Data in Behavioral and Experimental Research: Datasets and Applications

A special issue of Data (ISSN 2306-5729).

Deadline for manuscript submissions: 31 July 2026 | Viewed by 9328

Special Issue Editors


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Guest Editor
Center for Economic Research, Shandong University, Jinan 250100, China
Interests: information economics; experimental economics; behavioral economics; industrial economics
Special Issues, Collections and Topics in MDPI journals

E-Mail Website
Guest Editor
Department of Decision Sciences and Managerial Economics, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong
Interests: behavioral economics; experimental economics; microeconomics

Special Issue Information

Dear Colleagues,

Behavioral experiments generate complex datasets that capture nuanced dimensions of human cognition, decision-making, and social interactions. As these experiments continue to grow in sophistication and scale, so does the value of the data that they produce. Despite their scientific importance, many behavioral datasets remain isolated, lack standardized documentation, or pose methodological challenges for analysis.

This Special Issue invites submissions that address the full lifecycle of behavioral experimental data, ranging from experimental design to data sharing and advanced analysis. We welcome papers that introduce, describe, or analyze datasets generated through laboratory experiments, field studies, or online behavioral research. This Special Issue of Data will bridge existing gaps by fostering the open sharing of behavioral datasets and promoting innovative methodologies for their analysis.

Topics include but are not limited to the following:

  • Dataset Descriptors: Articles that document experimental datasets, including information on experimental design, data collection procedures, and potential use cases. We are particularly interested in articles that describe datasets derived from diverse experimental paradigms, such as decision-making tasks, economic games, neuroeconomic studies, or field experiments. Such contributions enhance the reusability and citability of valuable behavioral data across the research community.
  • Analytical Studies: We also call for methodological advances in processing and interpreting behavioral data. We encourage articles that demonstrate how such data can be used to generate insights, test theories, or explore new methodological approaches. These studies may include cross-study analyses, novel empirical strategies, or applications of advanced statistical and computational tools. Specifically, submissions may explore innovative statistical models for capturing heterogeneous behavioral patterns, machine learning techniques, or integrative approaches that combine choice data with multimodal signals, such as eye-tracking, physiological measurements, or neuroimaging data. We also welcome cross-disciplinary applications that demonstrate how behavioral datasets can inform real-world challenges, for instance, case studies on policy design grounded in experimental evidence, analyses of public health interventions leveraging behavioral insights, or investigations into cultural and demographic variations in decision-making.

In addition, we welcome submissions addressing topics such as the following:

  • Replication studies using shared behavioral data;
  • Methods for organizing and standardizing behavioral datasets;
  • Meta-analyses and cross-experimental comparisons;
  • Interdisciplinary applications of behavioral data (e.g., in health, education, development, or finance);
  • Innovations in data sharing, archiving, and citation practices;
  • Ethical and methodological considerations in data openness.

We hope that this Special Issue will serve as a platform to promote open science, data transparency, and methodological rigor in the behavioral sciences. We welcome submissions from experimental economists, behavioral scientists, psychologists, data scientists, and all related disciplines.

Prof. Dr. Jie Zheng
Dr. Jaimie W Lien
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 250 words) can be sent to the Editorial Office for assessment.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Data is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • behavioral experiments
  • experimental economics
  • behavioral data
  • experimental data
  • data sharing
  • dataset descriptors
  • replication
  • meta-analysis
  • open science
  • experimental design
  • decision-making

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Published Papers (6 papers)

