Using simulations, this study examined the “trifactor model,” a recent model developed to address rater disagreement.

Using a national sample of kindergarten to eighth grade students from Catholic and public schools who took MAP Growth assessments, we examine achievement growth over time between sectors.

In this paper we present a new R package called BFpack that contains functions for Bayes factor hypothesis testing for the many common testing problems. The software includes novel tools for (i) Bayesian exploratory testing (e.g., zero vs positive vs negative effects), (ii) Bayesian confirmatory testing (competing hypotheses with equality and/or order constraints), (iii) common statistical analyses, such as linear regression, generalized linear models, (multivariate) analysis of (co)variance, correlation analysis, and random intercept models, (iv) using default priors, and (v) while allowing data to contain missing observations that are missing at random.

Using data from the Applied Problems subtest of the Woodcock-Johnson Tests of Achievement administered to 1,364 children from the National Institute of Child Health and Human Development (NICHD) Study of Early Childcare and Youth Development (SECCYD), this study measures children’s mastery of three numeric competencies (counting, concrete representational arithmetic and abstract arithmetic operations) at 54 months of age.

This research introduces a novel approach, using the Bayes factor, wherein a researcher can directly test for homogeneous within-person variance in hierarchical models. Additionally, we introduce a membership model that allows for classifying which (and how many) individuals belong to the common variance model.

This document presents the intended uses and methodology of the MAP Growth linking studies, a description of the results provided in the linking study reports, and a summary of the recent linking studies conducted by NWEA to incorporate the new 2020 norms.

Two studies were conducted to evaluate whether the existing MAP Growth item parameter estimates are invariant across different calibration samples.