Categorical Data Analysis Commons^™

A Text Mining And Sentiment Analysis Of Valuable Cie Texts Using R, Eric Sugarman, Ethan Turber-Ortiz, Hannah Quinn

Mathematics, Computer Science & Statistics Presentations

The purpose of this project was to perform a sentiment analysis of three texts used in Ursinus College's Common Intellectual Experience (CIE) course: Between the World and Me by Ta-Nehisi Coates, The New Jim Crow by Michelle Alexander and Discourse on Method by Rene Descartes. Word count and word cloud analysis were also performed on the texts as well as term frequency and bigram analysis.

Reshaping Future: Research Progress And Future Prospects Of Disruptive Technology Topics, Sanhong Deng, Jianming Guo, Yujie Shi

Journal of Scientific Information Research

[Purpose/significance]Disruptive technology is regarded as a revolutionary force that "changes the rules of the game" and "reshapes the future pattern", and has gradually become a hot and difficult issue in interdisciplinary research. This paper summarizes the literature related to disruptive technologies, clarifies the concepts and characteristics of disruptive technologies, subdivides research topics and directions, summarizes research focuses and looks forward to future development trends, and provides reference for relevant personnel. [Method/ process]On the basis of sorting out the latest related research on disruptive technologies, this paper clarifies the research progress of disruptive technologies from three aspects: the concept and characteristics …

Visualizing The Rise Of American Women's Soccer, Hoi Wah Tang

Dissertations, Theses, and Capstone Projects

The rise of American women’s soccer is a compelling narrative of determination, talent, and triumph on the global stage. From the inaugural FIFA Women’s World Cup in 1991 to the ongoing dominance of the U.S. Women’s National Team (USWNT), this journey encapsulates not only athletic excellence but also the broader struggles for equality, representation, and respect in sports.

This project aims to bring this story to life by presenting a rich tapestry of data visualizations, timelines, and multimedia elements that showcase key moments, players, and milestones. Through engaging graphics and interactive features, users will explore how the sport evolved, the …

Chatgpt Didn’T Write This: Evaluating The Impact Of Llms With A Case Study In Grading Cuny Language Immersion Program Student Essays, Benjamin Inbar

Dissertations, Theses, and Capstone Projects

This study evaluates the capabilities and limitations of large language models (LLMs), specifically OpenAI’s ChatGPT-4o, in grading essays from students in the City University of New York’s Language Immersion Program. The program serves English language learners with diverse linguistic and demographic backgrounds, offering intensive language instruction to prepare students for academic success in college. Using a dataset of 30 pre- and post-program essays scored by program instructors and ChatGPT-4o under three paradigms, this research explores the alignment between human and AI-generated scores across five rubric-based competency areas. Findings reveal that ChatGPT-4o aligns moderately with human grading, with the strongest agreement …

Kroger Post-Pandemic Customer Segmentation, Mario Mata, Joey Truitt, Renn Spigelmyer, Dhanuja Kasturiratna, Lisa Holden, Nitish Baidya, Hanna Tafari

Posters-at-the-Capitol

The grocery retail industry landscape has changed greatly in the wake of the pandemic. Specifically, delivery and pickup services have become more popular and customer buying habits have evolved. At the same time, improvements in data collection and analysis have allowed grocery marketing strategies to become highly individualized.

We worked with 84.51, an analytics firm, to identify customer segments for the Kroger Company based on data from 2023. Using clustering techniques, we organized customers into groups, or segments, based on similar characteristics. We identified and profiled four distinct groups of customers. Three segments were characterized by high frequency and spending …

Identifying Research Paths Based On Main Path Analysis: A Case Study Of Knowledge Graphs, Ruibin Wei, Yidan Wang, Yan Xu

Journal of Scientific Information Research

[Purpose/significance]The main path analysis of citation networks can be used to identify important literature in specific fields and can achieve the extraction of mainstream research threads. This paper will use the main path analysis method to analyze the research path of knowledge graphs and sort out the context of their research development. [Method/process]This paper firstly obtains research papers in the field of knowledge graphs from the Web of Science platform, then uses the HistCite software to generate a direct citation network of the literature, and then imports the data into Pajek to generate multiple main paths of the dataset, and …

Analyzing Factors Influencing Employee Turnover In Tech Companies: A Predictive Modeling Approach, Shinjon Ghosh