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Research

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16 pages, 594 KB  
Article
A Data-Driven Analysis of Cognitive Learning and Illusion Effects in University Mathematics
by Rodolfo Bojorque, Fernando Moscoso, Miguel Arcos-Argudo and Fernando Pesántez
Data 2025, 10(11), 192; https://doi.org/10.3390/data10110192 - 19 Nov 2025
Cited by 1 | Viewed by 1317
Abstract
The increasing adoption of video-based instruction and digital assessment in higher education has reshaped how students interact with learning materials. However, it also introduces cognitive and behavioral biases that challenge the accuracy of self-perceived learning. This study aims to bridge the gap between [...] Read more.
The increasing adoption of video-based instruction and digital assessment in higher education has reshaped how students interact with learning materials. However, it also introduces cognitive and behavioral biases that challenge the accuracy of self-perceived learning. This study aims to bridge the gap between perceived and actual learning by investigating how illusion learning—an overestimation of understanding driven by the fluency of instructional media and autonomous study behaviors—affects cognitive performance in university mathematics. Specifically, it examines how students’ performance evolves across Bloom’s cognitive domains (Understanding, Application, and Analysis) from midterm to final assessments. This paper presents a data-driven investigation that combines the theoretical framework of illusion learning, the tendency to overestimate understanding based on the fluency of instructional media, with empirical evidence drawn from a structured and anonymized dataset of 294 undergraduate students enrolled in a Linear Algebra course. The dataset records midterm and final exam scores across three cognitive domains (Understanding, Application, and Analysis) aligned with Bloom’s taxonomy. Through paired-sample testing, descriptive analytics, and visual inspection, the study identifies significant improvement in analytical reasoning, moderate progress in application, and persistent overconfidence in self-assessment. These results suggest that while students develop higher-order problem-solving skills, a cognitive gap remains between perceived and actual mastery. Beyond contributing to the theoretical understanding of metacognitive illusion, this paper provides a reproducible dataset and analysis framework that can inform future work in learning analytics, educational psychology, and behavioral modeling in higher education. Full article
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37 pages, 2744 KB  
Article
Synergistic Evolution or Competitive Disruption? Analysing the Dynamic Interaction Between Digital and Real Economies in Henan, China, Based on Panel Data
by Yaping Zhu, Qingwei Xu, Chutong Hao, Shuaishuai Geng and Bingjun Li
Data 2025, 10(8), 126; https://doi.org/10.3390/data10080126 - 4 Aug 2025
Cited by 1 | Viewed by 1949
Abstract
In the digital transformation era, understanding the relationship between digital and real economies is vital for regional development. This study analyses the interaction between these two economies in Henan Province using panel data from 18 cities (2011–2023). It incorporates policy support intensity through [...] Read more.
In the digital transformation era, understanding the relationship between digital and real economies is vital for regional development. This study analyses the interaction between these two economies in Henan Province using panel data from 18 cities (2011–2023). It incorporates policy support intensity through fuzzy set theory, applies an integrated weighting method to measure development levels, and uses regression models to assess the digital economy’s impact on the real economy. The coupling coordination degree model, kernel density estimation, and Gini coefficient reveal the coordination status and spatial distribution, while the ecological Lotka–Volterra model identifies the symbiotic patterns. The key findings are as follows: (1) The digital economy does not directly determine the state of the real economy. For example, cities such as Zhoukou and Zhumadian have low digital economy levels but high real economy levels. However, the development of the digital economy promotes the real economy without signs of diminishing returns. (2) The two economies are generally coordinated but differ spatially, with greater coordination in the Central Plains urban agglomeration. (3) The digital and real economies exhibit both collaboration and competition, with reciprocal mutualism as the dominant mode of integration. These insights provide guidance for policymakers and offer a new perspective on the integration of both economies. Full article
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31 pages, 2284 KB  
Article
Rethinking Inequality: The Complex Dynamics Beyond the Kuznets Curve
by Sarthak Pattnaik, Maryan Rizinski and Eugene Pinsky
Data 2025, 10(6), 88; https://doi.org/10.3390/data10060088 - 14 Jun 2025
Cited by 3 | Viewed by 3127
Abstract
Income inequality has emerged as a defining challenge of our time, particularly in advanced economies, where the gap between rich and poor has reached unprecedented levels. This study analyzes income inequality trends from 2000 to 2023 across developed countries (the United States, the [...] Read more.
Income inequality has emerged as a defining challenge of our time, particularly in advanced economies, where the gap between rich and poor has reached unprecedented levels. This study analyzes income inequality trends from 2000 to 2023 across developed countries (the United States, the United Kingdom, Germany, and France) and developing nations using World Bank Gini coefficient data. We employ comprehensive visualization techniques, Pareto distribution analysis, and ARIMA time-series forecasting models to evaluate the effectiveness of the Kuznets curve as a predictor of income inequality. Our analysis reveals significant deviations from the traditional inverse U-shaped Kuznets curve across all examined countries, with persistent volatility rather than the predicted decline in inequality. Forecasts using ARIMA and neural networks indicate continued fluctuations in inequality through 2030, with the U.S. and Germany showing upward trends while France and the UK demonstrate relative stability. These findings challenge the conventional Kuznets hypothesis and demonstrate that contemporary inequality patterns are influenced by factors beyond economic development, including technological change, globalization, and policy choices. This research contributes to the literature by providing empirical evidence that the Kuznets curve has limited predictive power in modern economies, informing policymakers about the need for targeted interventions to address persistent inequality rather than relying on economic growth alone. Full article
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Other

