🔎 What This Asks
A test of whether conjoint analysis can elicit more honest survey responses by reducing social desirability bias (SDB) on sensitive attributes.
🔬 How the test was set up
A novel experimental comparison contrasts three designs:
- a standard, fully randomized conjoint design;
- a partially randomized design where only the sensitive attribute varies between the two profiles in each task;
- a control condition that accounts for any confounding from greater attention to the varying attribute under the partially randomized design.
📊 Where this was implemented and what was measured
- Two empirical studies: one on attitudes about environmental conservation and one on preferences for congressional candidates.
- The primary outcome is the average marginal component effect (AMCE) of the sensitive attribute.
- Key empirical result: in both studies, estimates indicate the fully randomized conjoint design could reduce SDB for the AMCE of the sensitive attribute by about two‑thirds of the AMCE itself.
⚠️ Caveats and next steps
- Findings are encouraging but exploratory. Results show sensitivity to alternative model specifications.
- Additional confirmatory evidence using the same experimental comparison is recommended before strong conclusions are drawn about generalizability.
Why it matters: the proposed design provides a direct way to evaluate whether conjoint designs mitigate SDB and offers a concrete path for future confirmatory tests in survey research.
