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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
Maxi Schulz; Malte Kramer; Oliver Kuss; Tim Mathes – Research Synthesis Methods, 2024
In sparse data meta-analyses (with few trials or zero events), conventional methods may distort results. Although better-performing one-stage methods have become available in recent years, their implementation remains limited in practice. This study examines the impact of using conventional methods compared to one-stage models by re-analysing…
Descriptors: Meta Analysis, Data Analysis, Research Methodology, Research Problems
David Bruns-Smith; Oliver Dukes; Avi Feller; Elizabeth L. Ogburn – Grantee Submission, 2024
We provide a novel characterization of augmented balancing weights, also known as automatic debiased machine learning (AutoDML). These popular "doubly robust" or "de-biased machine learning estimators" combine outcome modeling with balancing weights -- weights that achieve covariate balance directly in lieu of estimating and…
Descriptors: Regression (Statistics), Weighted Scores, Data Analysis, Robustness (Statistics)
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
Yi, Zhihui; Schreiber, James B.; Paliliunas, Dana; Barron, Becky F.; Dixon, Mark R. – Journal of Behavioral Education, 2021
The recent commentary by Beaujean and Farmer (2020) on the original paper by Dixon et al. (2019) serves a cautionary tale of selective p-values, the law of small N sizes, and the type-II error. We believe these authors have crafted a somewhat questionable argument in which only 57% of the original Dixon et al. data were re-analyzed, based on a…
Descriptors: Research Problems, Data Analysis, Statistical Analysis, Probability
Seo, Michael; Furukawa, Toshi A.; Karyotaki, Eirini; Efthimiou, Orestis – Research Synthesis Methods, 2023
Clinical prediction models are widely used in modern clinical practice. Such models are often developed using individual patient data (IPD) from a single study, but often there are IPD available from multiple studies. This allows using meta-analytical methods for developing prediction models, increasing power and precision. Different studies,…
Descriptors: Prediction, Models, Patients, Data Analysis
S. Mabungane; S. Ramroop; H. Mwambi – African Journal of Research in Mathematics, Science and Technology Education, 2023
The issue of missing data raises concerns in all statistical and educational research. In this study, we focus on missing data in school-based assessment data generated by progressed high school learners (those who did not meet the promotional requirements for their current grades but were allowed to move to the next grade because of policy…
Descriptors: Data Analysis, Research Problems, High School Students, Student Promotion
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
Download full textZiqian Xu – Grantee Submission, 2022
With the prevalence of missing data in social science research, it is necessary to use methods for handling missing data. One framework in which data with missing values can still be used for parameter estimation is the Bayesian framework. In this tutorial, different missing data mechanisms including Missing Completely at Random, Missing at…
Descriptors: Research Problems, Bayesian Statistics, Structural Equation Models, Data Analysis
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
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
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
Prathiba Natesan Batley; Erica B. McClure; Brandy Brewer; Ateka A. Contractor; Nicholas John Batley; Larry Vernon Hedges; Stephanie Chin – Grantee Submission, 2023
N-of-1 trials, a special case of Single Case Experimental Designs (SCEDs), are prominent in clinical medical research and specifically psychiatry due to the growing significance of precision/personalized medicine. It is imperative that these clinical trials be conducted, and their data analyzed, using the highest standards to guard against threats…
Descriptors: Medical Research, Research Design, Data Analysis, Effect Size
Demarest, Leila; Langer, Arnim – Sociological Methods & Research, 2022
While conflict event data sets are increasingly used in contemporary conflict research, important concerns persist regarding the quality of the collected data. Such concerns are not necessarily new. Yet, because the methodological debate and evidence on potential errors remains scattered across different subdisciplines of social sciences, there is…
Descriptors: Guidelines, Research Methodology, Conflict, Social Science Research
Grund, Simon; Lüdtke, Oliver; Robitzsch, Alexander – Journal of Educational and Behavioral Statistics, 2023
Multiple imputation (MI) is a popular method for handling missing data. In education research, it can be challenging to use MI because the data often have a clustered structure that need to be accommodated during MI. Although much research has considered applications of MI in hierarchical data, little is known about its use in cross-classified…
Descriptors: Educational Research, Data Analysis, Error of Measurement, Computation
