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

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.

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.

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).

There has been increasing concern about the presence of disengaged test taking in international assessment programs and its implications for the validity of inferences made regarding a country’s level of educational attainment. In this paper, the author discusses six important insights yielded by 20 years of research on this and  implications for assessment programs.

The MAP Reading Fluency theory of action shows the hypothesized mechanisms of change and intermediate goals leading to the overarching goal of helping all students read fluently with comprehension.

This study investigated test-taking engagement on a large-scale state summative assessment. Overall, results of this study indicate that disengagement has a material impact on individual state summative test scores, though its impact on score aggregations may be relatively minor.