Research

Research Interests

My research advances the methodological foundations of empirical science across the social sciences, particularly those with psychological or behavioral predictors or outcomes. I develop and refine statistical and psychometric methods that enable researchers to design more rigorous studies, draw more accurate inferences, and build a cumulative scientific literature. This work addresses a fundamental challenge: without appropriate research design and principled analysis methods, empirical findings become unreliable, hindering scientific progress and practical application. I work at the nexus of research design, statistical inference, and measurement theory, with particular emphasis on ensuring that studies are optimally designed before data collection begins—the point where researchers have the greatest leverage to prevent methodological shortcomings rather than attempt post-hoc corrections. My collaborations span psychology, management, education, marketing, information technology, and behavioral medicine, applying and extending methodological tools to address substantive questions in each domain. As Tukey observed, “the best thing about being a statistician is that you get to play in everyone’s backyard!”

Core Research Focus

My primary methodological contributions center on the interplay between effect sizes, confidence intervals, statistical significance, and sample size planning. Sample size planning represents one of the most consequential decisions in research design. Studies with excessive sample sizes may waste resources, delay knowledge dissemination, and may expose more participants to risk than necessary. Conversely, underpowered studies compromise inferential precision, reduce the likelihood of detecting meaningful effects, and squander participants’ time and researchers’ efforts. My work provides researchers with principled frameworks—particularly accuracy in parameter estimation (AIPE) approaches—for determining appropriate sample sizes based on their specific inferential goals. The design stage is where researchers exert the greatest influence on study quality, and my methods help ensure that this opportunity is leveraged effectively.

Broader Methodological Interests

Beyond research design, my work encompasses longitudinal data analysis, mixed-effects and multilevel models, mediation analysis, general latent variable models, finite mixture modeling, statistical classification and discrimination, bootstrap methods, Monte Carlo simulation design, and psychometric theory. These methods are not siloed; they often combine synergistically to address complex research questions. Statistical computing undergirds this work, with most of my methodological developments implemented in R packages (including MBESS, BUCCS, and SMRD) that make these tools accessible to applied researchers. A unifying theme across my research is methodological cross-fertilization. Techniques developed in one discipline frequently remain unknown to adjacent fields facing similar analytical challenges. By working across traditional disciplinary boundaries, I identify opportunities to adapt and refine methods for new contexts, elevating methodological practice across multiple domains. This interdisciplinary perspective enriches both the methods themselves and the substantive research they enable.

Collaborative Research

I collaborate extensively with applied researchers on projects ranging from focused investigator-initiated studies to large-scale federally funded initiatives. These partnerships provide opportunities to develop new methods in response to real analytical challenges and to apply existing techniques in novel contexts. If you believe my methodological expertise could advance your research, I welcome inquiries. While my capacity for new collaborations depends on current commitments and project fit, I’m particularly interested in partnerships that involve complex design questions, novel measurement challenges, or opportunities for methodological innovation.

Potential PhD students interested in working with me should learn more and consider applying > to Notre Dame’s Analystics program.

My research program has been about making improvements to the scientific methods used in the social and behavioral sciences, in an effort to produce a more accurate and cumulative literature. My research evaluates, improves, and develops research methods of a statistical and measurement nature for the fields that use psychological, behavioral, or social data (e.g., psychology, sociology, management, marketing, education, behavioral medicine). My research focuses on the methods of designing studies and analyzing data for the social and behavioral sciences, which are arguably the most foundational aspects of an empirical science. Without an appropriate design or if impoverished analysis methods are used, the value of the research is questionable and leads to a literature filled with suspect conclusions, thereby limiting the effectiveness of what should be a living and cumulative literature. My efforts in this space have helped to reduce various methodological shortcomings. A general way of saying what I do is work on methods of designing studies and analyzing data. Additionally, I apply a variety of methods collaboratively with others in mutually beneficial collaborations in a variety of domain specific areas, where I can develop needed or apply existing methods to address interesting and important real-world problems. As Tukey pointed out, “the best thing about being a statistician is that you get to play in everyone’s backyard!”

Primary Area of Research-General

My primary research is on the interrelated topics of effect sizes, confidence intervals, and sample size planning. Sample size planning is one of the most important aspects of designing an empirical study, because using a sample size that is much too large for the particular research goal potentially puts more participants than necessary at risk, delays dissemination of findings, and is not an effective use of limited resources. Using a sample size that is too small for the goal, however, lowers the likelihood that the research goal can be addressed with enough confidence to add to the literature or ensure that the participants’ and researcher’s time was used wisely. The design stage of an empirical study is where researchers can have arguably the biggest impact on success, and where potential methodological shortcomings can be prevented (instead of attempting to fix later).

General Research Interests

My interests span widely across the field of research methodology – I just do not have enough time to work on all of them with the same intensity as I do for research design! Some of the other topics that I work on are longitudinal data analysis, mixed-effects models/multilevel models, mediation models, general latent variable models, finite mixture modeling, statistical classification and statistical discrimination, the bootstrap technique, the proper design and implementation of Monte Carlo simulation studies, and various psychometric issues. The methods that I am interested in need not be conceptualized as being mutually exclusive, as many times the methods are combined to form a unified approach to designing research studies and analyzing data. Further, much of what I do involves statistical computing and R is involved in much of my work. An interest related to all others is the cross-fertilization of methods from a variety of fields. Methodological developments in one field are often not well known in other fields, even though both fields may ask questions that can be addressed with the same or similar methods. By working in a variety of fields in an interdisciplinary fashion and borrowing methods from each, better methodological practice can be implemented in each field, which is beneficial all around.

General Research Interests

My interests span widely across the field of research methodology – I just do not have enough time to work on all of them with the same intensity as I do for research design! Some of the other topics that I work on are longitudinal data analysis, general latent variable models, finite mixture modeling, statistical classification and statistical discrimination, the bootstrap technique, the proper design and implementation of Monte Carlo simulation studies, and various psychometric issues. The methods that I am interested in need not be conceptualized as being mutually exclusive, as many times the methods are combined to form a unified approach to designing research studies and analyzing data. An interest related to all others is the cross-fertilization of methods from a variety of fields. Methodological developments in one field are often not well known in other fields, even though both fields ask questions that can be addressed with the same or similar methods. By working in a variety of fields and borrowing methods from each, better methodological practice can be implemented in each field and all fields benefit.

Overarching Research Goal

The overall goal of my research is to evaluate, improve, and develop research methods of a statistical and measurement nature for fields that use psychological, behavioral, or social data (e.g., management, marketing, education, behavioral medicine, information technology, sociology, psychology) so that substantive questions can be addressed with quality methods.

Collaboration

I have been a consultant on many research projects ranging from small scale narrowly focused studies to large scale government funded projects. Feel free to contact me if you think my research could be beneficial to your research. Depending on many factors, I may or may not be able to provide assistance and/or collaborate.

Get Involved

Graduate students interested in getting involved with methodological should feel free to contact me.