Publication Date
| In 2026 | 0 |
| Since 2025 | 8 |
Descriptor
| Data Analysis | 8 |
| Research Problems | 8 |
| Research Methodology | 4 |
| Artificial Intelligence | 2 |
| Data Collection | 2 |
| Educational Research | 2 |
| Social Science Research | 2 |
| Attention Control | 1 |
| Attention Span | 1 |
| Biology | 1 |
| COVID-19 | 1 |
| More ▼ | |
Source
Author
| Andy Trinh | 1 |
| Anna Kowalkowski | 1 |
| António José Mendes | 1 |
| Blessed Mbogo | 1 |
| Brian Keller | 1 |
| Brock Couch | 1 |
| Cara Gormally | 1 |
| Carlos Landaverde | 1 |
| Carly A. Busch | 1 |
| Chan-Tong Lam | 1 |
| Chen Zhu | 1 |
| More ▼ | |
Publication Type
| Journal Articles | 7 |
| Reports - Research | 5 |
| Information Analyses | 3 |
| Reports - Descriptive | 2 |
| Speeches/Meeting Papers | 1 |
Education Level
| Higher Education | 1 |
| Postsecondary Education | 1 |
Audience
| Researchers | 1 |
Location
| China | 1 |
Laws, Policies, & Programs
Assessments and Surveys
What Works Clearinghouse Rating
Peer reviewed
Direct linkFrancis Huang; Brian Keller – Large-scale Assessments in Education, 2025
Missing data are common with large scale assessments (LSAs). A typical approach to handling missing data with LSAs is the use of listwise deletion, despite decades of research showing that approach can be a suboptimal strategy resulting in biased estimates. In order to help researchers account for missing data, we provide a tutorial using R and…
Descriptors: Research Problems, Data Analysis, Statistical Bias, International Assessment
Download full textWan-Chong Choi; Chan-Tong Lam; António José Mendes – International Educational Data Mining Society, 2025
Missing data presents a significant challenge in Educational Data Mining (EDM). Imputation techniques aim to reconstruct missing data while preserving critical information in datasets for more accurate analysis. Although imputation techniques have gained attention in various fields in recent years, their use for addressing missing data in…
Descriptors: Research Problems, Data Analysis, Research Methodology, Models
Stephanie Wermelinger; Marco Bleiker; Moritz M. Daum – Infant and Child Development, 2025
Children's fuzziness leads to increased variance in the data, data loss, and high dropout rates in developmental studies. This study investigated the importance of 20 factors on the person (child, caregiver, experimenter) and situation (task, method, time, and date) level for the data quality as indicated via the number of valid trials in 11…
Descriptors: Infants, Young Children, Research Problems, Factor Analysis
Tina Law; Elizabeth Roberto – Sociological Methods & Research, 2025
Although there is growing social science research examining how generative AI models can be effectively and systematically applied to text-based tasks, whether and how these models can be used to analyze images remain open questions. In this article, we introduce a framework for analyzing images with generative multimodal models, which consists of…
Descriptors: Artificial Intelligence, Visual Aids, Open Source Technology, Social Science Research
Elizabeth S. Peterson; Joseph A. Taylor – Educational Research and Reviews, 2025
The methodological controversy surrounding ordinal outcome data has posed a distinct challenge to the conceptualization, design, and conduct of research in the social and behavioral sciences for more than 75 years. Accordingly, this study sought to supply a comprehensive and multidisciplinary perspective of the debate and in so doing lay the…
Descriptors: Research Problems, Educational Research, Social Science Research, Research Methodology
Kaitlyn Coburn; Kris Troy; Carly A. Busch; Naomi Barber-Choi; Kevin M. Bonney; Brock Couch; Marcos E. García-Ojeda; Rachel Hutto; Lauryn Famble; Matt Flagg; Tracy Gladding; Anna Kowalkowski; Carlos Landaverde; Stanley M. Lo; Kimberly MacLeod; Blessed Mbogo; Taya Misheva; Andy Trinh; Rebecca Vides; Erik Wieboldt; Cara Gormally; Jeffrey Maloy – CBE - Life Sciences Education, 2025
Trans* and genderqueer student retention and liberation is integral for equity in undergraduate education. While STEM leadership calls for data-supported systemic change, the erasure and othering of trans* and genderqueer identities in STEM research perpetuates cisnormative narratives. We sought to characterize how sex and gender data are…
Descriptors: LGBTQ People, Transgender People, Disproportionate Representation, Educational Research
Philip E. Kearney; Niamh Curran; Frank J. Nugent – Journal of Motor Learning and Development, 2025
Manipulation checks are an essential component of quality experimental design in motor learning. Guided by the Preferred Reporting Items for Systematic Reviews and Meta-Analyses framework, this methodological systematic review examined the utilization of manipulation checks in focus of attention research. Seventy-eight protocols from four…
Descriptors: Attention Control, Attention Span, Motor Development, Psychomotor Skills
Jiang Li; Chen Zhu; Mark Goh – Research Evaluation, 2025
Data Envelopment Analysis (DEA) is a widely adopted non-parametric technique for evaluating R&D performance. However, traditional DEA models often struggle to provide reliable solutions in the presence of data uncertainty. To address this limitation, this study develops a novel robust super-efficiency DEA approach to evaluate R&D…
Descriptors: Foreign Countries, Research and Development, COVID-19, Pandemics
