About

Dr. Bo Zhang is an Associate Professor at the School of Labor and Employment Relations. His research focuses on personnel selection and organizational research methods, with particular emphasis on improving personality assessment to enhance the effectiveness of personnel selection processes. He investigates how job applicants respond to personality items, methods to prevent faking using the forced-choice format, and techniques to detect and correct for faking through advanced statistical models. Dr. Zhang has developed augmented bifactor models, including the Mixture Dominance Unfolding Model, the Generalized Thurstonian Unfolding Model, and the Unfolding Item Response Tree Model, to enable more stable estimation of the validity of hierarchical constructs and to account for heterogeneity in item response processes and response styles. Additionally, his research explores personality change and the relationships between personality, stress, and health.

Research topics

• Social psychology
• Psychology
• Clinical psychology
• Statistics
• Developmental psychology
• Applied psychology

Selected publications

• A systematic review and meta-analysis of the proficiency and variability of mathematical ability in populations with autism spectrum disorder

  Nature Human Behaviour · 2026-02-18 · 1 citations

  article
  PublisherDOI

• Clarifying the Construct of Supervisor Support for Recovery and Its Impact on Employee Recovery Experiences

  Journal of Management · 2025-02-26 · 2 citations

  articleOpen accessSenior author

  Insufficient recovery from work stress is a pernicious issue for many workers. This study aims to understand the important role that supervisors play in employees’ recovery experiences. Specifically, we (1) proposed an expanded conceptualization of supervisor support for recovery (SSR), and (2) developed and validated a measure consistent with this expanded conceptualization. We refined the conceptualization of SSR with four dimensions: refraining from communicating about work during nonwork time, refraining from requiring work during nonwork time, modeling recovery, and encouraging recovery. These dimensions align with the recovery literature, which highlights the necessity of refraining from recovery-hindering behaviors to reduce energy exertion and engaging in recovery-promoting behaviors to provide recovery opportunities. The recovery-promoting dimensions also align with key themes of role modeling and encouragement emphasized in social cognitive theory. Based on the conceptualization, we further developed and validated an SSR scale using three different designs (cross-sectional, supervisor-subordinate dyadic, time-separated) in six studies. Results showed that SSR was distinct from related supervisor constructs (e.g., leader-member exchange and family supportive supervisor behaviors), was positively associated with recovery experiences, and provided further insight into recovery experiences, over and above the other supervisor constructs. This study provides a foundation for future research to better understand how supervisors can support employee recovery from work stress.

  PublisherOA PDFDOI

• Minimax rates and adaptivity in combining experimental and observational data

  Journal of Causal Inference · 2025-01-01 · 2 citations

  articleOpen access

  Abstract Randomized controlled trials (RCTs) are the gold standard for evaluating the causal effect of a treatment; however, they often have limited sample sizes and sometimes poor generalizability. On the other hand, non-randomized, observational data derived from large administrative databases have massive sample sizes and better generalizability, but are prone to unmeasured confounding bias. It is thus of considerable interest to reconcile effect estimates obtained from RCTs and observational studies investigating the same intervention, potentially harvesting the best from both realms. In this article, we theoretically characterize the potential efficiency gain from integrating observational data into the RCT-based analysis from a minimax perspective. For estimation, we derive the minimax rate of convergence for the mean-squared error and propose adaptive estimators that attain the optimal rate up to poly-log factors. For inference, we characterize the minimax rate for the length of confidence intervals and show that adaptation (to unknown confounding bias) is in general impossible. A curious phenomenon thus emerges: for estimation, the efficiency gain from data integration can be achieved without prior knowledge of the magnitude of the confounding bias; for inference, the same task becomes information theoretically impossible in general. We corroborate our theoretical findings using simulations and a real data example from the RCT DUPLICATE initiative.

