There is increasing interest in using factor scores in structural equation models and there have been numerous methodological papers on the topic. Nevertheless, sum scores, which are computed from adding up item responses, continue to be ubiquitous in practice. It is therefore important to compare simulation results involving factor scores to those of sum scores so that applied researchers can understand the advantages. Yet, researchers seldom compare sum scores and factor scores in terms of bias, a common simulation outcome. A reason for this is that sum scores are on a different scale and it has been unclear how to compare sum scores to other types of scores. This paper provides guidance on computing bias for sum scores by deriving the expected values of model parameters in a sum score model.