Focus of Study Certificates

This doctoral seminar provides an in-depth examination of the dialectical relationship between formal sociological theory and applied/clinical practice, aligned with CAPACS Standard 3.11. Students analyze how classical, modern, and middle-range sociological theories (including micro-, meso-, and macro-level frameworks) inform client interventions, organizational consultancies, and community-based social change. The course challenges students to move beyond abstract intellectualism by utilizing theoretical models as diagnostic tools and action frameworks, while critically reflecting on how practice outcomes refine, adapt, or challenge existing theoretical paradigms.

Aligned with CAPACS Standard 3.12, this doctoral course equips students with advanced quantitative, qualitative, and mixed-methods research methodologies tailored for applied and clinical settings. Students explore the full evaluation lifecycle from preliminary needs assessments and baseline diagnostics to formative process evaluations and summative impact studies. Emphasizing data-driven decision-making, the course prepares scholar-practitioners to rigorously assess social programs, organizational interventions, and community initiatives while navigating the practical constraints of real-world research environments.

Aligned with CAPACS Standard 3.13, this course focuses on the art and science of clinical sociological intervention. Students master multi-level problem-solving techniques designed to facilitate constructive social change across micro (individual/family), meso (group/organization), and macro (community/societal) systems. The curriculum examines specialized practice modalities—including sociotherapy, conflict resolution, organizational restructuring, community mediation, and policy reform to equip scholar-practitioners to diagnose systemic dysfunction, design evidence-based corrective strategies, and lead complex change management processes.

Aligned with CAPACS Standard 3.14, this doctoral seminar explores the ethical, moral, and values-based dimensions of applied and clinical sociology. The course examines professional codes of ethics (including CAPACS, AACS, and ASA standards), ethical decision-making models, client confidentiality, informed consent, and human subjects protection. Simultaneously, it integrates a Christian perspective on human dignity (Imago Dei), social justice, and professional stewardship. Students critically analyze power dynamics, practitioner bias, client autonomy, and potential iatrogenic harms in clinical practice.

Aligned with CAPACS Standard 3.15, this doctoral course develops advanced competencies in translating complex sociological findings, diagnostic evaluations, and strategic intervention plans into accessible formats for diverse non-academic audiences. Scholar-practitioners learn to communicate effectively with clients, community members, corporate executives, board members, and policymakers. The course covers strategic report writing, RFP (Request for Proposals) response engineering, public advocacy, conflict translation, and cross-cultural communication, ensuring that sociological insights lead to informed decision-making and sustainable action.

Aligned with CAPACS Standard 3.16, this doctoral course provides the structured framework for the student's field-based practice, clinical practicum, or experiential capstone project. Integrating all prior doctoral learning, students engage in supervised practice within an approved organizational, clinical, or community setting. Under the dual guidance of an academic advisor and an experienced site supervisor, students apply sociological theories, research methods, and intervention strategies to real-world challenges. The course emphasizes professional role integration, self-reflective practice, field accountability, and the synthesis of scholar-practitioner identity.

This doctoral seminar explores the social dimensions of health, illness, and healthcare systems. The course critically examines the processes of medicalization, whereby human conditions become defined and treated as medical problems, and their counterpart, demedicalization. A central focus is analyzing persistent inequities in healthcare access, quality, and outcomes based on social factors such as race, class, gender, and geographic location. Furthermore, the course investigates the complex dynamics between religion, stigma, and the medicalization of sensitive or contested areas to understand how cultural and religious beliefs intersect with medical definitions and social responses.

This seminar applies essential statistical methods for medical sociology and introduces basic statistical applications relevant to the field. Students learn the one-sample t-test, ANOVA, and the chi-squared test of independence. Emphasis is placed on applying these techniques to sociological questions in health. Students will analyze real-world data and evaluate published research. The course prepares students to use these statistical tools in their research.

This doctoral seminar explores the multifaceted nature of clinical reasoning and judgment in various social professions (clinical sociology, included). The course centers on Christine Tanner's influential model of clinical judgment (Noticing, Interpreting, Responding, Reflecting) as a framework for understanding the cognitive and intuitive processes practitioners use. Simultaneously, it engages critically with Michel Foucault's concept of the "clinical gaze," analyzing its historical emergence and the dynamics of power, knowledge, and objectification embedded within institutionalized assessment and intervention practices. Students will explore the implications of these perspectives for ethical practice and for conducting social research on professional decision-making.

