Skill UI

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.

This guide ensures that the chosen research methodology genres (e.g., behavioral empirical, analytical modeling, design science) are correctly aligned with the core research claim or phenomenon. It helps researchers identify the necessary design rigor, appropriate identification strategies (like DiD or IV), and the level of analysis required to ensure the study can genuinely support its contribution in the IS domain.

This skill serves as a comprehensive guide for researchers to evaluate their academic manuscripts for suitability in Issues in Agricultural Economy. It covers the journal's precise scope, preferred methodologies (e.g., DID, micro-data), submission policies (blind review, fees), and common rejection pitfalls. It provides actionable advice on structuring and refining the manuscript to meet the rigorous standards of core Chinese academic journals focusing on rural and agricultural policies.

A comprehensive guide for authors positioning theoretical computing research in top-tier venues like ITCS. It emphasizes defining the 'delta'—the genuinely new contribution—by leading comparisons with what prior work *did not* ask. The guide also covers best practices for multi-area surveying, maintaining academic honesty, and correctly handling self-citations under double-blind review conditions.

A comprehensive methodological guide detailing how to design rigorous, publishable empirical or analytical studies for top-tier accounting and economics journals (like JAE). It focuses heavily on achieving causal identification by mastering techniques such as Difference-in-Differences (DiD), Instrumental Variables (IV), Regression Discontinuity (RDD), and event studies, ensuring the study design credibly supports the economic prediction.

This guide outlines the rigorous standards for preparing empirical tables and figures for top-tier academic journals, such as the Journal of Accounting and Economics (JAE). It covers the entire research workflow, including variable definition, sample construction, descriptive statistics, correlation matrices, advanced regressions, and event studies. The goal is to ensure all exhibits are self-contained, reproducible, and meet strict academic reviewer expectations.

A comprehensive guide for researchers on sharpening the theoretical contribution of academic manuscripts, especially for specialized journals like JAIS. It teaches authors how to transition from describing 'what they did' to articulating 'what the field now knows,' ensuring the work is generative, challenging, and structurally aligned with its chosen category (Theory, Empirical, etc.).

A comprehensive guide for empirical marketing researchers on selecting appropriate advanced statistical estimators (SEM, HLM, DiD, etc.) and adhering to rigorous academic reporting standards (APA style, effect sizes, fit indices). Crucially, it guides users on translating complex statistical findings into actionable, managerially relevant insights for journals like JAMS.

This guide details the rigorous, reproducible empirical strategies required to identify causal parameters in applied econometrics, meeting high standards (e.g., JAE). It covers various designs—including DID, IV, panel data, and time-series—and mandates that every assumption and diagnostic test must be explicitly stated, tested, and reproducible using deposited code/data.

A comprehensive guide for executing and reporting the full empirical robustness battery required by top accounting journals. It covers modern econometric techniques like standard error clustering, advanced Difference-in-Differences (DiD) estimators for staggered timing, Regression Discontinuity (RD) diagnostics, and Instrumental Variables (IV/2SLS). The guide emphasizes rigorous validation of accounting constructs, executing multiple sensitivity tests, and ensuring full data-and-code reproducibility for academic publication.

A comprehensive guide for designing robust, causal econometric studies using large-sample archival data. It details how to identify valid sources of variation (e.g., DiD, RD, IV), mitigate endogeneity threats (e.g., reverse causality, selection bias), and construct reproducible data pipelines required for top-tier academic research in accounting.

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.