Linking score scales across different tests is considered speculative and fraught, even at the aggregate level (Feuer et al., 1999; Thissen, 2007). We introduce and illustrate validation methods for aggregate linkages, using the challenge of linking U.S. school district average test scores across states as a motivating example. We show that aggregate linkages can be validated both directly and indirectly under certain conditions, such as when the scores for at least some target units (districts) are available on a common test (e.g., the National Assessment of Educational Progress). We introduce precision-adjusted random effects models to estimate linking error, for populations and for subpopulations, for averages and for progress over time. In this case, we conclude that the linking method is accurate enough to be used in analyses of national variation in district achievement, but that the small amount of linking error in the methods renders fine-grained distinctions among districts in different states invalid. We discuss how this approach may be applicable when the essential counterfactual question—“what would means/variance/progress for the aggregate units be, had students taken the other test?”—can be answered directly for at least some of the units.
Validation methods for aggregate-level test scale linking: A case study mapping school district test score distributions to a common scale
Year of Publication:2018
(2018). Validation methods for aggregate-level test scale linking: A case study mapping school district test score distributions to a common scale.