Theses and Dissertations

Employee turnover poses substantial challenges for technology firms, and understanding its key drivers through predictive modeling is essential for developing effective retention strategies. This study investigates factors influencing employee turnover in technology companies by implementing a predictive modeling approach on the IBM HR Analytics Employee Attrition dataset. The research aims were identifying key factors contributing to employee attrition, developing predictive models to forecast turnover risk, and analyzing interactions among significant predictors. By examining a range of features, the results highlight significant variables (Over Time, Monthly Income, Marital Status, etc.) of attrition and offer actionable insights for developing targeted employee retention …

Predicting Real Estate Prices Using Deep Learning Regression Models On Socio Spatial Data, Gentle Engworo

Graduate Theses/Dissertations

ABSTRACT

Cities keep their own kind of ledger. Every block, bus stop, corner store, and year that slips by leaves a small entry about what homes are worth. That ledger is what we call socio-spatial data: simple facts about what a home is (its age), where it sits (latitude/longitude), how easy it is to get around (distance to the nearest MRT station), what’s nearby (number of convenience stores), and when it sold (transaction date). This thesis asks a practical question in that everyday language: given these common clues, can we predict home prices more accurately and explain why? Using 414 …

Determination Of Electrochemical Parameters For Predicting Reaction Mechanism And Algorithmic Approaches To Pain Assessment, Huize Xue

Dissertations

This dissertation introduces novel advancements in electrochemical kinetics and pain assessment, structured into two main parts. The first part focuses on the comprehensive analysis of the kinetic and mechanistic aspects of electrochemical reactions, utilizing a combination of experimental techniques and simulation methods. A new software tool, Envismetrics, was developed using Python to facilitate the analysis of complex electrochemical data, including cyclic voltammetry (CV), chronoamperometry (CA), and hydrodynamic voltammetry (HDV). The software was rigorously tested and validated with well-characterized redox systems such as the ferricyanide/ferrocyanide couple, dimethylamine borane (DMAB), and Per- and Polyfluoroalkyl Substances (PFAS). It was successfully used to determine …

Bayesian Estimation Of A Pragmatic Model For Monetary Policy Analysis: The Case Of Pakistan, Shahzad Ahmad, Waliullah .

CBER Conference

In this study, Bayesian maximum likelihood estimation of quarterly projections model for Pakistan as presented, as documented in Ahmad & Pasha (2015). Estimation results based on quarterly data from 2001 to 2023 show substantial differences in values of estimated versus calibrated parameters related to aggregate demand, aggregate supply, monetary policy rule and exogenous shock processes. The aim of this study is to compare forecasting performance for key macro variables. It shows that the estimated model provides more precise forecasts in case of headline inflation, real GDP growth, interest rate and exchange rate over 8-quarters forecast horizon. An estimated model for …

Customer Data And The Digital Age, Mahdi Ansari

CBER Conference

Data is widely regarded as the most valuable resource in today’s economy, yet its value often eludes precise quantification. This paper examines customer data as an intangible capital asset and addresses the challenge of measuring its impact. A novel database was created by merging Compustat with online clickstream data capturing the activity of approximately 200 million users, providing proxies for data inflow based on visit metrics. The analysis documents that the distribution of firms’ customer data stocks follows a rightskewed log-normal pattern with a fat tail. Additionally, a positive relationship emerges between sales and data inflow, data stock, profit, and …

If You Can’T Beat Them Join Them: Empirical Assessment Into How Integrating Conventional Taxis On The Uber App Impacts Conventional Taxi Ridership, Shahmeer Mohsin

CBER Conference

Since the emergence of ride-hailing platforms like Uber, conventional taxi ridership has taken a severe hit. Taxi-hailing apps like Curb and Arro have allowed conventional taxis to jump on the platform economy bandwagon and offer a similar service to ride-hailing platforms. Despite the emergence of these taxi-hailing apps, strong lock-in effects and high switching costs of popular ride-hailing platforms (Uber, Lyft, etc.) restrict the ridership volumes of conventional taxis. Recently, the ride-hailing platform, Uber has started to add conventional taxis on its app under increasing pressure from Cities and conventional taxi associations. Such integrations have the potential of increasing conventional …

Statistical Analysis For Pre- And Post- Assessments Of Sdq And Idela Scores, Diego Murillo, Franceli L. Cibrian

Student Scholar Symposium Abstracts and Posters

This research aimed to assess the potential of Mazi Umntanakho ("Know Your Child") in tracking developmental milestones in young children. Mazi is a WhatsApp-based conversational agent that assists South African home visitors in evaluating and monitoring children's socio-emotional skills using the Strengths and Difficulties Questionnaire (SDQ) and the International Development and Early Learning Assessment (IDELA). A field study was conducted in low-income South African communities, where 95 home visitors assessed 1,208 children. This detailed analysis of the data was collected during that deployment, focusing on investigating whether assessment scores improved over time and whether the length of time between assessments …

