About

Ulf Bockenholt is the John D. Gray Professor of Marketing at the Kellogg School of Management. He has served as the Senior Associate Dean for Faculty & Research from 2021 to 2024. His research interests focus on the development and application of statistical and psychometric methods to understand consumer behavior and improve marketing decision-making. His work has been published widely across marketing, psychology, economics, and statistics journals. Recent research areas include measuring the effectiveness of visual advertisements, conducting meta-analyses in behavioral research, response biases in self-reports, and the impact of trust in financial consumer decisions. Professor Bockenholt currently serves as an Associate Editor for Psychometrika, Behaviormetrika, and the Journal of Behavioral and Educational Statistics. He has held academic positions at various institutions, including the University of Chicago, University of Groningen, University of Illinois, and McGill University, where he was the Canada Research Chair of E-Marketing. He holds a PhD from the University of Chicago and has received numerous awards, including being an APS Fellow and a Fellow of the Association of Psychological Science.

Research topics

• Computer Science
• Artificial Intelligence
• Statistics
• Mathematics
• Business
• Econometrics
• Data Mining
• Marketing
• Political Science
• Epistemology
• Social psychology
• Psychology
• Finance
• Accounting
• Clinical psychology
• Economics
• Environmental economics
• Applied psychology
• Law

Selected publications

• On the Limits of Research Rigidity: The Number of Items in a Scale

  2026-01-01

  book-chapter1st authorCorresponding
  PublisherDOI

• Single paper meta‐analysis is unavoidable

  Journal of Consumer Psychology · 2025-09-10 · 2 citations

  articleOpen accessSenior author

  Abstract We advocate that the multiple studies of a common phenomenon that are featured in a typical behavioral research paper be jointly analyzed to provide a statistical summary of the set of studies as a whole. Indeed, we view such single paper meta‐analysis as unavoidable in typical behavioral research papers because (i) such papers feature multiple studies of a common phenomenon, (ii) such papers contain summaries of those studies, and (iii) statistical summaries of studies (i.e., meta‐analyses) are superior to non‐statistical summaries of them. Nonetheless, the current dominant practice is for such papers to feature separate statistical analyses of the data from each of the studies but to contain a non‐statistical summary of the multiple studies rather than a statistical summary. We believe that this is regrettable and therefore aim to rectify matters. Consequently, we review some considerations about meta‐analysis and statistical analysis more broadly; compare single paper meta‐analysis to traditional meta‐analysis and illustrate its benefits via a case study; discuss and dismiss concern about single paper meta‐analysis; and discuss single paper meta‐analysis and the review process.

  PublisherOA PDFDOI

• Item Response Models for Rating Relational Data

  Psychometrika · 2025-06-01

  articleOpen access

  This article introduces item response models for rating relational data. The relational data are obtained via ratings of senders and receivers in a directed network. The proposed models allow comparisons of senders and receivers on a one-dimensional latent scale while accounting for unobserved homophilic relationships. We show that the approach effectively captures reciprocity and clustering phenomena in the relational data. We estimate model parameters using a Bayesian specification and utilize Markov Chain Monte Carlo methods to approximate the full conditional posterior distributions. Simulation studies demonstrate that model parameters can be recovered satisfactorily even when the dimensionality of the network is small. We also present an extensive empirical application to illustrate the usefulness of the proposed models for complete and incomplete networks.

  PublisherOA PDFDOI

• Is Sustainability a Liability? Green Marketing and Consumer Beliefs About Eco-Friendly Products

  Journal of Public Policy & Marketing · 2024 · 8 citations

  • Political Science
  • Computer Science
  • Marketing

  Prior research has suggested that consumers believe sustainable products tend to underperform compared with those made using traditional methods, a phenomenon referred to as the “sustainability liability.” Despite early conceptual justification and evidence supporting this argument, recent research has not attempted to validate this effect and assess its practical relevance. By employing a variety of scenarios adapted from prior studies, the authors quantify the magnitude of the sustainability-liability effect and show that it is relatively small and unlikely to be meaningful. This research also estimates the boundary conditions to identify scenarios in which a significant sustainability-liability effect might occur. Using archival data, the authors demonstrate that the association between sustainability and inferior product performance has decreased over time, explaining the discrepancy between their findings and prior research. These findings have important public policy implications, providing decision makers with empirical evidence that designing and promoting eco-friendly products can benefit society without detracting from the perceived performance of the company's offerings.

  PublisherDOI

• Correcting for Context Effects in Ratings

  Behaviormetrics · 2023-01-01

  book-chapterSenior author
  PublisherDOI

• Meta-analysis: Assessing Heterogeneity Using Traditional and Contemporary Approaches

  Cambridge University Press eBooks · 2023-03-30 · 2 citations

  book-chapterSenior author

  A meta-analysis is a statistical analysis that combines and contrasts two or more studies of a common phenomenon. Its emphasis is on the quantification of the heterogeneity in effects across studies, the identification of moderators of this heterogeneity, and the quantification of the association between such moderators and effects. Given this, and in line with the growing appreciation for and embracement of heterogeneity in psychological research as not a nuisance but rather a boon for advancing theory, gauging generalizability, identifying moderators and boundary conditions, and assisting in future study planning, we make the assessment of heterogeneity the focus of this chapter. Specifically, we illustrate the assessment of heterogeneity as well as the advantages offered by contemporary approaches to meta-analysis relative to the traditional approach for the assessment of heterogeneity via two case studies. Following our case studies, we review several important considerations relevant to meta-analysis and then conclude with a brief summation.

