manage research panels

This study investigates how respondents’ prior survey experiences in online probability panels influence subsequent response behavior, aiming to reduce nonresponse and panel attrition. Using longitudinal panel data, it is the first to systematically apply discrete-time survival analysis to nonresponse modeling—effectively accommodating unbalanced panel structures—while integrating dynamic effects of multi-wave survey experiences (e.g., duration, perceived enjoyment, mode—telephone vs. web—and inter-wave interval) and stable individual traits (e.g., conscientiousness, openness). Results indicate that longer survey duration, lower enjoyment ratings, telephone administration, and extended inter-wave intervals significantly increase refusal risk; conversely, personality traits exhibit robust cross-wave predictive power for response propensity. The findings advance theoretical understanding of panel engagement dynamics and provide empirically grounded, actionable insights for optimizing panel management strategies and enhancing data quality in longitudinal survey research.

This study challenges the “intellectual fairness” of Italy’s national research evaluation panels in economics, statistics, and business—questioning whether surface-level demographic balance (e.g., gender, institutional affiliation) conceals deeper intellectual homogeneity. Method: We propose a multidimensional academic network framework—integrating co-authorship, journal publication, and institutional affiliation—to quantify panel members’ epistemic connectivity and cognitive diversity via social network analysis. Using graph-theoretic metrics (network density, clustering coefficient) and a randomized controlled design, we compare official panels against randomly sampled counterparts across disciplinary domains. Contribution/Results: Both officially appointed panels exhibit significantly higher network connectivity than their random counterparts, revealing structural intellectual homogeneity that undermines substantive fairness in peer review. The study shifts beyond demographic proxies for fairness, introducing a rigorous, operationalizable metric for assessing intellectual diversity in research governance.

This study addresses the identification of deterministic monotone conditional mappings governed by refreshment samples in panel data subject to unrestricted attrition. By leveraging set dominance theory to characterize consistent mappings, and integrating density ratio control, explicit rationalization of the attrition process, and rank condition analysis, it derives identified sets and trimmed bounds for mean effects both with and without attrition. The work reveals a novel property that tail behavior does not determine the identified set, quantifies biases across various matching strategies, and demonstrates that refreshment designs can identify path curvature. Empirically validated using Japanese panel data, this research provides a robust identification framework for handling complex panel attrition.

为解决大规模人口调查中的响应率低、成本高及数据延迟等问题，研究提出结合人类与大语言模型的混合面板方法，通过迭代优化提高数据质量。

This study addresses the persistent ambiguity in classifying repeated measures experimental designs, which often arises from conceptual confusion. To resolve this issue, the authors systematically clarify the core characteristics of such designs and propose a novel classification framework grounded in experimental units and randomization strategies. For the first time in this context, Hasse diagrams are introduced to visually represent the hierarchical structure of these designs. This approach effectively distinguishes among various types of repeated measures designs, eliminates terminological ambiguities, and substantially enhances both the rigor and interpretability of experimental planning and reporting.

This study investigates whether coarse-grained marginal validation alone suffices to support large language models (LLMs) as surrogate participants in human behavioral research. We construct a fixed panel of 16 lightweight personality-conditioned GPT-4 instances and evaluate their marginal alignment with human data in repeated games, systematically analyzing the impact of prompts, phrasing, and labeling on behavioral outputs. Methodologically, we integrate a fixed-panel design, symmetric Dirichlet sensitivity analysis, finite-opportunity plug-in estimation, and exact gating tests, alongside a verifiable reproduction framework that requires no real-time API calls. Results show that three of four game units meet preregistered marginal criteria; prompt variations account for 47%–96% of behavioral variance; and minor wording adjustments increase cooperation rates from 0/40 to 37/40. While marginal matching is achievable, treatment effect estimates remain imprecise, revealing methodological limitations including household-level error, interdependence, and boundary uncertainty.

This study addresses how prior participation in panel surveys influences subsequent responses and propagates bias. It characterizes the identified set of conditional paths in staggered panels, elucidates the mechanism by which two-way fixed effects absorb unidentified directions, and proposes a tenure-indicator-based corrected regression approach. By leveraging non-equidistant schedules to disentangle interview from calendar increments, the work establishes bounds linking event-study coefficient shifts to path curvature, solving the problem through algebraic identification theory combined with kernel projection analysis. The identification identity is rigorously verified, and empirical applications using Japanese panel and CPS data demonstrate that the proposed method effectively achieves conditional computation and bias correction.

This study addresses the lack of systematic support for creating, managing, and deploying stimulus materials in visualization experiments—a gap that often leads to invalid results or wasted resources. Through semi-structured interviews with 19 visualization researchers, the work systematically examines practices and challenges across the full lifecycle of stimulus materials, from exploration and selection to deployment and analysis, integrating perspectives from user research and human factors engineering. The findings reveal, for the first time, a heavy reliance on manual processes and significant scalability limitations as core pain points in current workflows. Building on these insights, the study identifies key opportunities for improvement, including automated generation and intelligent validation of stimuli, thereby laying the groundwork for future directions such as AI-assisted stimulus design.

研究通过对比合成调查面板与实际调查数据，发现仅依靠边际保真度无法有效验证用户模拟，使用概率向量法可提高准确性。

本文解决了在主体内实验设计中如何准确估计处理效应的问题，通过构建潜在结果框架和敏感性分析方法来评估并改进现有方法。