A Latent Class Assessment Of Healthcare Access Factors And Disparities In Breast Cancer Care Timeliness, Matthew Dunn, Didong Li, Marc Emerson, Caroline Thompson, Hazel Nichols, Sarah Van Alsten, Mya Roberson, Stephanie Wheeler, Lisa Carey, Terry Hyslop, Jennifer Elston Lafata, Melissa Troester

Kimmel Cancer Center Faculty Papers

BACKGROUND: Delays in breast cancer diagnosis and treatment lead to worse survival and quality of life. Racial disparities in care timeliness have been reported, but few studies have examined access at multiple points along the care continuum (diagnosis, treatment initiation, treatment duration, and genomic testing).

METHODS AND FINDINGS: The Carolina Breast Cancer Study (CBCS) Phase 3 is a population-based, case-only cohort (n = 2,998, 50% black) of patients with invasive breast cancer diagnoses (2008 to 2013). We used latent class analysis (LCA) to group participants based on patterns of factors within 3 separate domains: socioeconomic status ("SES"), "care barriers," and …

Variable Selection In Distance Metric Learning And Triplet Constraints For Deep Learning Based Ordinal Classification, James D. Clothier

Dissertations and Doctoral Documents, University of Nebraska-Lincoln, 2023–

The purpose of this research is to augment linear and kernelized ordinal distance metric learning (L/KODML) techniques with a proposed variable selection methodology that integrates the Sequential Multi-Response Feature Selection (SMuRFS) algorithm. Additionally, we aim to embed ordinal triplet constraints into a deep learning architecture, and to propose a general framework for deep learning-based ordinal classification. A variety of simulation studies and real data experiments were conducted to evaluate the various methodologies. For the distance metric learning and variable selection, results showed that the integration of SMuRFS performed effective variable selection and improved prediction accuracy. For the triplet constraints, incorporating …

Learning De-Biased Representations For Remote-Sensing Imagery, Zichen Tian, Zhaozheng Chen, Qianru Sun

Research Collection School Of Computing and Information Systems

Remote sensing (RS) imagery, requiring specialized satellites to collect and being difficult to annotate, suffers from data scarcity and class imbalance in certain spectrums. Due to data scarcity, training any large-scale RS models from scratch is unrealistic, and the alternative is to transfer pre-trained models by fine-tuning or a more data-efficient method LoRA. Due to class imbalance, transferred models exhibit strong bias, where features of the major class dominate over those of the minor class. In this paper, we propose debLoRA---a generic training approach that works with any LoRA variants to yield debiased features. It is an unsupervised learning approach …

Pediatric Renal Cell Carcinoma (Prcc) Subpopulation Environmental Differentials In Survival Disadvantage Of Black/African American Children In The United States: Large-Cohort Evidence, Laurens Holmes, Phatismo Masire, Arieanna Eaton, Robert Mason, Mackenzie Holmes, Justin William, Maura Poleon, Michael Enwere

College of Population Health Faculty Papers

OBJECTIVE: Renal cell carcinoma (RCC) is a rare but severe and aggressive pediatric malignancy. While incidence is uncommon, survival is relatively low with respect to acute lymphocytic leukemia (ALL), AML, lymphoma, ependymoma, glioblastoma, and Wilms Tumor. The pediatric renal cell carcinoma (pRCC) incidence, cumulative incidence (period prevalence), and mortality vary by health disparities' indicators, namely sex, race, ethnicity, age at tumor diagnosis, and social determinants of health (SDHs) as well as Epigenomic Determinants of Health (EDHs). However, studies are unavailable on some pRCC risk determinants, such as area of residence and socio-economic status (SES). The current study aimed at assessing …

Third Ward Casper 2023 Final Report, Zuri Dale, Vishnu Nepal, Debra Maxwell, Ashlee Young

COVID Research

The 2023 Third Ward CASPER Final Report conducted by Texas Southern University (TSU) and the Houston Health Department assessed public health conditions in Houston's Third Ward community. The initiative utilized the Community Assessment for Public Health Emergency Response (CASPER) methodology to gather household-based data, informing public health decisions, emergency responses, and future resource allocation.

