Skill UI

A comprehensive guide for academic researchers on integrating robust economic theory into legal studies. It details how to model legal rules—such as deterrence, liability, and bargaining—to generate testable predictions, derive optimal welfare outcomes, or advance theory contributions in the Chicago tradition. Focuses on ensuring the model names a mechanism and does not eclipse the empirical design.

A comprehensive guide for establishing rigorous identification strategies in monetary, banking, and macro-financial modeling. It details the standards required for publishing causal claims in top-tier journals (like JMCB), covering three branches: macro shock identification (SVAR), structural parameter identification, and micro-banking causal designs (e.g., staggered regulations). Learn how to defend against information effects, address endogeneity, and ensure policy relevance.

A comprehensive guide detailing the quantitative and econometric standards required for submissions to top-tier monetary and macroeconomic journals. It covers advanced techniques like VAR/SVAR, Local Projections, and DSGE modeling, focusing on proper inference, robustness checks, and diagnostic reporting to ensure scholarly rigor.

This guide helps authors frame their academic contributions within highly specialized fields like monetary policy, DSGE modeling, and financial intermediation. It teaches how to precisely define the novel element (shock, mechanism, or counterfactual) relative to the closest prior work, ensuring the manuscript is viewed as an advance, not a mere survey. Ideal for preparing submissions to top-tier economics journals.

A comprehensive guide detailing the rigorous statistical and methodological norms required for publishing family research, specifically tailored for journals like JMF. It covers handling complex survey weights, non-independence in dyads and families, missing data imputation, attrition modeling, and robust reporting of uncertainty and selection effects.

A comprehensive guide for conducting rigorous, publication-quality statistical analyses required by top-tier management journals. It covers advanced techniques including Structural Equation Modeling (SEM)/CFA, Hierarchical Linear Modeling (HLM), complex regression, and meta-analysis, ensuring reports include necessary effect sizes, confidence intervals, and robustness checks to meet demanding academic standards.

This guide provides a rigorous methodological checklist for conducting data analysis suitable for top-tier academic journals, such as JMIS. It covers advanced techniques across econometrics (addressing identification and endogeneity), behavioral modeling (SEM/PLS, common-method bias), and ML/analytics, ensuring that research findings meet the highest standards of robustness and theoretical grounding.

This guide assists researchers in aligning their study design—whether behavioral (lab/field experiments) or modeling-econometric (IV/DiD/RDD)—with the rigorous standards required by the Journal of Marketing Research (JMR). It covers best practices for causal identification, power analysis, structural estimation, and meeting journal-level expectations like replication and Web Appendix planning.

A guide for integrating behavioral theory into rigorous empirical studies. It outlines the necessary steps to move beyond descriptive findings of irrationality by constructing a statistically identifiable behavioral model (e.g., incorporating loss aversion parameters) and testing it against rational baselines using controlled, incentive-compatible experimental designs. Essential for submitting papers requiring model explanation of behavioral biases.

This skill provides a rigorous framework for designing academic studies, particularly for high-impact psychological journals. It guides researchers to proactively address common methodological pitfalls such as common-method variance (CMV), unmodeled nesting, and construct validity gaps. Topics covered include temporal separation, multi-level modeling, advanced causal inference techniques (DiD, IV), and pre-registration best practices, strengthening the plan before data collection.

A comprehensive guide for presenting complex statistical evidence in academic manuscripts, specifically tailored for journals like JAP. It covers APA 7th-edition standards for creating self-contained exhibits, including correlation matrices, CFA/SEM path diagrams, multilevel modeling (HLM) results, and meta-analytic plots. Emphasis is placed on reporting effect sizes, confidence intervals, and reliabilities.

This guide provides a framework for authors to strategically position their empirical research within the ongoing academic debates of Operations and Supply Chain Management (OM/SCM). It teaches authors to move beyond merely identifying knowledge gaps by problematizing shared assumptions, explicitly distinguishing empirical observation from analytical modeling, and aligning the contribution with the target department's specific mission.