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.

The American Rescue Plan provides $122 billion for COVID recovery in schools. With more than 40 state plans approved, how are districts collecting, monitoring, reporting and learning from the unprecedented interventions? What can districts do now to design and implement data collection processes that will shape collective learning? In this webinar, you will hear how district leaders and researchers are approaching this opportunity to alter life outcomes for generations.

Graphical modeling has emerged recently in psychology (Epskamp et al. 2018), where the data is typically long or low-dimensional (p < n; Donald R. Williams et al. (2019),
Donald R. Williams & Rast (2019)). The primary purpose of GGMnonreg is to provide methods that were specifically designed for low-dimensional data (e.g., those common in the
social-behavioral sciences).