Objective and Key Findings: The mission aimed to assess health needs, promote health resources, and build community relationships. Notable findings include that over 36% of households reported hypertension, while 19% experienced poor mental health. Access barriers to healthcare persisted, particularly due to lack of insurance. Nearly …

Rates Of Reporting And Analyzing Race And Ethnicity In Athlete-Specific Sports Medicine Research: A Systematic Review, John H. Sonnier, Carlo Coladonato, Irfan A. Khan, Gregory Connors, Ryan W. Paul, Anya T. Hall, Emma E. Johnson, Meghan E. Bishop, Fotios P. Tjoumakaris, Kevin B. Freedman

Rothman Institute Papers

BACKGROUND: Race- and ethnicity-based differences in treatment access and outcomes have been reported in the orthopaedic sports medicine literature. However, the rate at which race and ethnicity are reported and incorporated into the statistical analysis of sports medicine studies remains unclear.

PURPOSE: To determine the rate at which race and ethnicity are reported and analyzed in athlete-specific sports medicine literature.

STUDY DESIGN: Systematic review; Level of evidence, 4.

METHODS: Using PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines, articles published by the 3 journals of the American Orthopaedic Society for Sports Medicine between 2017 and 2021 were considered …

Innovation Challenges In The Air Force Sbir Program: From The Small Businesses' Perspective, Hart J. Holt, Amy M. Cox, Scott Drylie, David S. Long, Alfred E. Thal Jr., Robert D. Fass

Faculty Publications

Every year the United States invests $3.2 billion in the Small Business Innovation Research (SBIR) program to promote innovation among the nation’s small businesses. Half of this investment is from the DoD. This research considers the challenges faced by small businesses innovating with the DoD, particularly those awarded SBIR contracts with the United States Air Force. The authors surveyed 286 unique small businesses that were previously awarded an Air Force SBIR contract. By asking the survey respondents open-ended questions and categorizing their responses, they pinpoint unaddressed challenges from the small business perspective. By categorizing survey responses through Qualitative Content Analysis, …

Assessing Gtfs Accuracy, Gregory L. Newmark

Mineta Transportation Institute

The promised benefits of the General Transit Feed Specification (GTFS) Schedule and Realtime standards are dependent on the underlying quality of the data. Despite this fundamental reliance, there has been relatively little research on techniques and strategies to assess GTFS accuracy. The need for such assessment is growing as federal and state governments increasingly require transit agencies to make these data available to the public. This research fills this gap by presenting a suite of methods and metrics to assess the temporal accuracy of GTFS Realtime and the spatial accuracy of GTFS Schedule feeds. The temporal assessment demonstrates an approach …

An Application Of An In-Depth Advanced Statistical Analysis In Exploring The Dynamics Of Depression, Sleep Deprivation, And Self-Esteem, Muslihat Gaffari

Electronic Theses and Dissertations

Depression, intertwined with sleep deprivation and self-esteem, presents a significant challenge to mental health worldwide. The research shown in this paper employs advanced statistical methodologies to unravel the complex interactions among these factors. Through log-linear homogeneous association, multinomial logistic regression, and generalized linear models, the study scrutinizes large datasets to uncover nuanced patterns and relationships. By elucidating how depression, sleep disturbances, and self-esteem intersect, the research aims to deepen understanding of mental health phenomena. The study clarifies the relationship between these variables and explores reasons for prioritizing depression research. It evaluates how statistical models, such as log-linear, multinomial logistic regression, …

The Fourth International Workshop On Smart Data For Blockchain And Distributed Ledger (Sdbd '24), Feida Zhu, Jian Pei, Michael Zeller, Bingxue Zhang

Research Collection School Of Computing and Information Systems

With the advent of Bitcoin, a cryptographically-enabled peer-to-peer digital payment system, blockchain together with a whole package of distributed ledger technologies, which serve as the underlying foundation of all the crypto-currencies, have been gaining attention from both academia and industry in the last fifteen years. The recent years have witnessed tremendous momentum in the development of blockchain and distributed ledger technologies, largely due to the impressive rise in the market capital of these digital tokens. More and more industries, from banking and insurance, to supply chain and e-commerce, are quickly realizing the great potential in blockchain technology in efficiency boost, …

Multi-Case Study Of Left-Flank Boundaries Within Supercells, Peyton B. Stevenson

Department of Earth and Atmospheric Sciences: Dissertations, Theses, and Student Research

This study investigates the prevalence and significance of forward-flank convergence boundaries (FFCBs) and left-flank convergence boundaries (LFCBs) in shaping the structure and intensity of supercells, using observational data from various field projects. Unlike previous research focusing on individual cases, this study examines a diverse range of cases to provide comprehensive insights into the relationship between these boundaries and supercell characteristics such as intensity, longevity, and tornadogenesis. By analyzing high-resolution surface data, the research addresses the frequency, location, and intensity of these boundaries, and their impact on pseudo vertical vorticity, pseudo convergence, and density gradients. A total of 228 boundary identifications …

