descriptive statistics

This study investigates cross-disciplinary trends in statistical software adoption across economics, political science, and statistics. Method: We systematically replicated and coded open-source code and data files from over 10,000 peer-reviewed papers, integrating web-crawled metadata, manual annotation, qualitative coding, and frequency analysis to construct the first student-led, interdisciplinary database of statistical software usage. Contribution/Results: We introduce the “multi-platform collaborative analysis” paradigm, revealing that Stata remains dominant in economics, while R has become the preferred tool in political science and statistics; moreover, over 30% of social science studies employ two or more software packages synergistically. The project significantly enhances students’ reproducibility capacity and data literacy, fostering concurrent updates in pedagogy and research practice.

This study addresses the lack of a unified computational, management, and visualization framework for characterizing diverse pairwise associations—such as linear correlation, nonlinear dependence, and Simpson’s paradox—between numeric and categorical variables. Methodologically, we propose an end-to-end analytical framework: (1) a unified R interface integrating 12 heterogeneous association measures; (2) a standardized tidy data structure enabling consistent storage, retrieval, and cross-type comparison of results; and (3) an enhanced multidimensional heatmap (implemented in the *bullseye* package, built upon *ggplot2*) supporting grouped comparisons, metric overlay, and automated paradox detection. Our key contribution is the first standardized,全流程 implementation of association analysis in R, significantly improving exploratory efficiency, reproducibility, and interpretability. The framework uniquely enhances detection of nonlinear relationships, mixed-variable dependencies, and structural biases—filling a critical gap in the R ecosystem for out-of-the-box, principled association analysis.

Identifying manually defined subgroups in high-dimensional data is challenging, and existing methods lack support for exploratory subgroup discovery. To address this, we propose a novel paradigm—exploratory subgroup analysis—and introduce the “Subgroup Map,” a visual analytics framework enabling dynamic reordering, interactive refinement, and quantitative analysis of subgroup coverage relationships. We further design a lightweight, approximate subgroup discovery algorithm, seamlessly integrated into Jupyter notebooks. Our approach balances computational efficiency with interpretability, supporting automatic subgroup detection, multi-dimensional evaluation (e.g., statistical significance, coverage, divergence), and iterative filtering. In an empirical study with 13 data scientists, our method significantly improved both the speed and depth of subgroup discovery, successfully uncovering unexpected feature interactions and fine-grained distributional biases. The Subgroup Map provides a new analytical tool for understanding data heterogeneity and model behavior.

Traditional “Table 1” formats inadequately convey details of numerical variables when presenting baseline characteristics across groups, hindering intuitive comparison. This work proposes “snapshot plots,” which transform summary tables into parallel univariate visualizations with consistent color encoding—a specialized form of hammock plots—to substantially enhance intergroup comparability and information density. Built upon the principles of parallel coordinates, the approach uniformly handles mixed variable types and is implemented in Python, accompanied by an interactive web application. Validation on two real-world “Table 1” examples demonstrates that snapshot plots significantly improve readability and facilitate cross-group comparisons.

Current open-source text analysis tools exhibit significant limitations in scalability, statistical modeling capabilities, and alignment with social science research paradigms, hindering paradigm-driven qualitative analysis of large-scale textual data. This study introduces an open-source Python framework designed specifically for computational social science, integrating sociological and anthropological research logic with scalable NLP architectures. It supports visual exploration and pattern discovery across heterogeneous qualitative sources—including field notes and web-based texts. Methodologically, the framework adopts a problem-oriented—rather than technology-driven—design, embedding core qualitative analysis workflows; leverages PyTorch and SciPy ecosystems to enable efficient distributed processing of document collections exceeding one million items; and provides low-code parameter interfaces alongside modular architecture to facilitate iterative, theory-informed validation bridging qualitative reasoning and quantitative modeling. The framework addresses critical gaps in the open-source ecosystem concerning openness, reproducibility, and methodological sensitivity.

This study addresses the substantial bias often introduced in meta-analyses when estimating standard deviations solely from the five-number summary—specifically, the minimum, maximum, and median—due to insufficient information, which can compromise inferential reliability. To mitigate this issue, the authors propose a novel estimation method based on a scaled Beta distribution that incorporates data shape characteristics to improve accuracy. A comprehensive sensitivity analysis is systematically conducted to quantify estimation uncertainty. Through extensive simulation studies and real-data applications, the proposed approach demonstrates markedly superior performance over conventional estimators across a variety of underlying distributions. Additionally, the authors provide an interactive web tool to facilitate practical implementation, enabling researchers to readily assess and correct potential bias in standard deviation estimates, thereby enhancing the robustness of meta-analytic findings.

Traditional data visualizations often obscure individual differences through aggregated statistics, limiting viewers’ empathetic engagement and deep understanding. This work proposes Zoomable Empathic Visualizations (ZEVs), a novel approach that integrates empathy mechanisms into a scalable interactive framework, enabling smooth transitions between abstract statistical representations and concrete individual narratives. By supporting continuous cognitive shifts from the collective to the personal, ZEVs facilitate richer interpretive experiences. The design leverages interactive techniques, multi-level data representation, and qualitative user studies, validated through three case studies. Findings demonstrate that ZEVs significantly enhance users’ emotional connection and depth of comprehension, offering actionable insights for future visualization systems aimed at fostering empathy in data-driven contexts.

This work addresses the lack of intuitive, programming-free interactive tools for introductory statistics learners by developing three open-source web applications built with R and Shiny, focusing respectively on probability distributions, confidence intervals and hypothesis testing, and simple linear regression. These applications innovatively integrate dynamic visualizations, real-time statistical computations, and inline mathematical derivations within a unified interface, structured according to pedagogical progression to enable learners without programming experience to simultaneously grasp conceptual and operational aspects. High-quality rendering of graphics and equations is achieved through ggplot2 and MathJax. All applications are freely accessible online, and their source code is released under the CC-BY-4.0 license, thereby effectively supporting the teaching and learning of core statistical concepts.

This work addresses the systematic bias in traditional prevalence estimation under covariate shift, which arises from the assumption that measurement device error rates remain stable across distributions. To overcome this limitation, the study introduces multicalibration theory into prevalence estimation for the first time, achieving unbiased estimates under distributional shifts by calibrating predictions conditioned on key features. The proposed method is applicable to large language models and other classifiers and requires only calibration data covering important feature dimensions. Experimental results demonstrate that, in tasks such as state-level employment rate estimation and cross-national political text classification, the approach substantially reduces estimation bias—achieving near-zero error—and effectively overcomes the limitations of standard calibration and quantification methods when faced with distributional changes.

This study addresses the lack of systematic understanding regarding the effectiveness and usage practices of univariate distribution visualizations across diverse tasks and user groups. Through a mixed-methods approach—combining a click-based selection experiment and survey with 215 participants alongside in-depth interviews with five visualization practitioners—the work systematically evaluates the accuracy, user preferences, and common misinterpretations associated with boxplots, violin plots, jittered scatterplots, and histograms in typical analytical tasks. For the first time, it integrates task performance, subjective preference, and real-world practice, revealing a frequent mismatch between chart familiarity and task accuracy, thereby challenging the assumption that commonly used or conventional visualizations are inherently optimal. The findings demonstrate significant performance differences among chart types in low-level tasks, with widely adopted histograms and boxplots not consistently outperforming alternatives.