Physical Sciences and Mathematics Commons^™

Using Social Network Analysis To Measure And Visualize Student Clustering Within Middle And High Schools, Geoffrey David West

USF Tampa Graduate Theses and Dissertations

The dominant philosophy of American public schools has been to group students together based on similar characteristics. Known as tracking, high achieving students would take courses on the “college track” while others would take “career track” courses. It was not long until advocates noticed that this process unfairly advantaged affluent and White student over poor and minoritized groups. A new process called “ability grouping” took over where tracking left off, but to the same effect. It is difficult to measure the degree students are grouped together by a certain characteristic, and while a few research papers aim to do so, …

Exploring Time-Varying Extraneous Variables Effects In Single-Case Studies, Ke Cheng

USF Tampa Graduate Theses and Dissertations

The effect of time-varying extraneous variables has been studied in other statistical analyses such as using Kaplan–Meier or Cox regression analysis in survival analyses. Nonetheless, the effect of modeling versus not modeling individual specific time varying extraneous variables has not been explored in multiple-baseline single case designs through Monte Carlo simulation studies. Therefore, in my dissertation, I used simulation methods to explore for a variety of conditions (varying in the number of participants, number of observations per participant, type of extraneous variable effect, size of the true intervention effect) the impact of extraneous variables on bias and standard error of …

Fuzzy Kc Clustering Imputation For Missing Not At Random Data, Markku A. Malmi Jr.

USF Tampa Graduate Theses and Dissertations

Research has a variety of difficulties, especially when involving human subjects, and one of the most prevalent is the issue of missing data. Missing data will always be present in research due to the fact there is no perfect method for collecting data and protecting against human error or mechanical failure. This requires researchers to be able to mitigate the problems that come along with missing data; reduction in power of an analysis and bias introduced by the missing pattern. This research investigated a non-parametric method using a nested approach of fuzzy K-Modes and fuzzy C-Means clustering to impute missing …

Beyond Statistical Significance: A Holistic View Of What Makes A Research Finding "Important", Jane E. Miller

Numeracy

Students often believe that statistical significance is the only determinant of whether a quantitative result is “important.” In this paper, I review traditional null hypothesis statistical testing to identify what questions inferential statistics can and cannot answer, including statistical significance, effect size and direction, causality, generalizability, and changeability of the independent variable. I illustrate these issues with examples from an empirical study of the association between how much time teenagers spent playing video games and time spent reading. I describe how study design and context determine each of those aspects of “importance,” and close by summarizing how to provide a …

Establishing The Validity And Reliability Of The Locus Assessments, Tim Jacobbe, Bob Delmas, Brad Hartlaub, Jeff Haberstroh, Catherine Case, Steven Foti, Douglas Whitaker

Numeracy

The development of assessments as part of the funded LOCUS project is described. The assessments measure students’ conceptual understanding of statistics as outlined in the GAISE PreK–12 Framework. Results are reported from a large-scale administration to 3,430 students in grades 6 through 12 in the United States. Items were designed to assess levels of understanding as well as components of the statistical problem solving process as articulated in the GAISE framework. We discuss details of how the model used to develop the LOCUS assessments guided the gathering of evidence for validity and reliability arguments. Three types of validity evidence are …

Association Between Use Of Remdesivir And Bradycardia, Gibret Umeukeje

USF Tampa Graduate Theses and Dissertations

Remdesivir received the first emergency use authorization from the FDA for the treatment of COVID-19. Multiple adverse drug reactions (ADR) have been reported since its approval in October 2020. Bradycardia, defined by a decrease in heart rate has been reported as an adverse event for patients receiving remdesivir for COVID-19 treatment. The purpose of the research is to systematically investigate the frequency of occurrence of bradycardia in adults receiving remdesivir using clinical data derived from the FDA Adverse Event Reporting System (FAERS) database. Patients receiving remdesivir were compared to those receiving Paxlovid, Regen-Cov, and Dexamethasone for COVID-19 treatment to see …

