Evidence-Based Practices For Assessing Students’ Social And Emotional Well-Being
Category: Student Well-Being
When analyzing treatment effects on test scores, researchers face many choices and
competing guidance for scoring tests and modeling results. This study examines the
impact of scoring choices 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 data from a randomized controlled trial 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 scoring weights.
Keywords
causal inference, latent variable models, factor analysis, psychometrics, educational measurement
Education level
Topics
Document Object Identifier (DOI)
10.26300/4hah-6s55
