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Peer reviewed
Download full textElizabeth 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
Direct linkKaitlyn 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
Hairui Yu; Suzanne E. Perumean-Chaney; Kathryn A. Kaiser – Journal of Statistics and Data Science Education, 2024
Missing data can significantly influence results of epidemiological studies. The National Health and Nutrition Examination Survey (NHANES) is a popular epidemiological dataset. We examined recent practices related to the prevalence and the reporting of the amount of missing data, the underlying mechanisms, and the methods used for handling missing…
Descriptors: Statistics Education, Data Science, Data Use, Research Problems
Yan Xia; Selim Havan – Educational and Psychological Measurement, 2024
Although parallel analysis has been found to be an accurate method for determining the number of factors in many conditions with complete data, its application under missing data is limited. The existing literature recommends that, after using an appropriate multiple imputation method, researchers either apply parallel analysis to every imputed…
Descriptors: Data Interpretation, Factor Analysis, Statistical Inference, Research Problems
Nica Basuel; Rohan Carter-Rau; Molly Curtiss Wyss; Maya Elliott; Brad Olsen; Tracy Olson; Mónica Rodríguez – Center for Universal Education at The Brookings Institution, 2024
To support and better understand how to scale effectively, in 2020, the Millions Learning project at the Center for Universal Education (CUE) at Brookings joined the Global Partnership for Education's (GPE) Knowledge and Innovation Exchange (KIX), a joint partnership between GPE and the International Development Research Centre (IDRC), to…
Descriptors: Scaling, Research, Educational Innovation, Educational Change
Navé Wald; Tony Harland; Chandima Daskon – European Journal of Higher Education, 2024
This paper examines how higher education researchers approach writing the rationale and justification for their work published in journal articles. A common way for establishing this justification is through claiming a gap, but the problem is that it is often hard to find a research gap, and if it is included, there is too often no explanation for…
Descriptors: Higher Education, Educational Research, Educational Researchers, Research Problems
Ibrahima Dina Diatta; André Berchtold – International Journal of Social Research Methodology, 2023
Using secondary data has many advantages, but there are also many limitations, including the lack of relevant information. This article draws on a previous study that used secondary data to investigate substance use in young, elite athletes. Three types of missing data appeared: missing data, lack of information about the data collection process,…
Descriptors: Data Analysis, Research Problems, Data Collection, Scientific Research
Francis 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
Wan-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
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
Smith, Elizabeth E. – International Journal of Research & Method in Education, 2022
The purpose of this paper is to analyze the use of the exemplar methodology (ExM) as a method for selecting exemplars in education research. ExM is a systematic approach to selecting outliers that can be used to education researchers who investigate outliers to better understand phenomena among students, teachers, schools, and communities. While…
Descriptors: Research Methodology, Educational Research, Research Problems, Evaluation Criteria
Susan Athey; Raj Chetty; Guido Imbens – National Bureau of Economic Research, 2025
Researchers increasingly have access to two types of data: (i) large observational datasets where treatment (e.g., class size) is not randomized but several primary outcomes (e.g., graduation rates) and secondary outcomes (e.g., test scores) are observed and (ii) experimental data in which treatment is randomized but only secondary outcomes are…
Descriptors: Observation, Research Problems, Bias, Data Science
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
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
Jon D. Miller; Belén Laspra; Carmelo Polino; Glenn Branch; Robert T. Pennock; Mark S. Ackerman – Sage Research Methods Cases, 2025
This case study focuses on a multidecade time-series study of changes in public acceptance of evolution in the United States. Change over time is often a central issue in social science research. There are two kinds of change over time. Time-series studies address change in populations or groups over time. Longitudinal studies address changes in…
Descriptors: Evolution, Public Opinion, Case Studies, Financial Support
