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Reliability and measurement error are related but distinct measurement properties. They are connected because both can be evaluated using the same data, typically collected from studies involving repeated measurements in individuals who are stable on the outcome of interest. However, they are calculated using different statistical methods and refer to different quality aspects of measurement instruments. We explain that a measurement error refers to the precision of a measurement, that is, how similar or close the scores are across repeated measurements in a stable individual (variation within individuals). In contrast, reliability indicates an instrument's ability to distinguish between individuals, which depends both on the variation between individuals (ie, heterogeneity in the outcome being measured in the population) and the precision of the score, ie, the measurement error. Evaluating reliability helps to understand if a particular source of variation (eg, occasion, type of machine, or rater) influences the score, and whether the measurement can be improved by better standardizing this source. Intraclass-correlation coefficients, standards error of measurement, and variance components are explained and illustrated with an example.
Mokkink et al. (Thu,) studied this question.