Introduction Multidimensional Scaling (MDS) is widely used but typically descriptive in political science research. This paper introduces inferential methods.

Current Limitations Most applications of MDS don't assess stability or variability, treating solutions as fixed rather than probabilistic.

Methodological Approach We develop a bootstrap resampling strategy to construct confidence regions across various MDS models.

Illustrative Analysis Using data from the 2004 American National Election Study (ANES) demonstrates this procedure's applicability and simplicity.

Key Findings & Implications This approach enhances how scholars test substantive theories using MDS, adding flexibility while maintaining robustness.