Correct Measurement-Error Bias
In projoint: Conjoint Analysis with Reliability Correction and Visualization

Why Correct for Measurement Error?

In conjoint experiments, measurement error is pervasive but often overlooked.
Respondents may:
Misread attributes
Misunderstand levels
Click randomly
Forget information across tasks
As a result, respondents' recorded choices often refelect random noise.
This random error leads to attenuate true prefernces (or true effects).

The Consequences of Ignoring Measurement Error

| Without Correction | With Correction | |:--------------------------------|:----------------------------------| | Underestimates true preferences | Provides more accurate preferences | | Falsely suggests indifference | Recovers meaningful trade-offs | | Misleads theory building and application | Provides accurate, unbiased insights |

How projoint Corrects for Measurement Error

Estimates the intra-respondent reliability (IRR) based on responses to a repeated task
Adjusts marginal means (MMs) and average marginal component effects (AMCEs) accordingly
Provides corrected estimates that better reflect respondents’ true preferences
Corrected estimates reveal the true magnitude of effects, improving both theoretical and applied inferences in political science, marketing, and other fields.

✅ No additional respondent burden (just one repeated task)
✅ Minimal survey design changes
✅ Massive improvements in accuracy

Key Takeaway

🧠 Measurement error systematically biases results.

🔥 Correcting for measurement error reveals true preferences, sharper trade-offs, and prevents misleading inferences.

📚 Key Reference

Clayton, Horiuchi, Kaufman, King, Komisarchik (Forthcoming).
"Correcting Measurement Error Bias in Conjoint Survey Experiments."
Forthcoming, American Journal of Political Science.
Pre-Print Available