An Attempt To Develop A Measurement Tool For Interpretation Performance Of Tourist Guides, Gizem Capar, Dilek Atci

University of South Florida (USF) M3 Publishing

The search for different experiences in touristic visits brings the necessity of differentiating the tours for tour guides with. Interpretation lies at the heart of this differentiation. This research aims to examine the structure of interpretation performance of tour guides empirically within the framework of E.R.O.T/T.O.R.E model. For this purpose, in line with the literature firstly conceptual structure of interpretation performance and interpretative guiding was determined, then expert opinion was sought with the expression pool consisting of draft statements. After expertising process, the measurement tool was first applied on a sample of 191 participants. For preliminary analysis the performance of …

Talking About Statistical Significance In Numeracy, Nathan D. Grawe, Gizem Karaali

Numeracy

In recent years, much debate has surrounded the potential for audiences to be mislead by several common practices when reporting statistical significance tests. Two editors of Numeracy share the journals perspectives on these questions. As an interdisciplinary journal, we recognize and honor the genre differences represented by our authors and audience members. As a consequence, the journal is open to many practices. Still, we acknowledge the concerns raised by the American Statistical Association and others and encourage authors to write with care and clarity, however results may be represented.

Data-Driven Analytical Predictive Modeling For Pancreatic Cancer, Financial & Social Systems, Aditya Chakraborty

USF Tampa Graduate Theses and Dissertations

Pancreatic cancer is one of the most deathly disease and becoming an increasingly commoncause of cancer mortality. It continues giving rise to massive challenges to clinicians and cancer researchers. The combined five-year survival rate for pancreatic cancer is extremely low, about 5 to 10 percent, owing to the fact that a large number of the patients are diagnosed at stage IV when the disease has metastasized. Our study investigates if there exists any statistical significant difference between the median survival times and also the survival probabilities of male and female pancreatic cancer patients at different cancer stages, and irrespective of …

Nonparametric Estimation Of Transition Probabilities In Illness-Death Model Based On Ranked Set Sampling, Ying Ma

USF Tampa Graduate Theses and Dissertations

The ranked set sampling (RSS) design is applied widely in agriculture, environmental science, and medical research where the exact measurements of sampling units is costly, but sampling units can be ranked by a correlated concomitant variable. RSS is usually a cost-efficient alternate to simple random sampling (SRS) for selecting more representative samples. This study presents a novel methodology to investigate the nonparametric estimation of transition probabilities in illness-death model using the RSS design. We study the Aalen–Johansen estimator of transition probabilities in illness-death Markov model based on RSS design under random right censoring time and propose nonparametric estimators of the …

New Developments In Statistical Optimal Designs For Physical And Computer Experiments, Damola M. Akinlana

USF Tampa Graduate Theses and Dissertations

Statistical design of experiments allows for multiple factors influencing a process to be systematically manipulated in an experiment, and their effects on the output of the process to be studied via statistical modeling and analysis. Classical designs offer general nice performance but have limited applications due to restricted design size, region, and randomization structure. Computer generated optimal designs become more popular in recent decades due to the rapid growth in computing power. Most existing work in optimal design of experiments involves designing experiments with optimal performance on a single chosen objective or a single response. However, with the increasing limitation …

Video Anomaly Detection: Practical Challenges For Learning Algorithms, Keval Doshi

USF Tampa Graduate Theses and Dissertations

Anomaly detection in surveillance videos is attracting an increasing amount of attention. Despite the competitive performance of several existing methods, they lack theoretical performance analysis, particularly due to the complex deep neural network architectures used in decision making. Additionally, real-time decision making is an important but mostly neglected factor in this domain. Much of the existing methods that claim to be online, depend on batch or offline processing in practice. Furthermore, several critical tasks such as continual learning, model interpretability and cross-domain adaptability are completely neglected in existing works. Motivated by these research gaps, in this dissertation we discuss our …

A Functional Optimization Approach To Stochastic Process Sampling, Ryan Matthew Thurman