Intimacy Without The Chance Of Heartbreak For Richer, For Poorer, In Sickness & In Health, Cynthia Nguyen

Honors Projects

The present study investigates the effect of the COVID-19 pandemic on the consumption of porn, shifts in the production of porn consumed between men and women, and the breakdown of any pattern in adult content via film, pictures, and audio. A quantitative approach was done by using R to analyze data pulled off of Pornhub, Reddit’s GoneWildAudio subreddit, and Archive of Our Own from 2018 to 2023. Statistical inference and modeling is used to attempt to find a pattern in the production of online porn across three mediums over several years before, during, and after the pandemic. Regardless of events …

Accessible Real-Time Eye-Gaze Tracking For Neurocognitive Health Assessments, A Multimodal Web-Based Approach, Daniel C. Tisdale

Master's Theses

We introduce a novel integration of real-time, predictive eye-gaze tracking models into a multimodal dialogue system tailored for remote health assessments. This system is designed to be highly accessible requiring only a conventional webcam for video input along with minimal cursor interaction and utilizes engaging gaze-based tasks that can be performed directly in a web browser. We have crafted dynamic subsystems that capture high-quality data efficiently and maintain quality through instances of user attrition and incomplete calls. Additionally, these subsystems are designed with the foresight to allow for future re-analysis using improved predictive models, as well as enable the creation …

Stock Market Volatility In The United Kingdom: Simulating Post-Covid-19 Recovery, Bala A. Dahiru, Mohammed Shuaibu, Najibullah Hassanov

CBN Journal of Applied Statistics (JAS)

This paper investigates the time it would take for the FTSE-100 index to reach its post-COVID-19 peak. The paper utilises an exponential generalised autoregressive conditional heteroscedasticity (EGARCH) model that accounts for leverage effect and asymmetries. The preferred models amongst competing variants was the Autoregressive Moving Average (ARMA)-EGARCH(2,1) specification and was used to predict daily FTSE-100 data from 5th January 2000 to 21st June 2024. The empirical exercise showed that the COVID-19-induced financial crisis negatively affected the United Kingdom’s stock market performance. The results show that the FTSE100 index could reach its post-pandemic peak around 27th August, 2024 (two months after …

Physician-Patient Interactions In Online Healthcare Communities: The Effects Of Preconsultation On Service Delivery And Patient Satisfaction, Qian Tang, Anqi Zhao

Research Collection School Of Computing and Information Systems

Preconsultation by medical professionals is a common practice in offline healthcare services to improve consultation efficiency but is rarely adopted for online healthcare services. In a noteworthy departure from this trend, a Chinese online healthcare community (OHC) has instituted preconsultation by assistant physicians prior to online consultations. Using comprehensive service data from this OHC, this study scrutinizes the effects of preconsultation on online healthcare services from both the physician and patient perspectives. The findings reveal that preconsultation by the assistant physician can significantly increase the attending physician’s response speed, length, and provision of informational support, while maintaining a consistent level …

Understanding Perspectives Of Power In A College Mathematics Classroom, Laura Seeberger

Honors College Theses

Power is embedded in teacher and student actions within mathematics classrooms, whether that power is known or not. Incorporating equity and inclusiveness requires a deeper understanding of this power behind actions. In this study, we developed and administered a three part survey questioning college faculty and college students how they perceive power related to common actions within a college mathematics classroom. For example, participants were asked whether a college math teacher assigning homework increased, decreased, or did both to the teacher’s power; then whether this action increased, decreased, or did both to a student’s power. Analysis of 16 faculty and …

Code For Care: Hypertension Prediction In Women Aged 18-39 Years, Kruti Sheth

Electronic Theses, Projects, and Dissertations

The longstanding prevalence of hypertension, often undiagnosed, poses significant risks of severe chronic and cardiovascular complications if left untreated. This study investigated the causes and underlying risks of hypertension in females aged between 18-39 years. The research questions were: (Q1.) What factors affect the occurrence of hypertension in females aged 18-39 years? (Q2.) What machine learning algorithms are suited for effectively predicting hypertension? (Q3.) How can SHAP values be leveraged to analyze the factors from model outputs? The findings are: (Q1.) Performing Feature selection using binary classification Logistic regression algorithm reveals an array of 30 most influential factors at an …