Assistant Professor
Office:
Scarfe Office Block 2526

Education:

University of British Columbia, 2015, Ph.D., Mathematics
DePaul University, 2007, M.Sc., Applied Mathematics in Statistics
DePaul University, 2006, B.Sc., Mathematics

Biography:

Dr. Ed Kroc joined the Department of Educational and Counselling Psychology, & Special Education in 2018 as an Assistant Professor in MERM. Dr. Kroc received his PhD in Mathematics at UBC and completed his M.Sc. (Applied Mathematics in Statistics), and B.Sc. (Mathematics) at DePaul University in Chicago, Illinois.

Scholarly Interests:

Measurement, applied and theoretical research methodology and best practice, spatio-temporal modelling, causal inference, urban ecology, life cycle of gulls

Selected Publications:

Astivia, O.L.O., Kroc, E. (2018) “Centering in multiple regression does not always reduce multicollinearity: How to tell when your estimates will not benefit from centering.” Educational and Psychological Measurement (to appear).

Kroc, E. (2018) “Reproductive ecology of urban-nesting Glaucous-winged Gulls (Larus glaucescens) in Vancouver, BC, Canada.” Marine Ornithology, 46(2), 155-164.

Busta, L., Hegebarth, D., Kroc, E., Jetter, R. (2017) “Changes in cuticular wax coverage and composition on developing Arabidopsis leaves are influenced by wax biosynthesis gene expression levels and trichome density.” Planta, 245(2), 297-311.

Liu, Y., Zumbo, B.D., Gustafson, P., Huang, Y., Kroc, E., Wu, A. (2016) ”Investigating causal DIF via propensity score methods: a demonstration with logistic regression.” Practical Assessment, Research and Evaluation, 21 (13), 1-24.

Zumbo, B.D., Kroc, E. (2016) “Some remarks on Rao and Lovric’s ‘Testing the point null hypothesis of a normal mean and the truth: 21st Century perspective’.” Journal of Modern Applied Statistical Methods, 15, 33-40.

Kroc, E., Pramanik, M. (2015) “Directional maximal operators over Cantor sets of directions in R^d.” Journal of Fourier Analysis and Applications, 22 (3), 623-674.

Research Projects:

The gap between modern statistics and applied “best practice”: Each applied science has its own methodological traditions and “best practices.” While the discipline of statistics often evolves in tandem with developments in some applied fields, there almost always exists a sizeable gap between modern methods in statistics and what are considered modern analytical methods in various applied sciences. One of my major programs of research is investigating how the most advanced statistical methods can and should be employed in the social, health, and natural sciences. This entails working with a variety of collaborators across disciplines, tailoring modern statistical methods to fit particular disciplinary needs, and educating applied practitioners about optimal quantitative procedures.

Rehabilitating measurement: the forgotten child of statistics: The field of statistics has long recognized the importance of study design, sampling, estimation, inference, and modelling, both mathematically and in the pursuit of scientific knowledge. Over the past few decades, computation and communications have rightfully earned their places alongside these pillars of statistics. However, the concept of measurement remains relatively forgotten. My major theoretical work aims at remedying this neglect by developing a general and useable theory of statistical measurement that can be seamlessly integrated into applied modes of estimation, inference, and modelling. Fixed measurements (i.e. measurements that are observable as fixed values) are surprisingly uncommon in the social sciences; so called random-variable-valued measurements are the norm. These more general measurements also commonly appear in the health and natural sciences. A robust theory is required to correct for potential bias and artificially deflated measures of uncertainty when performing inferences that are otherwise naive to the statistical nature of measurement.

Spatial and temporal modelling in the 21st Century: A relatively new area for me, I am greatly interested in modern statistical methods in spatial and temporal modelling, particularly within the context of Bayesian estimation and mixed effects modelling. While they have enjoyed remarkable success in some fields (such as epidemiology), these methods remain largely unknown among social scientists. Educational and behavioural researchers are often interested in geographical and temporal effects, but tend to employ rather naive methods to quantify them. I am interested in modernizing this domain methodology and developing new approaches for effective spatio-temporal modelling which are optimally suited for the domain.

Life of the urban gull: My main applied passion is urban ecology and, in particular, the lifestyles of the urban gull. Humans have drastically transformed nearly every habitat on the planet over the past 150 years, but none more so than those areas that we now consider “urban.” Some species have adapted remarkably well to this novel environment, yet little is understood about how nonhuman species use the urban environment and how an urban ecosystem actually functions. I try to understand these questions through the lens of the gull family, which contains many species that have adapted to and thrived among our cities. The Glaucous-winged Gull (Larus glaucescens) of the Salish Sea is my main species of focus, although I do study the urban lifestyle of most North American gulls.

 

Courses Taught:

EPSE 592 Experimental Designs and Analysis in Educational Research
STAT 450 Case Studies in Statistics
STAT 551 Statistical Consulting Practicum
STAT 302 Introduction to Probability
SCIE 300 Communicating Science
DSCI 542 Communication and Argumentation
MATH 105 Integral Calculus with Applications to Commerce and Social Sciences
MATH 104 Differential Calculus with Applications to Commerce and Social Sciences