USF Tampa Graduate Theses and Dissertations

The goal of the current research project is the formulation of a method for the estimation and modeling of additive stochastic processes with both linear- and cycle-type trend components as well as a relatively robust noise component in the form of Levy processes. Most of the research in stochastic processes tends to focus on cases where the process is stationary, a condition that cannot be assumed for the model above due to the presence of the cyclical sub-component in the overall additive process. As such, we outline a number of relevant theoretical and applied topics, such as stochastic processes and …

Using Fine-Scale Aquatic Habitat Data To Construct Dreissenid Sdms In The Laurentian Great Lakes, Grace C. Henderson

USF Tampa Graduate Theses and Dissertations

The invasion of the Laurentian Great Lakes by aquatic invasive species (AIS) has been the subject of investigation for decades, due to their dramatic alterations to the ecosystem and high economic costs. Two AIS with the largest impacts are dreissenid zebra and quagga mussels, and though these species have been studied extensively, questions remain about what factors control their distributions, and whether lake warming will alter these distributions. Species distribution models (SDMs) offer a powerful tool to examine the relationship between species presences and environmental variables, which are typically bioclimactic data. The creation of the Aquatic Habitat (AqHab) dataset containing …

Measurements Of Generalizability And Adjustment For Bias In Clinical Trials, Yuanyuan Lu

USF Tampa Graduate Theses and Dissertations

While randomized controlled trials (RCTs) are widely used as a gold standard in clinical research and public health, they are criticized because of a potential lack of generalizability, as the trial patients may be unrepresentative of the target patient population. Few research addresses how to assess and evaluate the generalizability of RCTs. As we know, patients are rarely selected on a random basis from a well-defined patient population of interest into a clinical trial. Generalizing findings from the RCT samples to the patient population has begun to receive increasing attention. We simulate a patient population with treatment effect size of …

How The Number Line Can Be Used To Promote Students' Understanding Of The Normal Distribution, Danri H. Delport

Numeracy

A strong foundation in early number concepts is crucial for students’ future success in statistics. Despite its importance in statistics, many first-year students struggle to comprehend the normal distribution due to a lack of basic number sense. Students get confused about the order and magnitude of negative z-scores on a standard normal curve or when problems about normally distributed random variables are presented in word questions which involve phrases that indicate inequalities. As a result, students shade wrong areas on the bell-shaped curve when they have to calculate probabilities for normally distributed variables. Visual representations such as the number …

Author’S Reflections On Making Sense Of Numbers: Quantitative Reasoning For Social Research, Jane E. Miller

Numeracy

Miller, Jane E. 2021. Making Sense of Numbers: Quantitative Reasoning for Social Research. (Los Angeles: SAGE Publications) 608 pp. ISBN 978-1544355597.

This article introduces and provides an excerpt from Making Sense of Numbers: Quantitative Reasoning for Social Research, published by Sage. The book explains and illustrates how making sense of numbers involves integrating concepts and skills from mathematics, statistics, study design, and communications, along with information about the specific topic and context under study. It teaches how to avoid making common errors of logic, calculation, and interpretation by introducing a systematic approach and a healthy dose of skepticism …

Uncertainty Quantification In Deep And Statistical Learning With Applications In Bio-Medical Image Analysis, K. Ruwani M. Fernando

USF Tampa Graduate Theses and Dissertations

Deep Learning (DL) has achieved the state-of-the-art performance across a broad spectrum oftasks. From a statistical standpoint, deep neural networks can be construed as universal function approximators. Although statistical modeling and deep learning methods are well-established as independent areas of research, hybridization of the two paradigms via probabilistic deep networks is an emerging trend. Through development of novel analytical methods under the statistical and deep-learning framework, we address some of the major challenges encountered in the design of intelligent systems which include class imbalance learning, probability calibration, uncertainty quantification and high dimensionality. When modeling rare events, existing methodologies require re-sampling …

Differential Privacy For Regression Modeling In Health: An Evaluation Of Algorithms, Joseph Ficek

USF Tampa Graduate Theses and Dissertations

Background: There is a need for rigorous and standardized methods of privacy protection for shared data in the health sciences. Differential privacy is one such method that has gained much popularity due to its versatility and robustness. This study evaluates differential privacy for explanatory regression modeling in the context of health research.

