Thank you for visiting my page. I am an Associate Professor in the Department of Statistics at the University of Virginia.
My scholarship aims to understand issues around the coordinated analysis of data in a wide variety of forms, and to develop conceptions of statistical knowledge and accompanying statistical practices that are amenable to integration with alternative modes of inquiry. These modes include varieties of qualitative inquiry, mixed methods research, and epistemologies that radically re-envision the notion of empiricism. I understand statistics as just one set of methodologies within an ecology of methodologies, each with its own approaches to such matters as the contextualization of knowledge, the role of ethical criteria, and the challenges of contributing to public and institutional policy. I am especially interested in the use of statistical methods in socially-inclusive research. This work overlaps at a methodological level with my interests in Bayesian statistics, wherein I have developed techniques for model choice (e.g., in high-dimensional analysis and clustering problems) and the communication of strength-of-evidence, among other specialized areas. Some of my older work examines issues in functional data analysis and classical shrinkage estimation.
Recent papers, preprints, and presentations, most of which are accessible through the links appearing at the bottom of this page, are as follows:
Meixner, C., Spitzner, D. J., and Rose, K. (July 17, 2026). Positionality-in-Diffraction: Rethinking Reflexive Practice Through Relational Entanglement [Presentation]. Roundtable Presentation to the Society for Qualitative Inquiry in Psychology 2026 Annual Meeting at Duquesne University in Pittsburgh, Pennsylvania, USA
Spitzner, D. J. (2026+). Cognitive maps and the quantitative-qualitative divide: Understanding research methodologies through characterizations of the quantitative and the qualitative [Under review after invited revision]
Spitzner, D. J. (June 27, 2025). Cognitive maps and the quantitative-qualitative divide [Presentation]. Presentation to the 24th Biennial International Conference of the Society for Philosophy and Technology in Eindhoven, the Netherlands
Spitzner, D. J. (October 19, 2024). Socially-aware foundations of statistics [Presentation]. Presentation to the 2024 Southeastern Ethics and Philosophy of Technology Workshop in Charlotte, North Carolina, USA
Spitzner, D. J. (March 1, 2024). Statistical practice under a qualitative mental model [Presentation]. Presentation to the Qualitative Report 15th Annual Conference in Fort Lauderdale, Florida, USA
Spitzner, D. J. (April 6, 2023). Statistical practice under a qualitative mental model [Presentation]. Presentation to the 7th International Qualitative Research in Management and Organization Conference in Albuquerque, New Mexico, USA
Spitzner, D. J. (June 14, 2023). Recent methodological advances in Bayes factors for use in forensic analysis and reporting [Presentation]. Presentation to the 11th International Conference on Forensic Inference and Statistics in Lund, Skåne County, Sweden
Spitzner, D. J. (February 18, 2021). Decolonizing statistical analysis [Presentation]. Presentation to the 11th Annual African, African American & Diaspora Studies Interdisciplinary Conference
Spitzner, D. J. (September 9, 2020). Socially-inclusive foundations of statistics [Presentation]. Presentation to the Humanities Informatics Lab Final Showcase: Human & Machine Intelligence
A selection of my other favorite published papers are as follows:
Spitzner, D. J., and Meixner, C. (2023). Mixed methods research in global public health. In P. Liamputtong (Ed.), Handbook of Social Sciences and Global Public Health. Cham: Springer. DOI: 10.1007/978-3-030-96778-9_52-1.
Spitzner, D. J. (2023b). Upending quantitative methodology for use in global public health. In P. Liamputtong (Ed.), Handbook of Social Sciences and Global Public Health. Cham: Springer. DOI: 10.1007/978-3-030-96778-9_51-1.
Spitzner, D. J. (2023c). Calibrated Bayes factors under flexible priors. Statistical Methods & Applications. DOI: 10.1007/s10260-023-00683-4. (link)
Meixner, C., and Spitzner, D. J. (2022). Leveraging the power of online qualitative inquiry in mixed methods research: Novel prospects and challenges amidst COVID-19. Journal of Mixed Methods Research. DOI: 10.1177/15586898221084504
Spitzner, D. J. (2023a). A statistical basis for reporting strength of evidence as pool reduction. The American Statistician, 77:1, 62-71. DOI: 10.1080/00031305.2022.2026478
Meixner, C., & Spitzner, D. J. (2021). Mixed methods research and social inclusion. In P. Liamputtong (Ed.), Handbook of social inclusion: research and practices in health and social sciences. Cham: Springer. DOI: 10.1007/978-3-030-48277-0_19-1
Spitzner, D. J. (2021). Socially-inclusive foundations of statistics: an autoethnography. In P. Liamputtong (Ed.), Handbook of social inclusion: research and practices in health and social sciences. Cham: Springer. DOI: 10.1007/978-3-030-48277-0_17-1 (Preprint available below)
Spitzner, D. J. & Meixner, C. (2021). Significant conversations, significant others: Intimate dialogues about teaching statistics. International Journal for Academic Development. DOI: 10.1080/1360144X.2021.1954931
Spitzner, D. J. (2019). Subjective Bayesian testing using calibrated prior probabilities. Brazilian Journal of Probability and Statistics. 33(4), 861-893. DOI: 10.1214/18-BJPS424
Spitzner, D. J. (2011). Neutral-data comparisons for Bayesian testing. Bayesian Analysis, 6:603-638. DOI: 10.1214/11-BA623
Spitzner, D. J. (2008). A powerful test based on tapering for use in functional data analysis. Electronic Journal of Statistics. 2:939-962.
Spitzner, D. J. (2005). Risk-reducing hierarchical shrinkage for generalized linear models. Journal of the Royal Statistics Society Series B, 67:1-14.
Spitzner, D. J., Marron, J. S., and Essick, G. K. (2003). Mixed-model functional ANOVA for studying human tactile perception. Journal of the American Statistical Association, 98:263-272.
Selected technical reports, also accessible through the links below, are as follows:
Spitzner, D. J. (2014a). Adjusting for multiplicities in variable selection using neutral-data comparisons. University of Virginia Department of Statistics, Technical Report Series, 14-02. Some of this paper's ideas are developed in Spitzner (2019).
Spitzner, D. J. (2014b). Neutral-data comparisons defining a spectrum between Bayes factors and the Schwarz criterion. University of Virginia Department of Statistics, Technical Report Series, 14-01. Some of this paper's ideas are developed in Spitzner (2019).