Sam Chijioke Nnorom
Adjunct
What I do
I lead the connection between academic instruction and applied research in social work and public health. At GSSW, I teach and coordinate graduate-level courses in research and evaluation, ensuring students develop a strong foundation in methodologies, data analysis, and evidence-based practices. Outside the classroom, I oversee a team of senior research and data analysts working on a federally funded initiative, where we implement rigorous evaluation frameworks to boost program success and inform policy. My work links theory and practice, empowering future social workers with essential research skills while advancing impactful, data-driven solutions in the field.Specialization(s)
As an educator and researcher, my expertise encompasses applied statistics, program evaluation, with a focus on causal-comparative designs and advanced statistical modeling techniques, manage teams of analysts, I aim to advance education, social work, and public health initiatives.
Professional Biography
Academic Leader | Applied Statistician | Public Health & Evaluation Expert
Dr. Samuel Nnorom is a strategic academic leader and data science expert with over a decade of experience spanning higher education, public health, and organizational analytics. He specializes in applied statistics and research methodology, with a strong commitment to integrating evidence-based practices into clinical and educational settings.
At the Graduate School of Social Work (GSSW), Dr. Nnorom teaches and coordinates graduate-level courses in research and evaluation, ensuring students gain a robust foundation in quantitative methods, data analysis, and program evaluation. His leadership extends beyond the classroom; he manages a team of senior research and data analysts working on a federally funded initiative focused on rigorous evaluation and evidence-informed decision-making. This dual role allows him to bridge academic theory with real-world application, empowering future social workers to become critical thinkers and data-driven practitioners.
Dr. Nnorom’s career includes impactful roles at the Georgia Department of Public Health and the Colorado Department of Human Services. Across these positions, he has led strategic planning, developed predictive models, built data governance frameworks, and supported accreditation and compliance efforts.
His technical proficiency spans various tools and platforms, including Power BI, Tableau, R, SAS, SQL, Snowflake, Azure, and AWS. He is known for his ability to translate complex data into actionable insights, foster cross-functional collaboration, and drive institutional effectiveness.
Beyond his professional pursuits, Dr. Nnorom is an amateur chef who enjoys exploring and sharing the rich flavors of authentic Nigerian cuisine with family and friends. This passion for food and culture adds a layer of warmth and approachability to his academic and analytical persona.
Dr. Samuel Nnorom is a strategic academic leader and data science expert with over a decade of experience spanning higher education, public health, and organizational analytics. He specializes in applied statistics and research methodology, with a strong commitment to integrating evidence-based practices into clinical and educational settings.
At the Graduate School of Social Work (GSSW), Dr. Nnorom teaches and coordinates graduate-level courses in research and evaluation, ensuring students gain a robust foundation in quantitative methods, data analysis, and program evaluation. His leadership extends beyond the classroom; he manages a team of senior research and data analysts working on a federally funded initiative focused on rigorous evaluation and evidence-informed decision-making. This dual role allows him to bridge academic theory with real-world application, empowering future social workers to become critical thinkers and data-driven practitioners.
Dr. Nnorom’s career includes impactful roles at the Georgia Department of Public Health and the Colorado Department of Human Services. Across these positions, he has led strategic planning, developed predictive models, built data governance frameworks, and supported accreditation and compliance efforts.
His technical proficiency spans various tools and platforms, including Power BI, Tableau, R, SAS, SQL, Snowflake, Azure, and AWS. He is known for his ability to translate complex data into actionable insights, foster cross-functional collaboration, and drive institutional effectiveness.
Beyond his professional pursuits, Dr. Nnorom is an amateur chef who enjoys exploring and sharing the rich flavors of authentic Nigerian cuisine with family and friends. This passion for food and culture adds a layer of warmth and approachability to his academic and analytical persona.
Degree(s)
- Ph.D., Research Methods & Statistics, University of Denver , 2025
- MPH, Public Health Epidemiology, Liberty University, 2017
Research
My research focuses on advancing evidence-based practices through rigorous statistical analysis and program evaluation, particularly within public health, education, and social work. I specialize in causal-comparative and ex post facto research designs, applying both quantitative and mixed methods approaches to uncover insights from complex datasets. A central goal of my work is to bridge the gap between research and practice by promoting data-informed decision-making and enhancing institutional effectiveness. I have led large-scale evaluations for government agencies, developed predictive analytics frameworks, and taught graduate-level courses in research methodology and data visualization.
One of my noteworthy research projects involved examining the comparative performance of Ordinary Least Squares (OLS) and Quantile Regression (QR) in modeling conditional relationships. Using mortality data from the Organization for Economic Co-operation and Development (OECD), I explored how age influenced weekly death counts across the full distribution of outcomes. While OLS estimated the average effect of age, it failed to account for distributional heterogeneity. In contrast, QR uncovered distinct patterns by estimating effects at various quantiles, offering a more nuanced understanding of age-related mortality trends. This study highlighted the limitations of relying solely on mean-based models and demonstrated the value of methodological pluralism in capturing variability and enhancing causal inference in social science research.
Other projects include leading large-scale evaluations for state agencies, developing data governance frameworks, and designing predictive models to support strategic planning and workforce development. I have also taught graduate-level courses in research methodology and data visualization, mentoring students in applying statistical techniques to real-world challenges. Through these efforts, I aim to bridge the gap between research and practice, promote data-informed decision-making, and enhance institutional effectiveness across sectors.
One of my noteworthy research projects involved examining the comparative performance of Ordinary Least Squares (OLS) and Quantile Regression (QR) in modeling conditional relationships. Using mortality data from the Organization for Economic Co-operation and Development (OECD), I explored how age influenced weekly death counts across the full distribution of outcomes. While OLS estimated the average effect of age, it failed to account for distributional heterogeneity. In contrast, QR uncovered distinct patterns by estimating effects at various quantiles, offering a more nuanced understanding of age-related mortality trends. This study highlighted the limitations of relying solely on mean-based models and demonstrated the value of methodological pluralism in capturing variability and enhancing causal inference in social science research.
Other projects include leading large-scale evaluations for state agencies, developing data governance frameworks, and designing predictive models to support strategic planning and workforce development. I have also taught graduate-level courses in research methodology and data visualization, mentoring students in applying statistical techniques to real-world challenges. Through these efforts, I aim to bridge the gap between research and practice, promote data-informed decision-making, and enhance institutional effectiveness across sectors.
Areas of Research
Applied statistics
research methods
program evaluation
evidence-based practice
causal-comparative research
quantile regression
ordinary least squares regression
Mixed Methods Research
educational measurement
Psychometrics
public health analytics
mortality analysis
health disparities
policy evaluation
strategic planning
institutional effectiveness
accreditation processes
curriculum design
faculty development
graduate education
online learning
data visualization
predictive analytics
outcome measurement
data governance
compliance reporting
stakeholder engagement
cross-functional collaboration
federal grant management
budget planning
RFP development
research dissemination
academic presentations
statistical modeling
survey design
longitudinal data analysis
data-informed decision-making
democratized AI access
social work research
Community-based Research
technical proficiency
Power BI
Tableau
R
SAS
SPSS
SQL
Snowflake
Dataiku DSS
Azure
AWS
