How measurement affects causal inference: Attenuation bias is (usually) more important than outcome scoring weights [Author Accepted Manuscript]

When analyzing treatment effects on outcome variables constructed from psychometric instruments (e.g., educational test scores, psychological surveys, or patient reported outcomes), researchers face many choices and competing guidance for scoring the measures and modeling results. This study examines the impact of outcome measure scoring and modeling approaches through simulation and an empirical application. Results show that estimates from multiple methods applied to the same data will vary because two-step models using sum or factor scores provide attenuated standardized treatment effects compared to latent variable models. This bias dominates any other differences between models or features of the data generating process, such as the use of scoring weights. An errors-in-variables (EIV) correction removes the bias from two-step models. An empirical application to 10 datasets from randomized controlled trials demonstrates the sensitivity of the results to model selection. This study shows that the psychometric principles most consequential in causal inference are related to attenuation bias rather than optimal outcome scoring weights.