Bruno Zumbo
Bruno D. Zumbo is a mathematical scientist trained in the tradition of research that combines pure and applied mathematics with statistical and algorithmic techniques to develop theory and solve problems arising in measurement, testing, and surveys in the social, behavioral, and health sciences. He is currently Professor and Distinguished University Scholar, the Tier 1 Canada Research Chair in Psychometrics and Measurement, and the Paragon UBC Professor of Psychometrics & Measurement at University of British Columbia. His body of research in the mathematical sciences reflects a wide range of research in mathematics and statistics aimed at developing and exploring the  properties and applications of mathematical structures of measurement, survey design, testing, and assessment. Many of these projects are conducted with a select group of local, national, and international collaborators as well as graduate students and postdoctoral researchers. The mission of his research funding is to invest in supporting training through research involvement of the next generation of social and health scientists, psychometricians, mathematical scientists, and professional research scientists at a portfolio of governmental departments, as well as non-governmental research institutes, and survey, testing and assessment organizations. He currently teaches in the graduate Measurement, Evaluation, & Research Methodology Program with an  additional appointment in the Institute of Applied Mathematics, and earlier also in the Department of Statistics, at the University of British Columbia (UBC) in Vancouver, British Columbia, Canada. Prior to arriving at UBC in 2000, he held professorships in the Departments of Psychology and of Mathematics at the University of Northern British Columbia (1994-2000), and earlier in the Faculty of Education with an adjunct appointment in the Department of Mathematics at the University of Ottawa (1990-1994). Biography. He was born and raised in Edmonton, Alberta, one of six children (including a twin sister) of immigrant parents from Reggio  di Calabria, Italy. He speaks English and Italian (including the Calabrese dialect), and reads English, Italian and French. He was a first-generation university graduate. He completed his B.Sc. at the University of Alberta (Edmonton, AB) and his MA and Ph.D. from Carleton University (Ottawa, ON). His studies reflected his curiosity and diverse interests. With an eye to fostering his talents and interests, the University of Alberta allowed him to take graduate (Masters and PhD) courses in statistics and mathematics while eventually completing his B.Sc. Likewise, in graduate school his professors allowed him to pursue and integrate mathematics, philosophy of  science and Psychology. His interests continue to be focused on mathematical sciences of measurement and scientific methodology with a blend of mathematics, social sciences like psychology, philosophy of science and measurement in science. His doctoral dissertation titled "Statistical Methods to Overcome Nonindependence of Coupled Data in Significance Testing" was under the direction of Prof. Donald W. Zimmerman (Carleton University, Ottawa). Program of research. Professor Zumbo is an internationally recognized mathematical scientist, psychometrician, and measurement theorist. His program of research has had a wide influence on psychometric and validity theories, measurement invariance and test bias, educational and behavioral statistics, and language  testing. Overview. He is known for his contributions in the fields of statistics, psychometrics, validity theory, and studies of the mathematical basis of classical test theory, item response theory, and measurement error models. Measure-theoretic psychometric and measurement theory. A noteworthy contribution to the field is the pioneering work initiated by Donald Zimmerman in 1975 in "Psychometrika" and continued by Zimmerman and Zumbo in a series of collaborations dating back to 1990. This line of research has continued with a series of collaborations by Bruno Zumbo and Ed Kroc more fully articulating what Zumbo has termed as "measure-theoretic psychometric  and measurement theory" (e.g., Zimmerman, 1975; Zumbo & Zimmerman, 1993; Zimmerman & Zumbo, 2001; Kroc & Zumbo, 2018; ; ). "Measure-theoretic psychometric and measurement theory" emerges from applications of mathematical analysis and measure theory that allows for a precise, succinct, and mathematically coherent specification of test theory and resolves many inconsistencies and apparent paradoxes in conventional psychometric theory as developed, for example, in the seminal work of Lord and Novick (1968). In addition, in the measure theoretic formulation of psychometric and measurement theory (building upon measure-theoretic probability), one can speak of the general concept of measures instead of the restrictive notions of density  or mass functions; and avoiding any entanglements about Stevens' scales or levels of measurement, and being limited to discrete or continuous random variables and ignoring attributes or phenomena that are mixtures of continuous and discrete random variables (Zumbo & Zimmerman, 1993; ; ). In this general line of research of a measure-theoretic measurement theory, Kroc (2020) introduced random-variable-valued measurements; which can be used to characterize measurement protocols and response process error when sample data are not deterministic. Measurement Invariance, Exchangeability, and Bias & Differential Functioning of Tests and Surveys. A noteworthy contribution has been the body of research focused on the  analysis of the psychometric concepts of measurement invariance, exchangeability, and bias & differential item (survey question or assessment task) functioning. This body of research sometimes travels under the name "differential item or test functioning (DIF / DTF)." This program of research started as a study of the paradox that is measurement invariance (e.g., Li and Zumbo 2009; Rupp and Zumbo 2003, 2004, 2006; Zimmerman and Zumbo 2001; Zumbo and Gelin, 2005; Zumbo and Rupp 2004; Zumbo 1999, 2007a, 2007b, 2008, 2009). On the one hand, under a mathematical lens, it is a trivial identity but on the other hand, under a historical  and conceptual lens, it is probably the most important property of latent variable measurement models and item response theory (IRT), in particular. Furthermore, according to much of the contemporary psychometric literature, invariance is what sets IRT apart from classical test theory (CTT) models. Mathematically, IRT parameter invariance is a simple identity for parameters that are on the same scale. Yet the latent scale in IRT models is arbitrary, so that unequated sets of model parameters are invariant only up to a set of linear transformations specific to a given IRT model. Note that the properties of specific objectivity and scale  representation in terms of additive conjoint measurement for the Rasch model (Perline, Wright, & Wainer, 1979) do not imply that the Rasch scale is nonarbitrary; they merely imply that it has certain desirable measurement properties. Its metric remains fundamentally arbitrary, however, because the latent variable is a statistical construction (see, e.g., Lord, 1980, p. 36). As Rupp and Zumbo (2006) state, the "... process of clarifying what is meant by parameter invariance and demystifying its status, which is often misperceived as a “mysterious” property that all IRT models seem to possess by definition across an almost infinite range of examinee populations and  measurement conditions. Put differently, if thorough discussions about inferential limits and generalizability are desired, this article shows that the mathematical foundations of parameter invariance as a fundamental property of measurement cannot be ignored." Research on score equating, differential item functioning (DIF), and item parameter drift (IPD) deals with lack of measurement invariance LOI and the effects introduced thereby. Draper–Lindley–de Finetti Framework and the Explanation-Focused View of Validity and Validation. What has emerged alongside the body of research on exchangeability and invariance are (i) the Draper-Lindley-de Finetti framework focused on inferences about items (or tasks ) and persons made in assessment and surveys described in Zumbo (2007) for the articulation of the bounds of the claims from our surveys and assessments, and (ii) Zumbo's explanation-focused view of test validity (e.g., Zumbo, 2007, 2009, 2017; Zumbo and Gelin, 2005; Zumbo et al., 2015). These both invoke what Zumbo (2015) refers to as an in vivo view of testing and assessment rather than the more widely received in vitro view. Doing so, necessitates an ecological model of item responding and test performance (Zumbo et al., 2015). Selected publications. "See Scholarly Interests above for a full list  of publications"