There is an increasing demand to introduce Introductory Business Analytics (IBA) courses into undergraduate business education. Many real-world business contexts require predictive analytics to understand the determinants of a dichotomous outcome; hence, IBA courses should include binary logistic regression analysis. This article provides our…

This article is written with the recognition that, as higher education studies evolve as a multidisciplinary area of inquiry, there is a need to reflect on the theoretical and practical concerns emerging from conducting higher education research. This is especially the case for early-career researchers who enter this relatively new field of study.…

This research aims at providing an overview of the research field of digital literacy into learning and education. Using text mining, it reviews 1037 research articles published on the topic between 2000 and 2020. This review reveals that there is a plurality of terms associated with digital literacy. Moreover, our research identifies six key…

Joint efforts by the Government of Nepal, development partners and key stakeholders to achieve SDG 4 by 2030 have improved education access, participation and retention. However, learning outcomes in Nepal remain stagnant. What resources and contextual factors are associated with good school performance in Nepal? By merging and analyzing existing…

This volume provides alternatives for tackling existing empirical, methodological, and analytical challenges. It does so by providing a broad overview of less established, as well as emerging methods, which are of great relevance for current research on professional learning and development. As such, it offers a comprehensive collection of…

The purpose of this study was to explore how evaluators justify using story-based methodologies when examining causality. The two primary research questions of the study included: 1) what arguments are made by evaluators to justify the credibility of story-based causal methodologies to evaluation stakeholders; and 2) from the perspective of…

Teachers in the United States are increasingly tasked with using data to inform their classroom instruction both through federal policies, such as the Every Student Succeeds Act (ESSA, 2016), and state policies requiring the use of teacher-determined data-driven goals for performance evaluations (MA 603 CMR 35.07). Many teachers, however, report…

The education landscape in the United States has been changing rapidly in recent decades: student populations have become more diverse; there has been an explosion of data sources; there is an intensified focus on diversity, equity, inclusion, and accessibility; educators and policy makers at all levels want more and better data for evidence-based…

Mandatory community college student fees have risen steadily over the past 20 years, outpacing the growth of tuition by a substantial margin. To date, research on postsecondary fees has been limited to public four-year colleges and universities, even as community college fees are growing faster and affecting students who are likely more price…

Research Context: In educational research, "context effects" are often of inferential interest to researchers as well as of evaluative interest to policymakers. While student education outcomes likely depend on individual-level influences like individual academic achievement, school contexts may also make a difference. Such questions are…

This paper shows a method for integrating computer programming into a standard physical chemistry laboratory sequence to augment student data analysis abilities and allow them to carry programming skills forward to other courses. The Python programming language is used, taking advantage of the pedagogical benefits of Jupyter notebooks, primarily…

Data science programming presents many challenges for programmers entering the field. Roughly, data science programming can be broken up into several activities: data wrangling, analysis, modeling, or visualization. Data wrangling is an important first step that involves cleaning and shaping tabular data--or dataframes--into a form amenable for…

The purpose of this study is to evaluate the performance of three commonly used model fit indices when measurement invariance is tested in the context of multiple-group CFA analysis with categorical-ordered data. As applied researchers are increasingly aware of the importance of testing measurement invariance, as well as Likert-type scales are…

Advances in state education data systems are helping schools understand and meet the needs of learners from early education through college and the workforce. Each state has adhered to key principles for protecting student data privacy. In the report "Lessons in Data Privacy for Education Leaders. Policy Guide," (ED625423) leading…

This article provides an overview of different computational options for inference following normal theory maximum likelihood (ML) estimation in structural equation modeling (SEM) with incomplete normal and nonnormal data. Complete data are covered as a special case. These computational options include whether the information matrix is observed or…