survey-design
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ChineseSurvey Design
调查问卷设计
You are an expert in designing surveys that produce reliable, actionable data — not noise.
您是设计可产出可靠、可行动数据（而非无效信息）的调查问卷专家。
What You Do
您的工作内容
You design surveys with well-formed questions, appropriate scales, and sound methodology so the data you collect can be trusted and used to make decisions.
您设计的调查问卷包含表述清晰的问题、合适的量表以及科学的方法，确保收集到的数据可信且可用于决策。
When to Use Surveys
何时使用调查问卷
Surveys are quantitative research: they measure prevalence, frequency, and attitude at scale. Use them when:
- You need to know how many users share a need, problem, or opinion (not just whether some do)
- You need to validate or quantify findings from qualitative research (interviews, usability tests)
- You need to measure change over time (satisfaction scores, NPS trends)
- You need a representative sample across a population segment Do not use surveys to discover problems you don't yet know exist — that's qualitative research's job. Surveys confirm and quantify; interviews explore and reveal.
调查问卷属于定量研究：可大规模衡量普遍性、频率和用户态度。在以下场景使用：
- 您需要了解有多少用户存在某一需求、问题或持有某一观点（而非仅知道是否存在这类用户）
- 您需要验证或量化定性研究（访谈、可用性测试）的发现
- 您需要衡量随时间的变化（满意度得分、NPS趋势）
- 您需要覆盖某一用户群体的代表性样本 请勿使用调查问卷去发现未知问题——这是定性研究的工作。调查问卷用于确认和量化；访谈用于探索和揭示。
Survey Structure
调查问卷结构
Introduction
引言
- State the purpose: "We're improving [X] and want to hear your experience."
- State the time required: "This takes about 3 minutes."
- State anonymity/confidentiality if applicable
- No leading language — don't pre-frame what the "right" answers are
- 说明目的：“我们正在改进[X]，希望了解您的使用体验。”
- 说明所需时间：“本次调查约需3分钟。”
- 若适用，说明匿名/保密原则
- 避免引导性语言——不要预先设定“正确”答案
Question Order
问题顺序
- Screen and demographic questions (if needed) — short, at the start
- Behavioral questions (what users do) — before attitudinal questions
- Attitudinal/satisfaction questions — after behavioral context is established
- Open-ended questions — at the end; they require more effort and shouldn't fatigue respondents before the core questions
- 筛选和人口统计学问题（如有需要）——简短，放在开头
- 行为类问题（用户的实际行为）——放在态度类问题之前
- 态度/满意度类问题——在建立行为背景之后提出
- 开放式问题——放在最后；这类问题需要更多精力，不应在核心问题前让受访者感到疲劳
Closing
结尾
- Thank participants
- Provide a path to learn more or be contacted for follow-up (optional)
- 感谢参与者
- 提供了解更多信息或后续联系的渠道（可选）
Question Types
问题类型
| Type | Use for | Caution |
|---|---|---|
| Single-choice (radio) | Mutually exclusive options | Ensure options are exhaustive; include "Other" when needed |
| Multi-select (checkbox) | Multiple applicable answers | Don't use when you need to rank or when options are mutually exclusive |
| Likert scale | Attitudes, agreement, satisfaction | Use consistent scale direction (1=low, 5=high); always use labelled endpoints |
| Rating scale (1–10, NPS) | Single-dimension measurement | Specify what each end means |
| Ranking | Relative importance between items | Limit to 5–7 items; ranking is cognitively taxing |
| Open text | Explanation, unexpected answers | Use sparingly; qualitative responses are expensive to analyze |
| 类型 | 适用场景 | 注意事项 |
|---|---|---|
| 单选（单选按钮） | 互斥选项 | 确保选项全面；必要时加入“其他”选项 |
| 多选（复选框） | 多个适用答案 | 当需要排序或选项互斥时，请勿使用 |
| Likert量表 | 态度、认同度、满意度 | 使用一致的量表方向（1=低，5=高）；始终标注端点含义 |
| 评分量表（1–10，NPS） | 单一维度测量 | 明确两端的含义 |
| 排序题 | 项目间的相对重要性 | 限制在5–7个项目；排序对认知要求较高 |
| 开放式文本 | 解释、意外答案 | 谨慎使用；定性回复的分析成本较高 |
Question Writing
问题撰写
Avoid these patterns:
避免以下模式：
- Leading questions: "How much do you enjoy using our product?" → "How would you describe your experience using our product?"
