Missing values that fail to be appropriately accounted for may lead to reduced statistical power, biased estimators, reduced representativeness of the sample, and incorrect interpretations and conclusions (Gorelick, 2006). The current study provided an ontological perspective of data manipulation by explaining how statistical results can…

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…

Most White people have learned to ignore the way that the social construct of race benefits them or harms people of Color, myself included. In this way, people take for granted that race is not real, but rather the invention of White supremacists. Moreover, researchers who are responsible for generating knowledge far too often depend on…

This research explores the emerging field of data science from the scientometric, curricular, and altmetric perspectives and addresses the following six research questions: 1.What are the scientometric features of the data science field? 2.What are the contributing fields to the establishment of data science? 3.What are the major research areas of…

This dissertation adopted the three-article format. The entire dissertation aims to explore Describing Critical Statistical Literacy Habits of Mind (CSLHM). The first article describes the creation of the CSLHM framework. The second describes how secondary preservice mathematics teachers enact CSLHM. The third article describes a common and an…

Student success efficacy studies are aimed at assessing instructional practices and learning environments by evaluating the success of and characterizing student subgroups that may benefit from such modalities. We develop an ensemble learning approach to perform these analytics tasks with specific focus on estimating individualized treatment…

This three-article dissertation considers the pedagogical practices for developing statistically literate students and teaching data-driven decision-making with the goal of preparing students for civic engagement and improving student achievement. The first article discusses a critical review of the literature on data-driven decision-making…

This thesis examined middle school students' current understanding of variability using a constructed response item assessment question. Variability is an essential concept in the teaching and learning of statistics. However, many students have difficulty with the concept of variability especially when constructing boxplots. Using a framework…

Parceling is referred to as a procedure for computing sums or average scores across multiple items. Parcels instead of individual items are then used as indicators of latent factors in the structural equation modeling analysis (Bandalos 2002, 2008; Little et al., 2002; Yang, Nay, & Hoyle, 2010). Item parceling may be applied to alleviate some…

Nursing students are expected to apply knowledge from lectures and laboratories to the clinical setting. One major challenge of nursing educators is facilitating the transfer of knowledge to the clinical-practice setting. Simulation-based education provides students with an experiential-learning activity within the context of a simulated clinical…

The goal of this study was to examine the reciprocal relationship between statistical investigation and motivation of college students in a Mathematical Reasoning course (Math 1). Unlike previous studies in which students' projects or statistical investigations have been examined as the final product that shows evidence of statistical…

With the wealth of data provided by a wide range of high-throughout measurement tools and technologies, statistical physics of complex systems is entering a new phase, impacting in a meaningful fashion a wide range of fields, from cell biology to computer science to economics. In this dissertation, by applying tools and techniques developed in…

Outlier detection is an important data mining task that is focused on the discovery of objects that deviate significantly when compared with a set of observations that are considered typical. Outlier detection can reveal objects that behave anomalously with respect to other observations, and these objects may highlight current or future problems. …

The purpose of this study was to determine teachers' perceptions of the effectiveness of the professional development that they received to implement the Charlotte Danielson Framework for Teaching Model in South Dakota. The population for this study was K-12 teachers from 12 pilot schools selected by the Department of Education (DOE) in South…

The National Center of Education Statistics' Early Childhood Longitudinal Study, Kindergarten Class of 1998-1999 (ECLS-K) data were used to explore the curricular emphasis in schools of varying socioeconomic status in both public and private schools. Data collected between 1998 and 2003 were used in the secondary analyses of curricular emphasis in…