The National University System Repository exists to increase public access to research and other materials created by students and faculty of the affiliate institutions of National University System. Most items in the repository are open access, freely available to everyone.
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Item Advanced Statistical Concepts(2026-02-06)This is a record for a series of videos that are available on another platform. View the video series here: https://resources.nu.edu/adv-stats Transcripts of video will be avaliable as downloadable files within this record. Transcripts are coming soon. The Advanced Statistics video series includes 10 video lessons and SPSS tutorials focused on the following analyses techniques: (1) Examine data for errors and conduct descriptive statistics using SPSS (2) Examining the main steps of hypothesis testng (3) Analyzing data using bivariate correlation, regression, and t-tests (4) Conducting relationship and prediction analyses between multiple variables using multiple linear regression analyses (5) Analyzing effects between multiple variables using factorial ANOVA, ANCOVA, and MANOVA (6) Conducting Exploratory factor analysis (7) Analyzing data using non-parametric procedures.Item Introduction to Statistical Concepts(2026-02-06)The Introduction to Statistics video series includes 13 video lessons and SPSS tutorials focused on the following analyses techniques: (1) Descriptive vs. Inferential Statistics and conducting statistics using SPSS (2) Develop Graphs and Frequency Distributions for Categorical and Continuous Variables (3) Analyze Measures of Central Tendency and Measures of Variability (4) Examine the Normal Curve and Calculate Standard Scores (5) Determine the Standard Error of the Mean, Confidence Intervals, and Parametric Assumptions (6) Explore Relationships Between Variables Using Correlation, Linear Regression, Multiple Regression, And Chi-Square (7) Examine t-tests and ANOVA. This is a record for a series of videos that are available on another platform. View the video series here: https://resources.nu.edu/intro-stats Transcripts of video will be avaliable as downloadable files within this record. Transcripts are coming soon.Item The Lived Experiences of African American Entrepreneurs: An Interpretative Phenomenological Analysis(2026-01)The underrepresentation of African American entrepreneurs in the United States is a persistent socioeconomic problem that affects wealth creation, employment, and inclusive economic growth. This qualitative, interpretive phenomenological study explored the lived experiences of successful African American entrepreneurs, focusing on the socioeconomic factors that influence their career choices and the strategies they use to overcome systemic barriers. Guided by an interpretive phenomenological framework, the study examined how entrepreneurs navigated business ownership in environments shaped by racial and economic inequities. Data were collected through in-depth, semi-structured interviews with 14 African American entrepreneurs in the Midwestern United States who had sustained their businesses for more than 5 years. Participants were recruited purposively, and interviews were conducted individually via a virtual platform. Data analysis included iterative reading, NVivo coding, and thematic synthesis. Findings revealed persistent challenges, such as limited access to capital, racialized business environments, and structural barriers that restricted opportunities despite education and professional qualifications. Participants emphasized resilience, adaptability, and perseverance, with mentorship, social capital, and community networks emerging as critical support systems. Creative financing strategies and innovation are key mechanisms for sustaining business operations. The study concluded that African American entrepreneurs often rely on personal agency and community resources to overcome institutional barriers. Implications include expanding culturally responsive mentoring, improving equitable financing pathways, and strengthening community-based entrepreneurial networks. Future research should explore regional differences, longitudinal experiences, and comparative studies across diverse populationsItem Gender Differences in ADHD Diagnosis(2025-12)Attention Deficit Hyperactivity Disorder (ADHD) is a neurodevelopmental disorder that impacts 3% of the population worldwide and approximately 11% of children. Most of the research on ADHD has been conducted on samples of predominantly males which has resulted in a lack of research and knowledge on the symptom presentation in females. The goal of this capstone is to address the gap in ADHD research on females, as well as analyze the differences in symptom presentation between males and females. This capstone looks at female ADHD diagnosis through the lens of attachment and family systems theories. A proposal for a group session for professionals that highlights the differences in symptoms between males and females is explained in chapter 3.Item Applications of Machine Learning Algorithms for Examining the Impact of COVID-19 on the Dropout Rate for High Schools in the State of Illinois(2025-12)High school dropout remains a persistent and pressing issue in the United States and globally. This quantitative, non-experimental study aimed to develop a predictive model for high school dropout rates in the state of Illinois before and during the COVID-19 pandemic, covering the years 2017–2019 and 2020–2022, respectively. Publicly available datasets from the Illinois State Board of Education were analyzed using multiple linear regression, random forest, and XGBoost models to assess the impact of the pandemic and to identify which school-level features most strongly predict dropout outcomes. The study applied the CRISP-DM framework and interpreted results through the lens of survival analysis theory to address the problem of academic attrition over time. Research questions and hypotheses were tested using the three predictive models. The analysis identified mobility rate, COVID-19 period, and low-income enrollment as the most influential predictors of high school dropout, with mobility rate emerging as the top signal across models. Model performance was evaluated using R², mean absolute error (MAE), and root mean squared error (RMSE). The XGBoost model offered the best balance of predictive accuracy and computational efficiency, making it the most effective and preferred model for this study. Recommendations for future research are grounded in the study’s predictive scope and methodological limitations. Proposed next steps include evaluating model performance over extended timeframes, incorporating post–COVID-19 data, and exploring additional demographic and school-level predictors using time-aware validation and stratified replication. These extensions will strengthen the generalizability and practical value of predictive modeling for dropout prevention in educational settings.
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