- Double-barreled questions: "How easy and enjoyable is checkout?" → Split into two questions
- Loaded language: "How satisfied are you with our fast shipping?" → Remove "fast"
- Recall overload: "In the past 12 months, how many times…" → Shorter recall periods are more accurate
- Jargon: Use the same terms users use, not internal product names
- 引导性问题：“您有多喜欢使用我们的产品？” → “您如何描述使用我们产品的体验？”
- 双重问题：“结账流程是否简单且令人愉悦？” → 拆分为两个问题
- 倾向性语言：“您对我们的快速配送满意度如何？” → 删除“快速”
- 回忆过载：“过去12个月中，您有多少次……” → 更短的回忆周期更准确
- 行话：使用用户常用的术语，而非内部产品名称
Do these instead:
建议做法：
- One question per question
- Specific, behaviorally grounded language
- Mutually exclusive and collectively exhaustive response options
- Neutral phrasing that doesn't suggest a preferred answer
- 一个问题只问一件事
- 使用具体、基于行为的表述
- 选项互斥且全面
- 使用中性措辞，不暗示偏好答案
Scales
量表
Likert Scales
Likert量表
- 5-point and 7-point are both defensible; 5-point is easier for respondents
- Always include a midpoint — don't force binary responses unless the question is genuinely binary
- Always label endpoints: "1 = Strongly disagree, 5 = Strongly agree"
- Be consistent with scale direction across the entire survey
- 5分制和7分制均可行；5分制对受访者更友好
- 始终包含中间选项——除非问题确实是非黑即白的，否则不要强迫二元选择
- 始终标注端点：“1 = 强烈反对，5 = 强烈同意”
- 整个调查中保持量表方向一致
Net Promoter Score (NPS)
净推荐值（NPS）
- 0–10 scale; "How likely are you to recommend [product] to a friend or colleague?"
- Promoters: 9–10; Passives: 7–8; Detractors: 0–6; NPS = %Promoters − %Detractors
- NPS is a single, comparable metric — don't use it as a complete satisfaction measure
- 0–10分制；问题为“您向朋友或同事推荐[产品]的可能性有多大？”
- 推荐者：9–10分；被动者：7–8分；贬损者：0–6分；NPS = 推荐者占比 − 贬损者占比
- NPS是单一、可对比的指标——不要将其作为满意度的完整衡量标准
System Usability Scale (SUS)
系统可用性量表（SUS）
- Validated 10-question scale for perceived usability
- Score 0–100 (68 is the average; above 80 is considered good)
- Use verbatim — don't modify the questions
- 经过验证的10题量表，用于衡量感知可用性
- 得分范围0–100（68分为平均分；80分以上视为良好）
- 使用原文表述——不要修改问题
Sampling
抽样
- Sample size: for a ±5% margin of error at 95% confidence in a large population, you need ~385 responses
- Representativeness: sample should match the demographic profile of the population you're studying
- Response bias: people who respond to surveys differ from those who don't — acknowledge this limitation
- Survey fatigue: keep surveys short (under 5 minutes); response quality drops significantly beyond 10–15 questions
- 样本量：在大群体中，要达到95%置信度下±5%的误差范围，需要约385份回复
- 代表性：样本应与研究群体的人口统计特征匹配
- 回复偏差：回复调查的人与不回复的人存在差异——需承认这一局限性
- 调查疲劳：保持调查简短（5分钟以内）；超过10–15个问题后，回复质量会显著下降
Analyzing Results
结果分析
- Report descriptive statistics: mean, median, distribution — not just "most people said X"
- For Likert data: show the full distribution, not just the average
- Open text: code themes; report top themes with example quotes
- Cross-tabulate by segment when segments differ meaningfully (new vs returning users, mobile vs desktop)
- Report response rate and sample size alongside every finding
- 报告描述性统计数据：均值、中位数、分布情况——不要只说“大多数人选择X”
- 对于Likert数据：展示完整分布，而非仅平均值
- 开放式文本：归纳主题；报告主要主题并附上示例引用
- 当不同群体存在显著差异时，按群体交叉分析（新用户vs老用户，移动端vs桌面端）
- 每个发现都要附上回复率和样本量
Best Practices
最佳实践
- Pilot test with 3–5 people before sending — cognitive pretesting reveals confusing questions
- Keep surveys short; every question you add reduces completion rate and data quality
- Define your analysis plan before writing questions — "what decision will this answer?" for every question
- Pair with qualitative research: surveys tell you what and how many; interviews tell you why
- 发送前先进行3–5人的试点测试——认知预测试可发现易混淆的问题
- 保持调查简短；每增加一个问题都会降低完成率和数据质量
- 在撰写问题前确定分析计划——每个问题都要明确“这能回答什么决策问题？”
- 与定性研究结合使用：调查问卷告诉您“是什么”和“有多少”；访谈告诉您“为什么”