This report documents the procedure used to produce the achievement and growth user norms for a series of the course-specific MAP® Growth™ subject tests, including Algebra 1, Geometry, Algebra 2, Integrated Math I, Integrated Math II, Integrated Math III, and Biology/Life Science. Among these tests, Integrated Math I, Integrated Math II, Integrated Math III, and Biology/Life Science were the first time to have their norms available. The remaining tests, i.e., Algebra 1, Geometry, and Algebra 2, had their norms updated including receiving more between-term growth norms by using more recent test events. Procedure for norm sample selection and a model-based approach using the multivariate true score model (Thum & He, 2019) that factors out known imprecision of scores to generate the norms are also provided in detail, along with the snapshots of the achievement and growth norms for each test.

New research using longitudinal data provides evidence that deaf and hard of hearing (DHH) students continue to build skills in math and reading throughout grades 2 to 8, challenging assumptions that DHH students’ skills plataeu in elementary grades.

This report documents the norming study procedure used to produce the achievement and growth user norms for English MAP Reading Fluency Foundational Skills.

Four-day school weeks have proliferated across the United States, but little is known about their implementation or their effects on students. This study uses district-level data from Oklahoma  to provide estimates of the causal effect of the 4-day school week on high school students’ ACT scores, attendance, and disciplinary incidents during school.

This study compared the test taking disengagement of students taking a remotely administered an adaptive interim assessment in spring 2020 with their disengagement on the assessment administered in-school during fall 2019.

The MAP Growth theory of action describes key features of MAP Growth and its position in a comprehensive assessment system.

This study compares estimation of symptom networks with Bayesian GLASSO- and Horseshoe priors to estimation using the frequentist GLASSO using extensive simulations.