Advanced Statistical Methods
Quantitative Tools for Rigorous Research
At The University of Ontological Science, we recognize the value of sophisticated statistical approaches in addressing complex research questions across the spectrum of ontological inquiry. Whether you’re analyzing patterns in phenomenological data, testing theoretical models of consciousness, or exploring relationships between variables in empirical studies, advanced statistical methods provide powerful tools for generating robust insights. This section outlines the statistical training, resources, and support available to help you develop quantitative expertise that complements your philosophical foundations.
Core Statistical Training
Statistical Methods Sequence
Progressive development of quantitative skills:
- Foundations of Quantitative Research (STAT 501): Essential statistical concepts and approaches for graduate research
- Advanced Statistical Analysis (STAT 602): Beyond basic statistics to sophisticated analytical techniques
- Multivariate Methods in Ontological Science (STAT 610): Analyzing complex, multi-dimensional data relationships
- Bayesian Statistics for Researchers (STAT 625): Probability-based approaches particularly suitable for ontological questions
- Computational Statistics (STAT 640): Programming-based statistical analysis and simulation methods
- Mixed Methods Research Design (STAT 635): Integrating qualitative and quantitative approaches effectively
Specialized Methodology Courses
Focused training for specific research needs:
- Structural Equation Modeling (STAT 615): Testing theoretical constructs and relationships
- Multilevel and Hierarchical Models (STAT 622): Analyzing nested data structures
- Longitudinal Data Analysis (STAT 627): Studying change and development over time
- Time Series Analysis (STAT 631): Examining temporal patterns and relationships
- Network Analysis (STAT 645): Investigating relational structures and connections
- Machine Learning for Researchers (STAT 650): Applying computational approaches to pattern recognition
Short Course Series
Intensive skill development opportunities:
- Two-day workshops on focused statistical topics offered between semesters
- Bootcamp-style immersion courses during summer sessions
- Online mini-courses for flexible skill development
- Hands-on statistical programming intensives
- Special topics courses with visiting methodologists
- Workshop series on emerging statistical approaches
Statistical Software & Computing Resources
Software Training Workshops
Developing practical implementation skills:
- R for Graduate Researchers: From basics to advanced statistical programming
- Python for Data Analysis: Computational approaches to research data
- SPSS Comprehensive Workshop: Accessible statistical analysis platform
- Introduction to JASP: User-friendly Bayesian statistics software
- Mplus for Structural Modeling: Specialized tool for latent variable analysis
- Stan for Bayesian Inference: Powerful platform for probabilistic programming
Computing Infrastructure
Resources supporting sophisticated analyses:
- Dedicated statistical computing lab with specialized software
- High-performance computing cluster for intensive analyses
- Cloud-based statistical platforms accessible remotely
- Virtual computing environment with pre-configured statistical tools
- Statistical software licenses for student installation
- Secure data storage for research projects
Programming Support
Resources for developing technical implementation skills:
- Weekly code clinic drop-in hours for troubleshooting
- Peer programming groups for collaborative learning
- Online repository of example scripts and analyses
- Debugging assistance for statistical programming
- Version control workshops for managing analytical code
- Regular workshops on computational efficiency
Applied Statistical Support
Statistical Consulting Service
Personalized assistance with research applications:
- Individual consultations on study design and analysis planning
- Method selection guidance for specific research questions
- Sample size and power analysis assistance
- Analysis implementation support
- Results interpretation consultations
- Statistical review of manuscripts and dissertations
Research Design Support
Integrating statistics into your research planning:
- Pre-proposal statistical planning consultations
- Methodology sections development for grant applications
- Pilot study design and preliminary analysis
- Development of analysis plans for preregistration
- Selection of appropriate statistical approaches
- Identification of potential methodological challenges
Analysis Implementation Assistance
Hands-on support for conducting analyses:
- Step-by-step guidance for complex statistical procedures
- Troubleshooting assistance for analytical challenges
- Data preparation and cleaning support
- Visualization development for communicating results
- Interpretation frameworks for statistical findings
- Documentation assistance for reproducible analyses
Specialized Applications in Ontological Science
Phenomenological Data Analysis
Quantitative approaches to experiential research:
