Beliefs influence decision-making in various domains, yet accurately measuring them remains challenging. This study compares four belief elicitation methods: introspective best-guess, tokens allocation, probability equivalents, and exchangeability. Unlike traditional introspective methods, experimental approaches aim to mitigate biases linked to uncertainty preferences. Using a hierarchical Bayesian model, we assess the consistency of these methods and their ability to reveal latent beliefs. Our findings suggest that choice-based methods provide stable belief estimates, while introspective measures exhibit context-dependent reliability. Additionally, Bayesian estimates reveal stronger correlations across methods than naive approaches, underscoring the need for measurement bias corrections. These insights contribute to refining belief elicitation techniques and advancing research in economic decision-making.