Skill UI

This comprehensive guide assists in developing rigorous research designs for family science. It covers quantitative methodologies (longitudinal, dyadic, life-course, event history), qualitative approaches, and mixed-methods integration. The focus is on strengthening the design's defensibility by explicitly addressing units of analysis, interdependencies, and critical statistical challenges like selection bias and non-independence.

Provides comprehensive guidelines for creating self-contained, publication-ready tables and figures for academic manuscripts, specifically adhering to the Journal of Marriage and Family (JMF) style. It covers modified APA conventions, displaying effect sizes and confidence intervals, ensuring accessibility (grayscale/colorblind), and managing strict page budgets by distinguishing between main text and supplementary material.

A comprehensive guide detailing the data sharing and transparency requirements for submitting manuscripts to the Journal of Marriage and Family (JMF). It specifically addresses the challenge of restricted-use family datasets, teaching researchers how to document access paths and ensure replication-level detail without direct data redistribution.

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 comprehensive guide assists researchers in structuring and refining empirical studies for submission to top management journals. It addresses critical methodological pitfalls, including common-method bias, endogeneity, and multilevel structures. Learn how to correctly match theoretical claims (e.g., causal effects, temporal processes) to appropriate designs (experiments, panel data, meta-analysis) to ensure rigorous scientific validity and statistical integrity.

A comprehensive guide for generating rigorous, APA-compliant tables and figures for top-tier academic journals (e.g., JOM). Covers the creation of crucial exhibits, including descriptive statistics, CFA, SEM/HLM results, interaction plots, and meta-analysis forest plots. It ensures findings are presented clearly, adhere to strict methodological standards, and fit within tight page limits.

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.

A comprehensive guide for researchers conducting advanced data analysis and reporting for top marketing journals. It details selecting the appropriate econometric estimator (e.g., ANOVA, DiD, IV, mediation models) based on the study design (experimental, observational, panel). It emphasizes meeting rigorous journal mandates, including exact p-values, standard errors, and effect sizes, and linking statistical findings to practical managerial claims.

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.

This guide provides comprehensive standards for defending rigorous data analysis in academic submissions, particularly for the Journal of Management Studies (JMS). It details specific requirements for both quantitative work (e.g., robustness checks, SEM, endogeneity) and qualitative work (e.g., coding transparency, abductive logic, trustworthiness), ensuring the analysis credibly supports the theoretical claims.

A comprehensive guide for structuring rigorous academic research in management studies. It helps researchers align theoretical claims (causal, exploratory, mechanism testing) with appropriate methods—including qualitative, quantitative, and multi-method designs. The guide covers advanced topics such as addressing common-method bias, endogeneity, and cross-level analysis, ensuring methodological rigor for top-tier publications.

A comprehensive guide for authors submitting to academic journals, detailing how to structure and build necessary exhibits (tables and figures). It provides distinct guidelines for quantitative work (e.g., SEM results, interaction plots) and qualitative work (e.g., data-structure figures, representative quotes), ensuring all visuals make the argument legible and adhere to strict house style rules.