validate homophily measures

Existing research frequently suffers from model misspecification of formative constructs, and the absence of a consensus-based validation methodology leads scholars to erroneously apply reflective measurement frameworks, thereby compromising construct validity. Method: This paper introduces the first dedicated, multi-stage validation framework for formative constructs, integrating systematic literature review, descriptive statistics, multicollinearity diagnostics, and formative-model-specific tests to rigorously distinguish formative (causal) from reflective (effect) measurement logic. Contribution/Results: The framework ensures both theoretical rigor and practical feasibility, substantially enhancing the psychometric soundness and statistical integrity of formative indicators. It provides a reproducible, defensible methodological pathway for scale development and construct validation, directly addressing longstanding measurement challenges in behavioral and social science research.

研究提出AICOME框架，利用AI生成的个体级测量来恢复个体和群体层面的影响，通过2022年中国家庭追踪调查数据验证了该方法的有效性。

This study addresses the limitations of instructor assessment and the integrity concerns associated with peer evaluation in large-scale classrooms by proposing a hierarchical peer verification model. In this approach, instructors certify "seed students" who subsequently authorize peers in a cascading manner, integrating brief oral discussions to enable scalable assessment. An empirical investigation conducted across multi-institutional engineering practicums reveals that 79.8% of participants endorsed the model's effectiveness; however, 29% reported lowered grading standards, and 88.7% questioned its academic rigor. This work offers a novel paradigm for balancing assessment efficiency with scalability in large cohorts, while recommending the incorporation of post-hoc discussion mechanisms to safeguard evaluation quality.

Estimating the functional relationship between a continuous exposure and a binary outcome is challenging when covariates are measured with error. This study presents the first systematic evaluation of Simulation-Extrapolation, Regression Calibration, multiple imputation, and Bayesian correction methods, each coupled with flexible modeling techniques—including B-splines, P-splines, and fractional polynomials—within a multi-team, fully blinded, neutral simulation framework. By generating 155 distinct simulation scenarios and repeated samples, the research quantifies the bias and variance of each approach, revealing their relative strengths and limitations. The findings not only inform method selection under measurement error but also demonstrate the feasibility and value of this neutral comparative paradigm for rigorous methodological assessment.

In quantitative pairwise comparisons, expert judgments are vulnerable to bribery-based manipulation, leading to distorted global rankings. Method: This paper formally defines the “targeted manipulation” problem for the first time and introduces a unified modeling framework integrating game theory and graph theory to characterize adversarial interventions. It proposes three polynomial-time solvable manipulation algorithms capable of precisely achieving desired rankings. Contribution/Results: Theoretical analysis demonstrates that even minimal bribery costs can significantly distort ranking outcomes. Furthermore, the study uncovers structural properties and inherent vulnerabilities of manipulation strategies, providing a theoretical foundation for detecting anomalous judgments and designing robust aggregation mechanisms. This work bridges a critical gap in the robustness literature on pairwise comparisons by establishing the first formal model of adversarial intervention.

论文提出了一种新的验证框架，用于解决合成研究中决策行为预测准确性的问题，并通过定义三个公正维度和子群体报告要求来提高验证的代表性和公平性。

本文提出了一种新的分类测试框架，用于从定量估计中得出定性结论，以解决传统假设检验在区分竞争性假设上的不足。

This study addresses the ambiguity and inconsistency in evaluation criteria for software engineering replication studies, which have led to contradictory interpretations and uncertainty in reported results. Through a systematic review of ten replication studies published between 2021 and 2025, combined with qualitative content analysis, statistical principles, and modeling of measurement uncertainty, this work is the first to uncover the heterogeneity and lack of standardized practices in current evaluation approaches. Building on these insights, the paper proposes a unified evaluation framework that integrates statistical theory, methodological rigor, and measurement theory. Empirical illustration demonstrates that the framework effectively enhances the transparency, consistency, comparability, and reliability of replication studies in software engineering.

One aspired outcome of empirical research on quantitative data is a variance theory, i.e., a quantification of the effect of an independent on a dependent variables. The validity of variance theories stems from the synthesis of multiple pieces of evidence, which increases its validity beyond the findings of a single study. However, research synthesis in SE is rare and if done mostly limited to purely narrative syntheses. At best, researchers perform meta-analyses to synthesize variance theories from several quantitative results. But even meta-analyses only produce reliable results when synthesizing exact replications yet fail to generalize from variations. We aim to extend the frontier of research synthesis beyond the state-of-the-art to systematically manage empirical evidence and its evolution. We apply method engineering to construct a framework for research synthesis from proven, individual method fragments. The framework allows researchers to put new evidence in a clear relation to an existing body of evidence and systematically expand knowledge about a studied phenomenon. We demonstrate the application of this framework to two fields of research by explicitly modeling the relationship between existing pieces of evidence. The framework puts three types of evolution of evidence into relation: (1) replications investigate the same hypothesis in a new context to improve external validity, (2) revisions challenge an existing hypothesis to improve internal validity, and (3) reanalyses replace analysis methods to improve conclusion validity. Through a systematic evolution of evidence and clear assessment criteria for each dimension of validity, the proposed framework can determine the frontier of a field of research. The framework provides a perspective to systematically evolve empirical evidence in SE, supporting more constructive and productive advances in our field.

This study addresses the overreliance on inter-annotator agreement in current data annotation practices, which often overlooks annotation’s capacity to capture conceptual validity as a measurement act. Treating annotation as a measurement process, the work identifies five root causes of annotation issues—errors, ambiguity, impossibility, subjectivity, and annotator identity—and develops a measurement theory–based framework for diagnosing and improving annotation quality. Drawing on a synthesis of 132 literature sources and 10 semi-structured interviews, the research systematically defines target constructs, designs annotation instruments, implements labeling procedures, and evaluates both reliability and validity. The resulting framework equips annotation teams with evaluation methods that transcend mere agreement metrics, thereby substantially strengthening the foundational quality of AI training data.