Before running a study, researchers and relevant domain experts such as doctors, nurses, or mechanics often have expectations about important aspects of the data that will be collected. Prior elicitation methods are structured interview methods used to transform these pre-data beliefs into probability distributions that can be used to inform Bayesian statistical models. In practice, many different prior elicitation methods have been employed, but a comprehensive psychometric evaluation of these methods is still lacking. However, employing prior elicitation techniques that do not adequately capture individual plausibility judgments can lead to misinformed models and, in the worst case, biased conclusions. In this talk, I will present a series of studies designed to compare the psychometric quality of three prior elicitation methods from the well-known SHELF framework and discuss their advantages and disadvantages for the elicitation of probability distributions.