問卷設計 (Survey Design)

Overview

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.

When to Use

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

When NOT to Use

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

Assumptions

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

Methodology

Step 1 — Construct Operationalization

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).

Step 2 — Scale Design and Pretesting

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.

Step 3 — Assess Reliability and Validity

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.

Step 4 — Control for Common Method Variance

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.

Output Format

## Survey Design: [Study Title]

### Construct Operationalization
| Construct | Dimensions | Items | Source |
|-----------|-----------|-------|--------|
| [name] | [dim] | x items | [adapted from] |

### Reliability Assessment
| Construct | Items | Cronbach's α | CR | AVE |
|-----------|-------|-------------|-----|-----|
| [name] | x | x.xx | x.xx | x.xx |

### Validity Assessment
| 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] |

### CMV Controls
| Remedy | Type | Result |
|--------|------|--------|
| [remedy] | [procedural/statistical] | [finding] |

### Limitations
- [Note any assumption violations]

Gotchas

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

References

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.

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