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問卷設計 (Survey Design)

问卷设计 (Survey Design)

Overview

概述

Survey design translates theoretical constructs into measurable items through systematic operationalization, scale development, and psychometric validation. Rigorous surveys ensure that observed scores reliably and validly represent the intended constructs while controlling for method artifacts such as common method variance.

问卷设计通过系统化的操作化、量表开发和心理测量学验证，将理论构念转化为可测量的题项。严谨的问卷设计能确保观测得分可靠且有效地代表目标构念，同时控制诸如共同方法偏差等方法性偏差。

When to Use

适用场景

Measuring perceptions, attitudes, beliefs, or behavioral intentions
Operationalizing latent constructs from a theoretical framework
Developing or adapting multi-item Likert scales
Planning a quantitative study that relies on self-report data

测量感知、态度、信念或行为意向
操作化理论框架中的潜变量构念
开发或改编多题项Likert量表
规划依赖自陈式数据的量化研究

When NOT to Use

不适用场景

Objective behavioral data or archival data are available and more appropriate
The construct is better measured through experiments or observations
Population is unreachable via survey (extremely low literacy, no sampling frame)
Research question is exploratory and constructs are not yet well-defined

可获取客观行为数据或档案数据且更为合适时
构念更适合通过实验或观察法测量时
无法通过问卷接触到研究群体（如极低识字率、无抽样框）
研究问题为探索性且构念尚未明确定义时

Assumptions

假设前提

IRON LAW: A survey measures PERCEPTIONS, not objective reality — and common
method variance inflates correlations when predictor and criterion come
from the same source.

Key assumptions:

Respondents understand items as intended (semantic equivalence)
Responses are honest and not systematically biased by social desirability
The construct domain is adequately sampled by the items
Items within a scale are reflective indicators of the same underlying construct

铁律：问卷测量的是感知，而非客观现实——当预测变量和效标变量来自同一来源时，共同方法偏差会夸大变量间的相关性。

核心假设：

受访者能按题项的预期含义理解内容（语义等价）
回答真实，不会因社会期望性产生系统性偏差
题项能充分覆盖构念的领域范围
同一量表内的题项是同一潜在构念的反映性指标

Methodology

方法流程

Step 1 — Construct Operationalization

步骤1 — 构念操作化

Define each construct's conceptual domain from theory. Specify dimensions and sub-dimensions. Generate item pool from literature, expert judgment, and qualitative input (3-5 items per dimension minimum).

从理论层面明确定义每个构念的概念范畴，确定维度与子维度。通过文献研究、专家判断和质性输入生成题项池（每个维度至少3-5个题项）。

Step 2 — Scale Design and Pretesting

步骤2 — 量表设计与预测试

Choose response format (5-point or 7-point Likert). Avoid double-barreled, leading, or ambiguous items. Conduct cognitive interviews or expert panel review. Pilot test with N ≥ 30.

选择响应格式（5点或7点Likert量表）。避免双重负载、诱导性或模糊性题项。开展认知访谈或专家评审。进行预测试，样本量N ≥ 30。

Step 3 — Assess Reliability and Validity

步骤3 — 信度与效度评估

Reliability: Cronbach's alpha ≥ 0.70, composite reliability (CR) ≥ 0.70. Convergent validity: AVE ≥ 0.50, factor loadings ≥ 0.60. Discriminant validity: Fornell-Larcker criterion or HTMT < 0.90. See

references/

for formulas.

信度：Cronbach's α ≥ 0.70，组合信度（CR）≥ 0.70。聚合效度：平均方差提取值（AVE）≥ 0.50，因子载荷≥ 0.60。区分效度：满足Fornell-Larcker准则或HTMT < 0.90。公式可参考

references/

目录。

Step 4 — Control for Common Method Variance

步骤4 — 共同方法偏差（CMV）控制

Procedural remedies: separate predictor and criterion temporally, use different scale formats, guarantee anonymity. Statistical remedies: Harman's single-factor test (necessary but not sufficient), marker variable technique, CFA with common method factor.

程序层面的补救措施：将预测变量和效标变量的测量时间分离、使用不同的量表格式、保证匿名性。统计层面的补救措施：Harman单因子检验（必要但不充分）、标记变量法、包含共同方法因子的验证性因子分析（CFA）。

Output Format

输出格式

markdown

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markdown

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Survey Design: [Study Title]

问卷设计：[研究标题]

Construct Operationalization

构念操作化

Construct	Dimensions	Items	Source
[name]	[dim]	x items	[adapted from]

构念	维度	题项数量	来源
[名称]	[维度]	x项	[改编自]

Reliability Assessment

信度评估

Construct	Items	Cronbach's α	CR	AVE
[name]	x	x.xx	x.xx	x.xx

构念	题项数量	Cronbach's α	CR	AVE
[名称]	x	x.xx	x.xx	x.xx

Validity Assessment

效度评估

Test	Result	Threshold	Assessment
Factor loadings (min)	x.xx	≥ 0.60	[pass/fail]
AVE	x.xx	≥ 0.50	[pass/fail]
HTMT (max)	x.xx	< 0.90	[pass/fail]

检验项	结果	阈值	评估结论
因子载荷（最小值）	x.xx	≥ 0.60	[通过/不通过]
AVE	x.xx	≥ 0.50	[通过/不通过]
HTMT（最大值）	x.xx	< 0.90	[通过/不通过]

CMV Controls

CMV控制措施

Remedy	Type	Result
[remedy]	[procedural/statistical]	[finding]

补救措施	类型	实施结果
[措施名称]	[程序层面/统计层面]	[发现]

Limitations

局限性

[Note any assumption violations]

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[记录任何违反假设的情况]

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Gotchas

注意事项

Cronbach's alpha is a lower bound of reliability and assumes tau-equivalence; CR is preferred
High reliability with low validity means you are precisely measuring the wrong thing
Reverse-coded items reduce acquiescence bias but often form artifactual method factors in CFA
Harman's single-factor test is widely used but has very low power to detect CMV
Translation and back-translation do not guarantee measurement invariance across cultures
Response rate below 30% raises non-response bias concerns even with adequate sample size

Cronbach's α是信度的下限，且假设tau等价性；优先使用组合信度（CR）
信度高但效度低意味着你在精准地测量错误的内容
反向编码题项能降低默认偏差，但在验证性因子分析（CFA）中常形成人为的方法因子
Harman单因子检验虽被广泛使用，但检测CMV的效力极低
翻译与回译不能保证跨文化测量的不变性
应答率低于30%时，即使样本量充足，也会引发无应答偏差的担忧

References

参考文献

DeVellis, R. F. (2017). Scale Development: Theory and Applications (4th ed.). Sage.
Podsakoff, P. M., MacKenzie, S. B., Lee, J.-Y., & Podsakoff, N. P. (2003). Common method biases in behavioral research. Journal of Applied Psychology, 88(5), 879-903.
Hair, J. F., Black, W. C., Babin, B. J., & Anderson, R. E. (2019). Multivariate Data Analysis (8th ed.). Cengage.

DeVellis, R. F. (2017). Scale Development: Theory and Applications (4th ed.). Sage.
Podsakoff, P. M., MacKenzie, S. B., Lee, J.-Y., & Podsakoff, N. P. (2003). Common method biases in behavioral research. Journal of Applied Psychology, 88(5), 879-903.
Hair, J. F., Black, W. C., Babin, B. J., & Anderson, R. E. (2019). Multivariate Data Analysis (8th ed.). Cengage.