The Stanford Education Data Archive (SEDA) launched in 2016 to provide nationally comparable, publicly available test score data for U.S. public school districts. I introduce a special collection of six articles that each use SEDA to lend their questions and findings a national scope. Together, these articles demonstrate a range of uses of SEDA…

I review opportunities and threats that widely accessible Artificial Intelligence (AI)-powered services present for educational statistics and measurement. Algorithmic and computational advances continue to improve approaches to item generation, scale maintenance, test security, test scoring, and score reporting. Predictable misuses of AI for…

Positive Behavioral Interventions and Supports (PBIS) is an evidence-based approach to addressing behavior issues in schools. A significant amount of research has been done to identify the critical features of PBIS. More generally, implementation science points to a specific sequence to ensure that PBIS is implemented with fidelity. This fall…

Trend scoring constructed response items (i.e. rescoring Time A responses at Time B) gives rise to two-way data that follow a product multinomial distribution rather than the multinomial distribution that is usually assumed. Recent work has shown that the difference in sampling model can have profound negative effects on statistics usually used to…

Traditional validation approaches fail to account for the ways oppressive systems (e.g. racism, radical nationalism) impact the test design and development process. To disrupt this legacy of white supremacy, we illustrate how justice-oriented, antiracist validation (JAV) framework can be applied to construct articulation and validation, data…

For assessments that use different forms in different administrations, equating methods are applied to ensure comparability of scores over time. Ideally, a score scale is well maintained throughout the life of a testing program. In reality, instability of a score scale can result from a variety of causes, some are expected while others may be…

The rise of big data technology provides direction and support for the reform and development of education. Big data technology can realize the inventory management and effective dynamic monitoring of schools, students, and teachers. It is conducive to comprehensively and accurately controlling the development of teaching activities, injecting new…

Positive Behavioral Interventions and Supports (PBIS) is an evidence-based approach to addressing behavior issues in schools. A significant amount of research has been done to identify the critical features of PBIS. More generally, implementation science points to a specific sequence to ensure that PBIS is implemented with fidelity. This spring…

Due to repeated observations of an outcome behavior in N-of-1 or single-case design (SCD) intervention studies, the occurrence of missing scores is inevitable in such studies. Approximately 21% of SCD articles published in five reputable journals between 2015 and 2019 exhibited evidence of missing scores. Missing rates varied by designs, with the…

Composite scores are an exceptionally important psychometric tool for behavioral science research applications. A prototypical example occurs with self-report data, where researchers routinely use questionnaires with multiple items that tap into different features of a target construct. Item-level missing data are endemic to composite score…

In several sporting events, the winner is chosen on the basis of a subjective score. These sports include gymnastics, ice skating, and diving. Unlike for other subjectively judged sports, diving competitions consist of multiple rounds in quick succession on the same apparatus. These multiple rounds lead to an extra layer of complexity in the data,…

The "Every Student Succeeds Act" requires that student's test scores be disaggregated by racial characteristics. Nevertheless, the author's recent study suggests that K-12 school principals may not intentionally think about race when they collect, interpret, analyze, and make decisions about data. By not disaggregating data by race,…

Inappropriate treatment of missing data may introduce bias into the value-added estimation. We consider a commonly used value-added model (VAM), which includes the past student test score as a covariate. We formulate a joint model of student achievement and missing data, in which the probability of observing a test score depends on observing the…

This article is a response to an article written by Wang and Ma "An Examination of Plausible Score Correlation from the Trend in Mathematics and Science Study", published in the Athens Journal of Education. The purpose of this paper is to address issues with Wang's and Ma's suggestion to use analysis method for correlating plausible…

Many of the numbers used to assess students are statistical in nature. The theoretical context underlying the production of a typical number or statistic used in student assessment is presented. The author urges readers to recognize objective data as subjective information and to carefully consider the numbers that often determine admission,…