Methods: Surveyed and newly proposed algorithms were evaluated with respect to the accuracy (bias and RMSE) of coefficient estimates, the empirical coverage probability of confidence intervals, and the power and type I error rates of hypothesis tests. Evaluations took place in both simulated and real data from a …

Online And Adjusted Human Activities Recognition With Statistical Learning, Yanjia Zhang

USF Tampa Graduate Theses and Dissertations

Wearable human activity recognition (HAR) is a widely application system for our daily life. It hasbeen built in many devices, such as smartphone, smartwatch, activity tracker, and health monitor. Many researchers try to develop a system which requires less memory space and power, but has fast and accurate classification results. Moreover, the objective of adjusting the classifier by the system self is also a study direction. In the present study, we introduced the machine learning methods to both smartphone data and smartwatch data and an adjusted model with the continuous generating data. Further, we also proposed a new HAR system …

An Introduction To Calling Bullshit: Learning To Think Outside The Black Box, Jevin D. West, Carl T. Bergstrom

Numeracy

Bergstrom, Carl T. and Jevin D. West. 2020. Calling Bullshit: The Art of Skepticism in a Data-Driven World. (New York: Random House) 336 pp. ISBN 978-0525509202.

While statistical methods receive greater attention, the art of critically evaluating information in everyday life more commonly depends on thinking outside the black box of the algorithm. In this piece we introduce readers to our book and associated online teaching materials—for readers who want to more capably call “bullshit” or to teach their students to do the same.

Do Different Relevance Attributes Indicate The Same Conservation Priorities? A Case Study In Caves Of Southeastern Brazil, Maysa F.V.R. Souza, Denizar A. Alvarenga, Marconi Souza-Silva, Rodrigo L. Ferreira

International Journal of Speleology

In the last decade, the scientific community brought to the debate gaps that slow down the advance of knowledge regarding global biodiversity. More recently, this discussion has reached subterranean environments, where these gaps are even more dramatic due to the relict and vulnerable nature of their species. In this context, we tested ecological metrics related to some of these gaps, checking if the biological relevance of the caves would change depending on ecological attributes related to each metric. The study was carried out in caves from southeastern Brazil, located in a region presenting a high richness of troglobitic species restricted …

Be Careful! That Is Probably Bullshit! Review Of Calling Bullshit: The Art Of Skepticism In A Data-Driven World By Carl T. Bergstrom And Jevin D. West, James B. Schreiber

Numeracy

Bergstrom, C. T., & West, J. D. 2021. Calling Bullshit: The Art of Skepticism in a Data-Driven World. NY: Random House. 336 pp. ISBN 978-0525509189

The authors provide a journey through the numerical bullshit that surrounds our daily lives. Each chapter has multiple examples of specific types of bullshit that each of us experience on any given day. Most importantly, information on how to identify bullshit and refute it are provided so that reader finishes the book with a set of skills to be a more engaged and critical interpreter of information. The writing has a quick and lively …

Development And Validation Of A Scale To Measure Songwriting Self-Efficacy (Sses) With Secondary Music Students, Patrick K. Cooper

USF Tampa Graduate Theses and Dissertations

Social cognitive theory was developed to explain how individuals learn, in part, by witnessing the behavior of others. Self-efficacy is a construct within social cognitive theory which indicates the beliefs that an individual can be successful at a task under specific situational demands. The sources of self-efficacy include self-evaluating past experiences to predict future success, comparing our abilities to those around us, the verbal and social feedback we get from others, and the physiological feelings we experience when engaged in or thinking about the task. Measures of self-efficacy have been shown to be accurate predictors of successful learning outcomes, achievement, …

Bayesian Multivariate Joint Modeling For Skewed-Longitudinal And Time-To-Event Data, Lan Xu

USF Tampa Graduate Theses and Dissertations

In epidemiologic and clinical studies, a relatively large number of biomarkers are repeatedly measured in patients over time, often associated with data on epidemiologic and clinical interest events. So, much attention is focused on developing the specific patterns of the longitudinal measurements, and the associations between those patterns and the time to a certain event, such as heart attack, diagnose of disease, time to transplantation, or death. In the last two decades, the research into joint modeling of longitudinal and time-to-event data has received a tremendous amount of attention.

