Introduction:

Political science relies heavily on data from surveys, elections, and policy records. However, these sources often contain non-random measurement error, which occurs when variables are measured inaccurately in a way systematically tied to other factors.

The Problem:

This type of error can seriously bias findings if not addressed properly. For example:

Mis-measurement of public opinion*

Inaccurate voting patterns*

Potential gaps in policy records*

We demonstrate how these errors could affect recent research.

The Solution:

We introduce a straightforward sensitivity analysis approach, designed to tackle this challenge. This method:

Applies universally across all data types and statistical models*

Is easy for researchers to implement regularly*

Communicates findings clearly, assessing inference robustness*

How It Works:

Our sensitivity framework helps political scientists evaluate how much their conclusions depend on the accuracy of measurements. We apply this technique effectively in two case studies.

Why This Matters:

This practical tool empowers researchers to take a more rigorous approach to their findings, ensuring greater reliability and credibility.