  PublisherDOI

• Variation and Covariation in Large-Scale Replication Projects: An Evaluation of Replicability

  Journal of the American Statistical Association · 2022-03-18 · 8 citations

  article

  Over the last decade, large-scale replication projects across the biomedical and social sciences have reported relatively low replication rates. In these large-scale replication projects, replication has typically been evaluated based on a single replication study of some original study and dichotomously as successful or failed. However, evaluations of replicability that are based on a single study and are dichotomous are inadequate, and evaluations of replicability should instead be based on multiple studies, be continuous, and be multi-faceted. Further, such evaluations are in fact possible due to two characteristics shared by many large-scale replication projects. In this article, we provide such an evaluation for two prominent large-scale replication projects, one which replicated a phenomenon from cognitive psychology and another which replicated 13 phenomena from social psychology and behavioral economics. Our results indicate a very high degree of replicability in the former and a medium to low degree of replicability in the latter. They also suggest an unidentified covariate in each, namely ocular dominance in the former and political ideology in the latter, that is theoretically pertinent. We conclude by discussing evaluations of replicability at large, recommendations for future large-scale replication projects, and design-based model generalization. Supplementary materials for this article are available online.

  PublisherDOI

• Variation and Covariation in Large-scale Replication Projects: An Evaluation of Replicability

  Figshare · 2022-01-01

  datasetOpen access

  Over the last decade, large-scale replication projects across the biomedical and social sciences have reported relatively low replication rates. In these large-scale replication projects, replication has typically been evaluated based on a single replication study of some original study and dichotomously as successful or failed. However, evaluations of replicability based on a single study and as dichotomous are inadequate, and evaluations of replicability should instead be based on multiple studies, be continuous, and be multi-faceted. Further, such evaluations are in fact possible due to two features shared by many large-scale replication projects. In this paper, we provide such an evaluation for two prominent large-scale replication projects, one which replicated a phenomenon from cognitive psychology and another which replicated 13 phenomena from social psychology and behavioral economics. Our results indicate a very high degree of replicability in the former (e.g., variation ranging from 15 to 17 milliseconds across variates and 2 to 3 milliseconds across effects at the lab level) and a medium to low degree of replicability in the latter (e.g., variation ranging from 0.11 to 0.69 <i>σ_d</i> across variates and 0.09 to 0.79 <i>σ_d</i> across effects at the lab level). They also suggest an unidentified covariate in each, namely ocular dominance in the former and political ideology in the latter, that is theoretically pertinent. They finally have implications for evaluations of replicability at large, recommendations for future large-scale replication projects, and design-based model generalization.

  PublisherDOI

• Multilevel multivariate meta-analysis made easy: An introduction to MLMVmeta

  Behavior Research Methods · 2022-08-01 · 12 citations

  reviewSenior author
  PublisherDOI

• Variation and Covariation in Large-Scale Replication Projects: An Evaluation of Replicability

  Figshare · 2022-01-01

  datasetOpen access

  Over the last decade, large-scale replication projects across the biomedical and social sciences have reported relatively low replication rates. In these large-scale replication projects, replication has typically been evaluated based on a single replication study of some original study and dichotomously as successful or failed. However, evaluations of replicability that are based on a single study and are dichotomous are inadequate, and evaluations of replicability should instead be based on multiple studies, be continuous, and be multi-faceted. Further, such evaluations are in fact possible due to two characteristics shared by many large-scale replication projects. In this article, we provide such an evaluation for two prominent large-scale replication projects, one which replicated a phenomenon from cognitive psychology and another which replicated 13 phenomena from social psychology and behavioral economics. Our results indicate a very high degree of replicability in the former and a medium to low degree of replicability in the latter. They also suggest an unidentified covariate in each, namely ocular dominance in the former and political ideology in the latter, that is theoretically pertinent. We conclude by discussing evaluations of replicability at large, recommendations for future large-scale replication projects, and design-based model generalization. Supplementary materials for this article are available online.

  PublisherDOI

Frequent coauthors

• Blakeley B. McShane

  University of Illinois Chicago

  21 shared

• Mark Daniel

  Dasman Diabetes Institute

  20 shared

• Michel Wedel

  16 shared

• P.G.M. van der Heijden

  Utrecht University

  15 shared

• Wagner A. Kamakura

  Rice University

  10 shared

• Spencer Moore

  Wageningen University & Research

  9 shared

• Elke U. Weber

  Princeton University

  8 shared

• Maarten Cruyff

  Utrecht University

  7 shared

Labs

• John D. Gray Professor of Marketing at the Kellogg School of ManagementPI

Awards & honors

• APS Fellow, Association for Psychological Science
• John D. Gray Professorship
• Bell Chair in E-Marketing, McGill University
• Canada Research Chair Tier 1 Fellow, CIRANO