In operational testing, item response theory (IRT) models for dichotomous responses are popular for measuring a single latent construct [theta], such as cognitive ability in a content domain. Estimates of [theta], also called IRT scores or [theta hat], can be computed using estimators based on the likelihood function, such as maximum likelihood (ML), weighted likelihood (WL), maximum a posteriori (MAP), and expected a posteriori (EAP). Although the parameter space of [theta] is theoretically unrestricted, the range of finite [theta hat] is constrained by the estimator and test form properties, which is important to consider but often overlooked when developing a score scale for reporting purposes. Irrespective of the estimator or test forms at hand, a common practice is to fix arbitrary points symmetric about zero (e.g., -4 and 4) as anchors for deriving a score transformation, possibly resulting in unintended gaps or truncations at the extremes. Therefore, a systematic framework is proposed for using IRT scores to construct a robust score scale with informed boundaries that are logical and consistent across test forms.