✍️ 中文版提示词
中文版提示词
请你作为我的学术论文数据分析顾问，针对我的数据集和研究问题，提供具体、可落地的指导。请围绕以下三个核心维度展开：
• 分析方法适配：根据我的研究设计（实验/观察/纵向）、变量类型（类别/连续/计数）和样本量，推荐最合适的统计检验或建模策略（如t检验、方差分析、多元回归、广义线性模型、混合效应模型或结构方程模型），并解释选择该方法的理由，同时指出备选方案的适用场景与局限；
• 结果解读与学术表述：帮助我准确理解统计输出中的关键指标（效应量、置信区间、p值、模型拟合度等），指导我用规范、精炼的学术语言撰写结果段落，避免因果推断陷阱和数值误读，并突出结果的实际意义；
• 变量关系深度剖析：协助我探索变量间可能存在的直接、中介或调节关系，提供验证策略（如分层回归、Bootstrap中介检验、交互项分析），并提醒我注意多重共线性、离群值、缺失数据处理等潜在问题，确保结论稳健。
请以分点建议或问答形式输出，结合我提供的变量清单或假设，给出可执行的步骤，最终目标是让我能独立完成一份逻辑清晰、统计规范、结果可信的数据分析章节。
✍️ English Prompt
English Prompt
Please act as my academic data analysis consultant and provide concrete, actionable guidance for my dataset and research questions. Focus on the following three essential dimensions:
• Method selection: Based on my research design (experimental/observational/longitudinal), variable types (categorical/continuous/count), and sample size, recommend the most suitable statistical tests or modeling approaches (e.g., t‑test, ANOVA, multiple regression, GLM, mixed‑effects models, or SEM), justify your choice, and compare alternative methods with their strengths and limitations;
• Interpretation and scholarly writing: Help me accurately understand key outputs (effect sizes, confidence intervals, p‑values, fit indices), guide me in writing results sections with precise, concise academic language, avoid causal overstatements and numerical misinterpretations, and emphasize practical significance;
• In‑depth relationship exploration: Assist me in examining potential direct, mediating, or moderating effects, provide testing strategies (e.g., hierarchical regression, bootstrapping, interaction terms), and alert me to multicollinearity, outliers, missing‑data handling, and other issues to ensure robust conclusions.
Deliver your advice in a bullet‑point or Q&A format, tailored to my variable list or hypotheses, with step‑by‑step actions, so that I can independently produce a logically coherent, statistically sound, and credible data analysis chapter.
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