For some traits, it is convenient to have multiple indicators (items). For example one might have for a particular disease 10 symptoms that each can be scored as absent (0) or present (1). For each individual one can then compute a sum score that indicates to what extent the individual is affected by the disease. Such sum scores usually show a normal distribution or do so after an appropriate transformation. It is typically assumed that the normally distributed scores or transformations thereof reflect a continuous interval scale and the variance of the sum scores is subsequently decomposed. This approach follows classical test theory (CTT) where it is assumed that the observed score (the sum score) is the aggregate of a true score and a random component, usually referred to as measurement error. When decomposing the variance of sum scores, the measurement error variance (the unreliability) ends up as part of the non-shared environmental variance. As a result, when the reliability of a scale is low (i.e., the measurement error is large) and the analysis is based on sum scores, the heritability of the actual trait is significantly underestimated.
