Randomized trial examines growth and diagnostic assessment using vertical scaling and DCMs in students, indicating viable approaches for effective evaluation.
Key Points
The research aims to integrate vertical scaling with Diagnostic Classification Models to assess student growth and diagnostic needs over time.
Evaluated four model specifications against traditional vertical scaling utilizing data from 17,302 kindergarten and 18,339 first-grade students.
Assessed model fit, score comparability, and classification reliability, along with Monte Carlo simulations for parameter recovery.
Identified tradeoffs among the modeling approaches for measuring student growth and diagnostics.
Second-order DCMs with constraints demonstrated practical viability although no single model emerged as ideal.