This study investigates how different modeling approaches affect the measurement of loss aversion, a fundamental concept in psychology and economics. Analyzing 10 datasets comprising over 140,000 trials from 686 participants, we compared four prominent methods: Maximum Likelihood Estimation with Prospect Theory, Bayesian Prospect Theory, Generalized Linear Models, and Drift Diffusion Models. While group-level median loss aversion estimates showed consistency across methods, significant differences emerged at the individual level. The analysis revealed substantial individual-level methodological variability in both the magnitude of loss aversion estimates and participant classification. These findings demonstrate the impact of methodological choices on loss aversion measurement and underscore the need for careful consideration when comparing results across studies using different estimation techniques.