In this work, we introduce a novel oversampling technique, the theory of inheritance and Gower distance-based oversampling (TIGO) method, designed to address class imbalance issues in mixed categorical and continuous variables data set. Drawing inspiration from genetic inheritance principles, TIGO synthesizes new minority class data,…

This dissertation documented how undergraduate students made sense of data in news media. The participants were 30 undergraduate students enrolled in a course called "Numbers and Social Justice." The study used argument analysis (Toulmin, 2003) and ethnographic methodology to examine students' written work in a naturalistic setting.…

Data are everywhere. Data collected from samples are often reported in the form of polls, medical studies, and advertisement information and an understanding of sampling distributions and statistical inference is important for evaluating data-based claims (Bargagliotti et al., 2020). Despite the importance of understanding statistical inference…

Foundations of statistics research aims to establish fundamental principles guiding inference about populations under uncertainty. It is concerned with the process of learning from observations, notions of uncertainty and induction, and satisfying inferential objectives. The growing interest in predictive methods in high-stakes fields like…

Postsecondary Institutions are under pressure to explain their costs. Noninstructional administrative staff represent a significant cost and number of people working in higher education. Postsecondary institutions hire noninstructional staff in response to their specific needs related to educational activity at the institution. Past research on…

Latent change score (LCS) models are a powerful class of structural equation modeling that allows researchers to work with latent difference scores that minimize measurement error. LCS models define change as a function of prior status, which makes it well-suited for modeling developmental theories or processes. In LCS models, like other latent…

Simulations were conducted to establish best practice in hyperparameter optimization and accounting for clustering in Generalized Linear Mixed-Effects Model Trees (GLMM trees). Using data-driven best practices, the relationship between a 9th Grade On-Track to Graduate (9G-OTG) indicator and observed high school graduation within four years was…

In today's era of large-scale data, academic institutions, businesses, and government agencies are increasingly faced with heterogeneous datasets. Consequently, there is a growing need to develop effective methods for extracting meaningful insights from this type of data. Quantile, expectile, and expected shortfall regression methods offer useful…

Data-based decision-making has been a frequently used policy and practice intervention used in schools to help inform the decision-making processes of educational practitioners, with the aim of improving student outcomes. Interim benchmark assessments are designed by commercial test developers to support educators in this framework. In fact, the…

This policy analysis examined the intersection of a statistical model and locus of control to recommend an accountability model that best upholds the criteria of equity, efficacy, and impact on student learning for Missouri schools. Using Eugene Bardach's eightfold path and informed by the quality education in schools framework, this analysis…

This dissertation examines the connection between the English Language Arts Professional Learning Community, data analysis, and research-based instructional strategies to improve students reading levels, as measured by the Pennsylvania Literature Keystone Exam at an Urban High School. It is a mixed-method study. Data were collected from 9th -12th…

Institutional researchers use skills from their diverse backgrounds to collect, analyze and report data about their institutions to stakeholders representing various interests and levels of data literacy. However, there is little research into how these professionals process data and none into what aspects are important to institutional…

This quantitative study was designed to investigate whether a relationship existed between reading achievement and instructional modality during the COVID-19 pandemic. In the fall of 2020, school districts developed return-to-school plans that outlined if they would provide in-person, hybrid, or remote instruction to students following the state…

Graph data, especially large graph data, come up in many domains, such as social networks, the map of science, biological data, and even knitting! This presents a problem when we consider visualizing these structures. Layouts must be chosen carefully so that the structure of the graph is visible. Some graphs are large enough and connected enough…

Graph Signal Processing (GSP) plays a crucial role in addressing the growing need for information processing across networks, especially in tasks like supervised classification. However, the success of GSP in such tasks hinges on accurately identifying the underlying relational structures, which are often not readily available and must be inferred…