Numerous researchers have proposed joint modeling approaches for a single longitudinal …

Data-Driven Analytical Modeling Of Multiple Myeloma Cancer, U.S. Crop Production And Monitoring Process, Lohuwa Mamudu

USF Tampa Graduate Theses and Dissertations

Globally, cancer disease is a major health issue causing a lot of deaths. The duration of time an individual diagnosed with a particular type of cancer survives has become a major area of research concern. The Kaplan Meier and Cox Proportional Hazard (Cox-PH) model have been a traditionally used method for survival analysis of cancer data. These techniques of cancer survival analysis are developed from nonparametric and semi-parametric approaches, respectively, which are not as robust as a parametric approach. In this dissertation, we proposed a new method of cancer survival analysis based on a parametric approach using multiple myeloma cancer …

Confidence Intervals Of Covid-19 Vaccine Efficacy Rates, Frank Wang

Numeracy

This tutorial uses publicly available data from drug makers and the Food and Drug Administration to guide learners to estimate the confidence intervals of COVID-19 vaccine efficacy rates with a Bayesian framework. Under the classical approach, there is no probability associated with a parameter, and the meaning of confidence intervals can be misconstrued by inexperienced students. With Bayesian statistics, one can find the posterior probability distribution of an unknown parameter, and state the probability of vaccine efficacy rate, which makes the communication of uncertainty more flexible. We use a hypothetical example and a real baseball example to guide readers to …

Combination Of Time Series Analysis And Sentiment Analysis For Stock Market Forecasting, Hsiao-Chuan Chou

USF Tampa Graduate Theses and Dissertations

The goal of this research is to build a model to predict trend of financial asset price using sentiment from news headlines and financial indicators of the asset. Objective of the model is to conclude good results but also to minimize the difference between predicted values and actual values. Unlike previous approaches where the sentiments are usually calculated into score, we focus on combination of word embedding of news and financial indicators due to nonavailability of sentiment lexicon.

One idea is that the sentiment of news headline should have impact on financial asset val- ues. In other words, it would …

The General Psychopathology Factor (P) From Adolescence To Adulthood: Disentangling The Developmental Trajectories Of P Using A Multi-Method Approach, Alexandria M. Choate

USF Tampa Graduate Theses and Dissertations

Considerable attention is directed towards studying co-occurring psychopathology through the lens of a general factor (p-factor). However, the developmental trajectories and stability of the p-factor have yet to be fully understood. Study 1 first examined the explanatory power of dynamic mutualism theory — an alternative framework positing the p-factor to be a product of lower-level symptom interactions rather than the inherent cause of them. Predictions of dynamic mutualism were tested using three distinct statistical approaches including: longitudinal bifactor models, random-intercept cross-lagged panel models (RI-CLPMs), and network models. Next, given prior suggestions that borderline personality disorder (BPD) could be a marker …

Computing For Numeracy: How Safe Is Your Covid-19 Social Bubble?, Charles Connor

Numeracy

The COVID-19 pandemic has led many people to form social bubbles. These social bubbles are small groups of people who interact with one another but restrict interactions with the outside world. The assumption in forming social bubbles is that risk of infection and severe outcomes, like hospitalization, are reduced. How effective are social bubbles? A Bayesian event tree is developed to calculate the probabilities of specific outcomes, like hospitalization, using example rates of infection in the greater community and example prior functions describing the effectiveness of isolation by members of the social bubble. The probabilities are solved for two contrasting …