There is a need to improve the validity, reliability, and replicability of social and health science research and its applications through raising the quality of measurement. An important step is to establish and implement a clear and useful guideline for reporting and assessing psychometric properties of measures. We propose five basic criteria as a minimal checklist to help end users assess the quality of psychometric studies: unidimensionality; ordered response categories, invariance; targeting; and, contingent upon the previous four being fulfilled, reliability. An expanded and detailed reporting guideline is also presented, intended for use in reports and scientific publications of psychometric analyses. We argue that reliability should be reported using a Test Information Function curve, which describes the properties of the items, rather than a point estimate describing sample properties. Additionally, we present a freely available R package to streamline psychometric analysis with Rasch Measurement Theory and its documentation in line with the reporting guideline.