Objectives: Equivalence testing (ET) evaluates the comparability between electronic and paper modes of a clinical outcome assessment’s (COA) administration (Coons et al., 2009). The number of studies employing eCOA measures has grown substantially, but the methodology for ensuring paper and electronic measurement equivalence is highly varied across the industry. The goals of this work are to provide recommendations for standardizing and improving the implementation of ET. Background: ET is a quantitative process that determines whether patients’ responses to the items of an instrument change according to the mode of administration (e.g., paper vs electronic). At least two meta-analyses have examined how ET has been implemented across numerous studies (Gwaltney et al., 2008; Muehlhausen et al., 2015). Overall, there is great methodological variability between previous ET studies, including the number of patients (min = 10; max = 1439), whether administration order is randomized, the exact equivalence statistics computed, length of time between administrations, and whether any tasks intervene between administrations (e.g., distractor tasks). Furthermore, the critical statistics commonly computed (e.g., ICC or Pearson’s R) vary greatly between studies (min = 0.66; max = 0.99) and, as with descriptive statistics, do not definitively show whether equivalence has been maintained. Together, these issues limit the utility and comparability of existing ET work. Conclusions: We propose the introduction of more methodological and statistical rigor in a revised approach to ET, which leverages several types of statistical analyses (descriptive statistics, hypothesis testing via mixed effects models, correlational analyses) to convergently determine whether equivalence has been maintained and to locate the source of any differences, so that corrections to an instrument can be made. We further propose a standardized, fully randomized administration procedure with intervening distractor tasks and a sample size determined by power analysis to increase the validity of ET.