Skill UI

A comprehensive guide for building robust causal identification strategies in empirical research, particularly for analyzing policy shocks. It provides best practices and checklist items for advanced econometrics methods like Difference-in-Differences (DID), Instrumental Variables (IV), Regression Discontinuity (RDD), and Double Machine Learning (DML), helping researchers avoid common methodological pitfalls in top-tier academic publications.

This guide outlines the scope, theoretical expectations, and methodological requirements for submitting high-quality academic research to the Nankai Business Review. It targets studies in strategic management, organizational behavior (OB), HRM, and marketing that build management theory grounded in the Chinese context, requiring strong theoretical contribution and rigorous empirical or qualitative methods.

A comprehensive guide for structuring the analysis and results section of academic papers in Production and Operations Management (POM). It covers rigorous theoretical proofs, advanced empirical identification methods (e.g., DiD, IV), and data science validation techniques. The core focus is bridging statistical significance with actionable, operational managerial magnitude, ensuring research findings are decision-relevant.

This guide assists researchers in selecting the most appropriate and rigorous methodology for Production and Operations Management (POM) manuscripts. It covers various research tracks, including analytical modeling, stochastic process theory, game theory, causal empirical identification (DiD, IV, RD), behavioral experiments, simulation, and operations data science (ML/forecasting). It ensures the research method is tightly linked to the core operational question and possesses significant practical value.

This comprehensive guide helps authors structure the inferential backbone of economic papers for top journals. It provides detailed checklists for theoretical models (assumptions, regularity), empirical studies (causal design, identification), and computational methods (convergence, asymptotics), ensuring claims are robust and credible across different paper types.

This guide provides a comprehensive strategy for elevating the credibility of finance manuscripts to the standard of top-tier journals. It details rigorous methods for empirical causal identification (DID, IV, RDD, natural experiments) and ensures theoretical rigor by assessing assumptions, generalizability, and proof exposition. It helps researchers stress-test their work's core logic.

A comprehensive guide for addressing endogeneity, selection bias, and reverse causality in empirical research, specifically tailored for top-tier journals. It details advanced econometric methods (DID, IV, PSM, Heckman) and outlines best practices for conducting rigorous robustness checks, ensuring that identified effects are genuinely causal, not merely correlated.

This comprehensive guide provides methodological frameworks for designing rigorous empirical studies in strategic management. It covers defining the sample, selecting the unit of analysis, validating key measures (performance and strategic choices), and critically, implementing robust identification designs (e.g., DID, IV, Fixed Effects) to address endogeneity and establish causal claims.

This guide provides a comprehensive framework for structuring and validating the theoretical derivation of statistical methods for academic publishing, particularly in social sciences. It ensures that every claim—including assumptions, consistency, efficiency, and bias—is explicitly stated, traceable to supporting conditions, and linked to empirical or simulation checks, thus maintaining methodological rigor.

This guide details how to construct the critical real-data section of a sociological methods paper. It emphasizes that the illustration must demonstrate that the proposed method substantively changes a conclusion compared to the standard/incumbent approach, thereby proving the method's necessity and theoretical stake.

A comprehensive guide for authors submitting to Sociological Methods & Research (SMR). It routes authors through the rigorous, multi-stage process required for publication, covering methodological contribution, derivation, simulation studies, empirical illustration, and ensuring full software reproducibility. This tool helps authors structure their manuscript to meet high academic standards.

This guide provides rigorous guidelines for integrating various forms of empirical evidence—including quantitative data, qualitative case studies, and historical comparisons—into advanced social science manuscripts. It emphasizes that evidence must actively support or test the core proposition, requiring authors to translate raw findings into conceptual insights and mechanisms, rather than simply presenting methods or data.