  PublisherDOI

• Locating Lightning Strikes in Transmission Lines Using Electromagnetic Time Reversal Method Based on Criterion of Voltage Energy

  2025-06-17

  articleSenior author

  This paper presents the electromagnetic time reversal method based on voltage energy criterion to identify the location of lightning strikes. Compared with the current energy as a criterion, it is no need to set up a series of short-circuit branches and calculate the normalized value of each branch, thus avoiding changing the topology of the transmission line. In this study, an electromagnetic transient simulation model of transmission line is constructed, based on which the effect of electromagnetic time reversal method using voltage criterion to locate lightning faults is investigated. It shows that, while greatly reducing the calculation and workload, this method is able accurately identify the location of lightning strike, point of flashover or back-flashover and reach the same accuracy and effect compared with the criterion of current energy.

  PublisherDOI

• Agentar-DeepFinance-100K: A Large-Scale Financial Dataset via Systematic Chain-of-Thought Synthesis Optimization

  ArXiv.org · 2025-07-17

  preprintOpen access

  Recent advancements in large language models (LLMs) have demonstrated remarkable general reasoning capabilities, holding significant potential for applications in the financial domain, a field that requires robust and reliable reasoning. It has been demonstrated that distilling high-quality chain-of-thought (CoT) rationales from advanced general reasoning models offers a promising and efficient path to the financial reasoning model. However, existing CoT synthesis methods suffer from shallow CoT sampling, leaving the question of how to construct a well-designed knowledge space for finance reasoning unexplored. In this paper, we present Agentar-DeepFinance-100K, a large-scale financial reasoning dataset characterized by its systematic CoT synthesis optimization. We first introduce a comprehensive CoT synthesis pipeline featuring Multi-perspective Knowledge Extraction (MKE) and Self-Corrective Rewriting (SCR) to generate exhaustive and deep financial reasoning trajectories. Furthermore, a systematic investigation, termed CoT Cube, is conducted to analyze critical factors that influence CoT effectiveness, such as necessity, length and synthesizer, yielding valuable insights for high-quality financial CoT construction. Experiments demonstrate that models trained on our Agentar-DeepFinance-100K achieve significant improvements on financial benchmarks. We publicly release Agentar-DeepFinance-100K , hoping to advance the research in financial reasoning models.

  PublisherOA PDFDOI

• The bending-buckling coupled model for blistering behavior in anti-corrosion coatings

  Mechanics of Materials · 2025-01-02 · 1 citations

  article
  PublisherDOI

• TIPDF-DWSF: a task-oriented two-stage optimization framework for diffusion model LoRA fine-tuning

  Multimedia Systems · 2025-12-03

  article1st authorCorresponding
  PublisherDOI

• The Proficiency and Variability of Mathematical Ability in Populations with Autism Spectrum Disorder: A Meta-analysis

  Research Square · 2025-01-06

  preprintOpen access
  PublisherOA PDFDOI

• 九嶷山复式岩体成因

  Earth Science-Journal of China University of Geosciences · 2025-01-01

  articleOpen access1st authorCorresponding
  PublisherDOI

• Extracellular vesicles as multiorgan crosstalk mediators in cardiovascular diseases: emerging biomarkers and therapeutic targets

  European Journal of Pharmacology · 2025-07-18 · 2 citations

  review
  PublisherDOI

Frequent coauthors

• Tianjun Sun

  Kansas State University

  23 shared

• Jing Luo

  15 shared

• Mengyang Cao

  8 shared

• Brent W. Roberts

  University of Illinois Urbana-Champaign

  8 shared

• Fritz Drasgow

  University of Illinois Urbana-Champaign

  7 shared

• Jian Li

  Beijing Normal University

  7 shared

• Daniel K. Mroczek

  Northwestern University

  6 shared

• Louis Tay

  Purdue University West Lafayette

  5 shared

Education

• Ph.D., Human Resources

  University of Illinois at Urbana-Champaign

  2009

• M.S., Human Resources

  University of Illinois at Urbana-Champaign

  2006

• B.S., Human Resources

  University of Illinois at Urbana-Champaign

  2004