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9 pages, 1210 KB  
Data Descriptor
Preferred Colleague Dataset: A Human-Annotated Dataset of Perceived Colleague Preference
by Deepu Krishnareddy, Bakir Hadžić, Hamid Gazerpour, Michael Danner, Zhuoqi Zeng and Matthias Rätsch
Data 2026, 11(5), 100; https://doi.org/10.3390/data11050100 - 1 May 2026
Viewed by 167
Abstract
Recruitment is a time-consuming process, and AI systems are increasingly being used to support the decision-making process. However, machine learning models used in such systems can inherit bias if the underlying training data reflects biased human preferences. It is essential to analyze and [...] Read more.
Recruitment is a time-consuming process, and AI systems are increasingly being used to support the decision-making process. However, machine learning models used in such systems can inherit bias if the underlying training data reflects biased human preferences. It is essential to analyze and quantify these biases in order to develop fairer AI systems. To address this issue, we collected human judgments of colleague preference for 2200 face images. The face image set includes images of different ethnicities and genders, as well as both real and synthetically generated faces. The images were annotated by humans from diverse backgrounds in terms of age, gender, and ethnicity. Annotators were shown series of pairs of face images and asked to select which individual they would prefer as a colleague. We gathered responses from 451 annotators and aggregated the annotations to compute a preference score for each image. This dataset provides a basis for understanding human bias in colleague preference and can support the development of fair and unbiased AI models for use in recruitment settings. Full article
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22 pages, 777 KB  
Data Descriptor
Dataset on AI- and VR-Supported Communication and Problem-Solving Performance in Undergraduate Courses: A Clustered Quasi-Experiment in Mexico
by Roberto Gómez Tobías
Data 2026, 11(1), 6; https://doi.org/10.3390/data11010006 - 2 Jan 2026
Viewed by 677
Abstract
Behavioral and educational researchers increasingly rely on rich datasets that capture how students respond to technology-enhanced instruction, yet few open resources document the full pipeline from experimental design to data curation in authentic classroom settings. This data descriptor presents a clustered quasi-experimental dataset [...] Read more.
Behavioral and educational researchers increasingly rely on rich datasets that capture how students respond to technology-enhanced instruction, yet few open resources document the full pipeline from experimental design to data curation in authentic classroom settings. This data descriptor presents a clustered quasi-experimental dataset on the impact of an instructional architecture that combines virtual reality (VR) simulations with artificial intelligence (AI)-driven formative feedback to enhance undergraduate students’ communication and problem-solving performance. The study was conducted at a large private university in Mexico during the 2024–2025 academic year and involved six intact classes (three intervention, three comparison; n = 180). Exposure to AI and VR was operationalized as a session-level “dose” (minutes of use, number of feedback events, number of scenarios, perceived presence), while performance was assessed with analytic rubrics (six criteria for communication and seven for problem solving) scored independently by two raters, with interrater reliability estimated via ICC (2, k). Additional Likert-type scales measured presence, perceived usefulness of feedback and self-efficacy. The curated dataset includes raw and cleaned tabular files, a detailed codebook, scoring guides and replication scripts for multilevel models and ancillary analyses. By releasing this dataset, we seek to enable reanalysis, methodological replication and cross-study comparisons in technology-enhanced education, and to provide an authentic resource for teaching statistics, econometrics and research methods in the behavioral sciences. Full article
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16 pages, 446 KB  
Data Descriptor
Open Dataset on Neurocognitive Complaints and Physical Symptoms in Long COVID: A Six-Month Post-Infection Cohort
by Somayeh Pour Mohammadi, Francisco Mercado Romero, Moein Noroozi Fashkhami and Irene Peláez
Data 2025, 10(12), 198; https://doi.org/10.3390/data10120198 - 1 Dec 2025
Viewed by 1117
Abstract
Long COVID is frequently accompanied by enduring neurocognitive and physical symptoms that substantially affect quality of life. Cognitive complaints—including difficulties in memory, attention, and executive functioning—often co-occur with physical manifestations such as fatigue, dyspnea, and headache. Despite growing research, openly available datasets integrating [...] Read more.
Long COVID is frequently accompanied by enduring neurocognitive and physical symptoms that substantially affect quality of life. Cognitive complaints—including difficulties in memory, attention, and executive functioning—often co-occur with physical manifestations such as fatigue, dyspnea, and headache. Despite growing research, openly available datasets integrating demographic, cognitive, and physical symptom profiles assessed during chronic phases of Long COVID remain scarce. Here, we present two complementary self-report datasets collected ≥6 months after the most recent COVID-19 infection. The first dataset (“Neuro–Long COVID-212”) includes demographic information, binary neurocognitive symptom indicators, and a 14-item Post-COVID Cognitive Impairment Scale assessing memory and attention complaints. The second dataset (“Neuro–Long COVID–210”) provides a broad range of physical symptoms—operationally defined as somatic and neurological complaints (e.g., fatigue, pain, sleep disturbance, anosmia/ageusia)—recorded as binary indicators (present/absent). Data were collected online via the Porsline platform using individualized links, with remote researcher support to ensure accuracy. Quality assurance procedures included duplicate-response removal, consistency checks, and transparent handling of missing values. The datasets are released in Excel (.xlsx) format, fully de-identified and accompanied by a detailed data dictionary to facilitate reuse. These datasets enable reproducibility, secondary analyses, and meta-analyses on cognitive and physical outcomes in Long COVID, and may inform future cross-disciplinary rehabilitation research. Full article
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