researchers through rigorous design requirements, including causal identification in dyadic/network data, structured case selection and process tracing, experimental planning (surveys/conjoint), and formal-empirical linkages. The focus is on ruling out rival explanations and establishing robust empirical claims.

A comprehensive methodological guide for researchers writing Information Systems Research (ISR) papers. It details how to establish rigorous evidence across empirical (DiD, IV, RDD), analytical (proofs, comparative statics), and design-science claims. Learn to structure your analysis, handle common-method bias, and ensure full reproducibility using modern econometric techniques.

A comprehensive guide for finalizing the core evidence—tables and figures—for high-stakes empirical accounting or economics papers (e.g., Journal of Accounting Research). It outlines the mandatory 'exhibit set,' covering sample construction, descriptive statistics, correlation matrices, main regression results, and crucial identification plots (DiD, RD). Focuses on adhering to rigorous house styles and ensuring all claims are data-supported.

A comprehensive guide for designing and presenting rigorous experiments for top-tier computer science journals. It covers best practices for ensuring reproducibility, matching evidence to claims, selecting appropriate baselines, performing statistical significance testing, and handling modern challenges like LLM contamination and system scalability measurements. Essential for passing double-blind peer review.

This comprehensive guide establishes the rigorous methodological standards for developing credible causal identification strategies in development economics. It details best practices for modern quasi-experimental methods, including RCTs, DID, IV, and RDD. Authors learn how to meet the high empirical norms required by top-tier journals by ensuring robustness, addressing biases (e.g., TWFE), and validating external validity.

A comprehensive guide for strengthening causal claims in corporate and empirical finance research. This resource details advanced econometric methods—including IV, DiD, RDD, and natural experiments—and provides strategies to preempt common methodological attacks (e.g., endogeneity, parallel-trends violations) required by top-tier academic journals like the Journal of Finance.

A comprehensive guide for handling endogeneity, selection bias, and causal inference in empirical finance research, particularly targeting the high standards of journals like JFE. It details advanced econometric methodologies including Difference-in-Differences (DID), Regression Discontinuity Design (RDD), and Instrumental Variables (IV), ensuring your causal claims are rigorously defensible against referee scrutiny.

A comprehensive guide for rigorous causal inference in health policy research. It details advanced econometric methodologies—including Difference-in-Differences (DiD), Regression Discontinuity Design (RDD), and Instrumental Variables (IV)—and addresses critical issues like selection bias and institutional realism. This resource helps ensure that empirical claims meet the highest standards required by top-tier health economics journals.

A comprehensive guide for assembling reproducible research packages using restricted health data, such as Medicare claims or EHR data. This package focuses not on running the analysis, but on establishing a credible data access path, creating detailed Data Availability Statements (DAS), and ensuring code is shared and reproducible, which is crucial for publishing in health economics journals.