Statistical Model Selection
Statistical Model Selection
Description
Through multi-stage Q&A (research question ontology → data structure → identification strategy → distribution and heterogeneity → inference method → missing data → advanced diagnostics), this skill helps users select the appropriate statistical/econometric model for their data and research questions. The output includes the main model, alternative models, identification assumptions, robustness check menu, inference plan, reviewer challenge anticipation, software code, and transparency statement. Trigger scenarios: users ask 'What model should I use?', 'Help me choose a statistical method', 'How should I analyze this?', 'OLS or Logit?', 'How to model panel data?', 'DID or IV?', 'Can my data support causal inference?', 'How will reviewers challenge my model?', etc.
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Quantitative Topic Selection
Turn a vague research interest into a testable, feasible quantitative paper topic tailored to your target journal. Whether you plan to submit to a CSSCI or SSCI journal, or are comparing cross-sectional and longitudinal research, this Skill helps clarify your research direction, population, and scope while building a clear variable framework around independent, dependent, mediating, and moderating variables. As your topic takes shape, you can develop research questions, a theoretical framework, formal research hypotheses, measurement instruments, sampling and data collection plans, and statistical analysis approaches aligned with your hypotheses. If you already have a policy project or research keywords, you can use them to identify directions with academic publication potential. If you have not yet settled on a specific angle, you can generate and compare multiple candidate topics. The final output focuses on topic feasibility and publication potential: whether the data can be obtained, whether the variables can be operationalized, whether the theory, hypotheses, and methods form a coherent chain, whether the target journal is a good fit, and where the topic can occupy a distinct niche relative to existing research. You will receive a complete topic finalization plan covering the research direction, variable relationships, theoretical hypotheses, cross-sectional or longitudinal design, data and analysis plan, risk considerations, and feasibility recommendations—ready to share with your advisor or use as the basis for a literature review.
SEM Learning Coach
Do you want to master Structural Equation Modeling (SEM) and its application in research? This learning coach will help you fully understand SEM from theory to practice through real-case-driven teaching. We focus on core topics such as path analysis, confirmatory factor analysis (CFA), full model building, model fit diagnostics, and measurement invariance, ensuring you can seamlessly apply your knowledge to real research. This coach emphasizes multi-tool comparative teaching. You will simultaneously learn the operation and implementation of three mainstream SEM software: R (lavaan), AMOS, and Mplus, and can focus on specific ones based on your needs. Each case is derived from real research scenarios, covering scale validation, behavioral models, clinical outcomes, and cross-cultural adaptation, allowing you to grasp 'why to do this' and 'how to interpret results' in context, rather than just staying at the operational level. From model specification to estimation, to fit evaluation and modification suggestions, this coach guides you through a complete diagnostic loop, cultivating rigorous academic reporting standards. Whether you aim to validate scale structures, explore complex causal relationships, or conduct multi-group comparisons, you will receive systematic guidance and practice opportunities. Through interactive conversations and Markdown file output at key milestones, you will progressively build solid SEM analysis skills and ultimately complete a full SEM research project independently.
STEM PhD Topic Diagnostician
Already have a PhD dissertation topic but unsure whether it is rigorous, innovative, and feasible? This Skill is designed to diagnose existing topics—not randomly generate topics from scratch or merely polish the wording. It breaks down the research subject, core variables, mechanisms, outcome measures, and research boundaries in your topic. Based on the Chinese and English literature, experimental foundation, samples, methods, equipment, timeline, and budget you provide, it assesses the topic’s rigor, novelty, and feasibility, while identifying risks such as “innovation” based only on changing materials, confounded variables, insufficient evidence for the proposed mechanism, an uncontrolled sample matrix, and chapters that cannot build progressively. The final output is a STEM PhD Topic Health Check Report, which clearly recommends whether to: retain the topic with minor adjustments; retain the research subject while strengthening its dimensions; retain the research problem and reconstruct the subject; or stop patching and redesign the topic. It is intended for STEM PhD students in food science, chemistry, materials science, biology, basic medical sciences, environmental science, agriculture, energy, and engineering. It is especially useful for those preparing a proposal defense, revising a topic at an advisor’s request, who have already defended their proposal but lack a clear central thread, or who have existing experiments but are unsure whether they can support an entire PhD dissertation.