This doctoral seminar provides an advanced investigation of James Grier Miller’s Living Systems Theory (LST) as a rigorous analytical framework within the broader tradition of General Systems Theory. Students will dissect the functional architecture of social systems (from small groups to supranational entities) to identify the universal processes that sustain social life or lead to systemic entropy. Miller's 20 critical subsystems will be integrated with contemporary sociological theory to evaluate the tension between systemic efficiency and human agency. The seminar equips doctoral researchers with the methodological tools to operationalize LST in the design and interpretation of complex social research.

This seminar introduces doctoral scholars to data science methods relevant to social research. The course focuses on predictive and classification algorithms. Students will implement supervised machine learning algorithms such as linear regression (predictive), logistic regression (classification), and k-nearest neighbors (classification), and unsupervised machine learning using k-means clustering (classification). Students will learn the theoretical background of these methods and their application to social science data analysis. The course emphasizes practical implementation and interpretation of results. Students will learn to analyze and model social phenomena using data science techniques.

This seminar introduces students to methods for analyzing qualitative data in social research. The course focuses on practical coding techniques. Students will learn to code qualitative data by keyword to identify themes. Sentiment analysis coding for understanding emotional tones in text will be covered. Basic Natural Language Processing (NLP) concepts, such as Term Frequency-Inverse Document Frequency (TF-IDF), will be introduced to analyze qualitative data. The course emphasizes systematic approaches to qualitative data analysis and interpretation. Students will learn to analyze textual and non-numeric data relevant to social research questions.

This seminar introduces fundamental principles and techniques of data visualization for social research. The course emphasizes the creation of clear and effective visual representations of data. Students will learn to construct simple yet informative visualizations, including histograms for distributions, line charts for trends, scatterplots for relationships, bar charts for comparisons, and pie charts for proportions. The course focuses on best practices for visual design and interpretation within the context of social science data. Students will gain skills to communicate findings and explore patterns through visual means.

This doctoral seminar introduces the technical and ethical architecture of artificial intelligence. Framed by IBM’s Five Pillars of Trustworthy AI (Explainability, Fairness, Robustness, Transparency, and Privacy), the course includes technological foundations for AI implementation and ethical implications. The course includes a technicalical introduction to machine learning algorithms, artificial neural networks, deep learning, large language models, predictive and decision-making AI, and computer vision. Students will complete the IBM SkillsBuild "AI Foundations" credential to establish a technical baseline before analyzing an AI implementation through the five pillars.

This doctoral seminar introduces the technical and ethical architecture of artificial intelligence. Framed by IBM’s Five Pillars of Trustworthy AI (Explainability, Fairness, Robustness, Transparency, and Privacy), the course includes technological foundations for AI implementation and ethical implications. The course includes a technicalical introduction to machine learning algorithms, artificial neural networks, deep learning, large language models, predictive and decision-making AI, and computer vision. Students will complete the IBM SkillsBuild "AI Foundations" credential to establish a technical baseline before analyzing an AI implementation through the five pillars.

This doctoral seminar explores the social dimensions of artificial intelligence and its impact on human structures. The course critically examines the processes of algorithmic mediation, whereby human interactions and social opportunities become defined and governed by automated systems. A central focus is on analyzing persistent inequities in data representation and algorithmic outcomes based on social factors such as race, class, gender, and geographic location. The course investigates the complex dynamics between digital labor, social stratification, and the "automation of inequality" to understand how technological systems intersect with power dynamics and social responses.

This doctoral seminar investigates the intersection of Artificial Intelligence with theological anthropology, spiritual practice, and the metaphysical claims of the tech industry. The course focuses on "debunking" the quasi-religious narratives of AI "consciousness," "sentience," and the quest for Artificial General Intelligence (AGI). Contrasting the Imago Dei (Image of God) with the "Stochastic Parrot," students will critically examine the materialist reductionism that equates the human spirit with data processing. A central focus is placed on the ethical responsibility to demystify AI, reclaiming human agency from the "idolatry of the algorithm" and the secular eschatology of transhumanism.

In this doctoral seminar, scholar-practitioners will evaluate the structural paradigms of governance frameworks, international regulatory mandates, and ethical standards that regulate artificial intelligence architectures across diverse institutional contexts. Students will analyze lifecycle risk mitigation protocols, algorithmic auditing strategies, and continuous compliance frameworks designed to protect data privacy and eliminate algorithmic bias. Students will synthesize human-centered ethical theories with organizational strategy to align machine learning technologies with institutional values, civil liberties, and public trust. Students will formulate actionable governance models and policy implementations that empower scholar-practitioners to lead ethical transformation and oversee responsible innovation within their respective professional sectors.