Mission-driven nonprofit organizations sometimes struggle within a cycle of disempowerment resulting from oversaturation of data collection that is reactive to funder demands. In this article, we problematize Community Music School (CMS) data-collection and analysis efforts and discuss alternate approaches to learning about family and community…

Data is everywhere, but not always accessible to everyone. Conventional data visualization tools and guidelines often assume a standard user, neglecting the neurodivergent needs of individuals with cognitive disabilities. These disabilities can create significant obstacles in data-driven reasoning, including difficulties in processing complex…

Creating a welcoming community is key for an academic environment to thrive. This approach includes accurately representing community members' identities to understand their experiences, and establishing procedures for recording and utilizing individuals' names to support their ability to express their identities freely and without fear of…

This study examined the predictive power of students' demographic characteristics, reading attitudes, school characteristics, and teacher-informed reading activities on three metacognitive reading skills: understanding and remembering, summarizing, and assessing credibility and their influence on 15-year-old students' reading scores. The dataset…

Statistical data is used in the media and on online platforms, often with an interpretation that serves some commercial or political purpose. Primary preservice teachers must be able to understand statistical data and analyze it critically. Only in this way will they be able to contribute as future teachers to the development of a critical spirit…

Program evaluation is important for assessing the implementation and outcomes of local, state, and federal programs. The Program Evaluation Toolkit provides tools and resources to support individuals responsible for evaluating and monitoring local, state, or federal programs. The toolkit comprises eight modules that cover critical steps in program…

Longitudinal panel studies are widely used in developmental science to address important research questions on human development across the lifespan. These studies, however, are often challenging to implement. They can be costly, time-consuming, and vulnerable to test--retest effects or high attrition over time. Planned missingness designs (PMDs),…

This article is presented in two parts: in the first part we discuss the use of R and R-related tools when implementing a data science curriculum in the classroom and direct readers to helpful R resources in education, and in the second part, we demonstrate the use of R in exploring COVID-19 data. In particular, we explore ethnic/racial…

Multiple studies show that outcomes, whether they be better degree completion rates or successful short-term workforce training, are negatively affected by inadequate funding (Kahlenberg, 2015). To bolster their case for underfunding, researchers and college advocacy groups produce studies that rely on an important federal dataset--the Integrated…

Digital phenotyping is defined as the "moment-by-moment quantification of the individual level human phenotype in situ using data from personal digital devices". The passive data collected by smartphone devices, including GPS, accelerometer and communication logs, can provide insights on users' behaviors that could be related to various…

This guide is designed for use by school, district, and state education agency staff to improve the effectiveness of efforts to collect and use discipline data, including reporting accurate and timely data to the federal government. It explains the importance of collecting discipline data, identifies key considerations for agencies implementing…

In this paper, we investigate an interesting question that came up when reading a problem in a school textbook: What happens to the variance of a dataset in the case of changing one single data point, and why? Some of the answers are not surprising but here we find the full answer and demonstrate the understanding of it suitable for school…

Evaluators have been slow to "plug" into the digital world, let alone engage with the strategic implications of the Big Data revolution for the evaluation discipline. Yet, Big Data holds enormous promise for extending the reach and improving the quality of evaluation practice. Equally, systematic recourse to evaluation would greatly…

Multilevel modeling is commonly used with clustered data, and much emphasis has been placed specifically on the multilevel linear model (MLM). When modeling clustered ordinal data, a multilevel ordinal model with cumulative logit link assuming proportional odds (i.e., multilevel cumulative logit model) is typically used. Depending on the research…

From Fall 2019 through Spring 2020, The Hunt Institute released a series of policy briefs, "Attainment for All: Postsecondary Pathways," that highlighted scalable state-level strategies to boost postsecondary attainment rates among specific student subpopulations including high school graduates, first-generation students, and adult…