# Correct Measurement-Error Bias

## 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