Bayesian multilevel models are increasingly used in psychological, educational, and behavioral research, yet applied guidance on specifying priors for scalar variance components remains limited. This tutorial provides a practical workflow for identifying target variance components, selecting benchmark values, calibrating priors on variance and standard-deviation scales, translating them into software-compatible prior specifications, evaluating their implications through prior predictive checks, posterior comparison, sensitivity analysis, and transparent reporting. We review several commonly used prior families, including gamma, inverse-gamma, half- t , and half-Cauchy distributions, and clarify how their parameterizations relate across variance, precision (the reciprocal of variance), and standard-deviation formulations. The tutorial focuses on helping researchers align prior choice with substantive knowledge, model parameterization, software constraints, and sensitivity goals. As a worked example, we use a two-level analysis of TIMSS 2019 data to show how benchmark information from a non-overlapping reference sample can be calibrated into alternative variance-prior specifications and then evaluated in a smaller worked example sample. The empirical illustration is used as a pedagogical example to demonstrate the workflow and its associated checking procedures. We emphasize practical safeguards, including prior-density inspection, prior predictive checking, posterior comparison, convergence assessment, sensitivity analysis, and transparent reporting of prior assumptions. The tutorial concludes by summarizing practical, literature-grounded considerations for documenting prior rationale, implementation decisions, and robustness checks in Bayesian multilevel applications.