- Statistical frameworks for analyzing first-person reports
- Coding and quantification of qualitative phenomenological data
- Pattern recognition in experiential descriptions
- Reliability assessment for phenomenological coding
- Mixed methods integration of phenomenological and quantitative data
- Natural language processing of experiential narratives
Consciousness Research Methods
Specialized techniques for mind studies:
- Signal detection theory for perception and awareness studies
- Psychophysical methods for measuring conscious experience
- Computational modeling of consciousness processes
- Statistical approaches to neural correlates of consciousness
- Time-series analysis of state fluctuations
- Network methods for integrated information
Theoretical Model Testing
Statistical approaches to evaluating ontological frameworks:
- Structural equation modeling of theoretical constructs
- Confirmatory factor analysis for validating measurement models
- Path analysis for examining theoretical relationships
- Model comparison techniques for competing frameworks
- Latent class analysis for identifying experiential patterns
- Simulation-based approaches to theoretical exploration
Collaborative Learning Opportunities
Statistical Methods Working Groups
Peer-based learning communities:
- Bayesian Analysis Group: Exploring probabilistic approaches to research
- Mixed Methods Research Circle: Integrating qualitative and quantitative approaches
- R Users Community: Developing skills in statistical programming
- Computational Modeling Collective: Building and testing theoretical models
- Data Visualization Studio: Creating effective visual representations of findings
- Open Science Practitioners: Implementing transparent research practices
Quantitative Research Symposium
Annual showcase of statistical applications:
- Graduate student presentations of innovative methodological approaches
- Poster sessions highlighting quantitative research projects
- Workshops on emerging statistical techniques
- Panel discussions on integrating philosophy and statistics
- Networking with quantitative researchers across disciplines
- Recognition for excellence in statistical applications
Peer Tutoring Program
Support from fellow students:
- One-on-one assistance with statistical concepts and techniques
- Software implementation guidance from experienced peers
- Study groups for statistical methods courses
- Practice problem sessions before exams
- Collaborative learning opportunities
- Mentorship from advanced quantitative students
Resources for Self-Directed Learning
Statistical Methods Resource Library
Comprehensive learning materials:
- Textbooks covering all major statistical approaches
- Video tutorials for common statistical procedures
- Annotated example analyses with code and explanations
- Method-specific guides and cheat sheets
- Practice datasets for developing analytical skills
- Curated articles on statistical applications in ontological science
Online Learning Platforms
Digital resources for flexible skill development:
- Access to premium statistical courses through institutional subscriptions
- Curated pathways through online statistical learning resources
- TUOS-developed modules on specialized topics
- Interactive tutorials for statistical software
- Virtual labs for practicing analytical techniques
- Self-assessment tools for statistical knowledge
Documentation and Guides
Reference materials for implementation:
- Statistical decision trees for method selection
- Comprehensive documentation for software packages
- Style guides for reporting statistical results
- Templates for common analyses
- Checklists for statistical best practices
- Glossary of statistical terminology
Accessing Statistical Support and Resources
The Statistical Methods Center is located in Wittgenstein Hall, Room 305, and offers services Monday through Friday, 9:00 AM to 5:00 PM. Statistical consultants are available by appointment, which can be scheduled through the Graduate Portal or by emailing stats@tuos.edu.
Software and computing resources are accessible through the Statistical Computing Lab (Wittgenstein Hall, Room 310) and remotely via the TUOS Virtual Computing Environment. All major statistical software packages are available on lab computers and through virtual desktop access.
For new graduate students, we recommend:
- Attending the “Statistical Methods Orientation” workshop offered at the beginning of each semester
- Scheduling an initial consultation to discuss your research interests and statistical needs
- Joining at least one statistical methods working group related to your research area
- Exploring the online Statistical Methods Resource Library for self-paced learning materials
At TUOS, we view advanced statistical methods not as separate from philosophical inquiry but as complementary tools that expand our capacity to investigate complex questions about consciousness, reality, and human experience. Whether you’re developing a mixed-methods dissertation, analyzing experimental data, or testing theoretical models, these quantitative approaches can strengthen the rigor and impact of your ontological research.